PyTorch.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Tensor Basics:"
      ],
      "metadata": {
        "id": "gByQoi1y41rq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7nPpO-lEd4CL"
      },
      "outputs": [],
      "source": [
        "import torch"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Variable Declarations & Basic Operations:"
      ],
      "metadata": {
        "id": "2OyNkO6T7rtZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###torch.empty() creates tensor with any data type you want, torch.Tensor() only creates tensors of type torch.FloatTensor. So torch.Tensor() is a special case of torch.empty()"
      ],
      "metadata": {
        "id": "BJwSOg6b6at0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.empty(1) # scalar\n",
        "print(x)\n",
        "x = torch.empty(3) # array 1D\n",
        "print(x)\n",
        "x = torch.empty(2,3) # array 2D\n",
        "print(x)\n",
        "x = torch.empty(2,3) # array 3D\n",
        "print(x)\n",
        "print(x.shape)\n",
        "x = torch.rand(2,2)\n",
        "print(x)\n",
        "x = torch.zeros(2,3)\n",
        "print(x)\n",
        "x = torch.ones(3,3)\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2WyKgY641gQ",
        "outputId": "6ee311ed-150e-4edc-d42c-5e447a597015"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([2.2981e-34])\n",
            "tensor([-1.3774e+37,  4.5615e-41,  2.3039e-34])\n",
            "tensor([[-1.3774e+37,  4.5615e-41,  2.3039e-34],\n",
            "        [ 0.0000e+00,  6.2628e+00,  4.5615e-41]])\n",
            "tensor([[1.8528e-34, 0.0000e+00, 2.2980e-34],\n",
            "        [0.0000e+00, 4.4842e-44, 0.0000e+00]])\n",
            "torch.Size([2, 3])\n",
            "tensor([[0.5977, 0.8313],\n",
            "        [0.9351, 0.0788]])\n",
            "tensor([[0., 0., 0.],\n",
            "        [0., 0., 0.]])\n",
            "tensor([[1., 1., 1.],\n",
            "        [1., 1., 1.],\n",
            "        [1., 1., 1.]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.ones(3,3)\n",
        "print(x.dtype)\n",
        "x = torch.ones(3,3,dtype = torch.int)\n",
        "print(x.dtype)\n",
        "print(x.size())\n",
        "print(x.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BqFIqgGX5Sba",
        "outputId": "63c3eb41-a65e-4b2b-d3b6-415f4956019d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.float32\n",
            "torch.int32\n",
            "torch.Size([3, 3])\n",
            "torch.Size([3, 3])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.tensor([2,3])\n",
        "print(x.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GOegrS4H5os_",
        "outputId": "ee1ad35d-a762-4fa5-ba67-2b2322a4bf43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([2])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Any function that has a trailing underscore \"add_\" will do its function in-place"
      ],
      "metadata": {
        "id": "QiEI0_WE7MkE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.rand(2,2)\n",
        "y = torch.rand(2,2)\n",
        "z = x + y # element-wise addition\n",
        "print(z)\n",
        "z = torch.add(x,y) # element-wise addition\n",
        "print(z)\n",
        "y.add_(x) # in-place addition\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rWU9ETS36nFU",
        "outputId": "e1ceb7fb-40ef-4ed7-f136-13299e402933"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[1.2117, 0.9824],\n",
            "        [1.2194, 0.5482]])\n",
            "tensor([[1.2117, 0.9824],\n",
            "        [1.2194, 0.5482]])\n",
            "tensor([[1.2117, 0.9824],\n",
            "        [1.2194, 0.5482]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Slicing:"
      ],
      "metadata": {
        "id": "vXqKrjUG7n9X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###If we have one element we can get its item using .item() function"
      ],
      "metadata": {
        "id": "IEqd1c2w8Pn2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.rand(5,3)\n",
        "print(x[:,0]) # first column and all the rows\n",
        "print(x[0,:]) # first row and all columns\n",
        "print(x[1,2]) # one element\n",
        "print(x[1,2].item())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_8O7s0bH7Djq",
        "outputId": "6ecd90ba-862c-4f6a-823f-a0980527720f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([0.4060, 0.0146, 0.1298, 0.5327, 0.0468])\n",
            "tensor([0.4060, 0.0432, 0.7630])\n",
            "tensor(0.6038)\n",
            "0.6038056015968323\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Reshaping:"
      ],
      "metadata": {
        "id": "ljVwefDA8c9F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.rand(4,4)\n",
        "print(x)\n",
        "y = x.view(16) # 1D vector, no. of elements must be the same\n",
        "print(y)\n",
        "y = x.view(-1,8) # This will be 2X8\n",
        "print(y)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GizDT_Fd8YYG",
        "outputId": "10f6343b-7d45-45e6-f4de-8eee9580eb9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.5364, 0.7047, 0.0173, 0.8284],\n",
            "        [0.2784, 0.7399, 0.7040, 0.2059],\n",
            "        [0.0178, 0.7920, 0.0271, 0.7192],\n",
            "        [0.5300, 0.8264, 0.3430, 0.2937]])\n",
            "tensor([0.5364, 0.7047, 0.0173, 0.8284, 0.2784, 0.7399, 0.7040, 0.2059, 0.0178,\n",
            "        0.7920, 0.0271, 0.7192, 0.5300, 0.8264, 0.3430, 0.2937])\n",
            "tensor([[0.5364, 0.7047, 0.0173, 0.8284, 0.2784, 0.7399, 0.7040, 0.2059],\n",
            "        [0.0178, 0.7920, 0.0271, 0.7192, 0.5300, 0.8264, 0.3430, 0.2937]])\n",
            "torch.Size([2, 8])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##converting from numpy to tensor and vice versa:"
      ],
      "metadata": {
        "id": "xmWWLN468_i6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "58BQFibR8loI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###If the tensor is on the CPU not the GPU then both tensor and numpy array will share the same memory location. Meaning that if we change one, it will affect the other."
      ],
      "metadata": {
        "id": "fAAnRV_T9br8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = torch.ones(5)\n",
        "print(a)\n",
        "b = a.numpy()\n",
        "print(b)\n",
        "print(type(b))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PgWPHb569F6T",
        "outputId": "fbd7e7ae-3892-4a71-a66f-805cfd34f66b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([1., 1., 1., 1., 1.])\n",
            "[1. 1. 1. 1. 1.]\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.add_(1)\n",
        "print(a)\n",
        "print(b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKUgPJZ19NOK",
        "outputId": "971cd1de-ffff-41a5-dc32-1fbbc90db32d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([2., 2., 2., 2., 2.])\n",
            "[2. 2. 2. 2. 2.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.ones(5)\n",
        "print(a)\n",
        "b = torch.from_numpy(a)\n",
        "print(b)\n",
        "a+=1\n",
        "print(a)\n",
        "print(b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tJAyFBFA9txU",
        "outputId": "a6a063b6-140a-4800-d340-a6328eb92923"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1. 1. 1. 1. 1.]\n",
            "tensor([1., 1., 1., 1., 1.], dtype=torch.float64)\n",
            "[2. 2. 2. 2. 2.]\n",
            "tensor([2., 2., 2., 2., 2.], dtype=torch.float64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Checking for GPU & Moving Tensors to GPU:"
      ],
      "metadata": {
        "id": "PKGs_loU-heS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if(torch.cuda.is_available()):\n",
        "  device = torch.device(\"cuda\")\n",
        "  x = torch.ones(5, device = device)\n",
        "  y = torch.ones(5)\n",
        "  y = y.to(device)\n",
        "  z = x + y # This will be performed on the GPU\n",
        "  #z.numpy() will give an error; as numpy is only available on cpu\n",
        "  z = z.to(\"cpu\")\n",
        "  z.numpy"
      ],
      "metadata": {
        "id": "hswHMpMn93UN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Calculating Gradience:"
      ],
      "metadata": {
        "id": "zhvTUHzz_nBh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.rand(5, requires_grad = True) # This will tell PyTorch that it will need to calculate the gradience for this tensor later in the optimization step\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uP6lYB4e-qYR",
        "outputId": "0aeec663-1821-43b6-cad1-482f6e09e1ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([0.7941, 0.0098, 0.2807, 0.6435, 0.2227], requires_grad=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Gradient Calculations:"
      ],
      "metadata": {
        "id": "wTl7xSq3AGOA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1- Forward Pass: Compute the loss\n",
        "### 2- Compute Local Gradients\n",
        "### 3- Backward Pass: Compute dloss/dweights using the chain rule"
      ],
      "metadata": {
        "id": "gIPnxHQuSjCu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###For each operation done to x, PyTorch will create a computation graph (y = x + 2, the inputs are x and 2 and + is in a node and the output is y. Using this graph with backpropagation, PyTorch can calculate the gradients."
      ],
      "metadata": {
        "id": "jeIKkLKuD0xY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# rand is a uniform sitribution [0,1) but randn is a normal distribution (-1,1)\n",
        "x = torch.randn(3, requires_grad = True)\n",
        "y = x + 2\n",
        "print(x)\n",
        "print(y)\n",
        "z = y * y * 2\n",
        "z = z.mean()\n",
        "print(z)\n",
        "z.backward() # Calculates the gradient of z with respect to x dz/dx using chain rule and this works for SCALARS since z is a scalar\n",
        "print(x.grad)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NyozX-WK_9A7",
        "outputId": "feff0de5-c9a7-40dd-a9eb-f90a09f4f260"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([ 3.6317, -0.7236,  0.6677], requires_grad=True)\n",
            "tensor([5.6317, 1.2764, 2.6677], grad_fn=<AddBackward0>)\n",
            "tensor(26.9743, grad_fn=<MeanBackward0>)\n",
            "tensor([7.5089, 1.7018, 3.5570])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.randn(3, requires_grad = True)\n",
        "y = x + 2\n",
        "print(x)\n",
        "print(y)\n",
        "z = y * y * 2\n",
        "print(z)\n",
        "v = torch.tensor([0.1,1.0,0.001],dtype = torch.float32)\n",
        "z.backward(v) # Calculates the gradient of z with respect to x dz/dx using chain rule passing a vector with the same size as z will make it work\n",
        "print(x.grad)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j6GDv62eEY_a",
        "outputId": "e331da23-da49-45d3-cece-d0f72028108b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([-0.8228,  0.3881, -0.1317], requires_grad=True)\n",
            "tensor([1.1772, 2.3881, 1.8683], grad_fn=<AddBackward0>)\n",
            "tensor([ 2.7715, 11.4062,  6.9807], grad_fn=<MulBackward0>)\n",
            "tensor([4.7087e-01, 9.5525e+00, 7.4730e-03])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Prevent Tracking Gradients:"
      ],
      "metadata": {
        "id": "mYhUtVukGjiI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### We usually do this to variables that we don't want to be part of our computational graph"
      ],
      "metadata": {
        "id": "zKKDymzobvmC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# There are several ways\n",
        "# 1- x.requires_grad_(False)\n",
        "# 2- x.detach() This creates a new tensor that doesn't require the gradient\n",
        "# 3- with torch.no_grad(): then do the operations inside this with\n",
        "x.requires_grad_(False)\n",
        "print(x)\n",
        "\n",
        "x.requires_grad_(True)\n",
        "print(x)\n",
        "y = x.detach()\n",
        "print(y)\n",
        "\n",
        "with torch.no_grad():\n",
        "  y = x + 2\n",
        "  print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5T_sDsO4GFC9",
        "outputId": "fb0b758c-7689-4793-c87c-47514b08c8a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([-0.8228,  0.3881, -0.1317])\n",
            "tensor([-0.8228,  0.3881, -0.1317], requires_grad=True)\n",
            "tensor([-0.8228,  0.3881, -0.1317])\n",
            "tensor([1.1772, 2.3881, 1.8683])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Training Example:"
      ],
      "metadata": {
        "id": "fh2C2KCPH4Ry"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gradients are accumulated by default. Therefore, we must empty the gradients before the next loop."
      ],
      "metadata": {
        "id": "CmJdtMcwIhKI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weights = torch.ones(4, requires_grad = True)\n",
        "for epoch in range(2):\n",
        "  model_output = (weights * 3).sum()\n",
        "  model_output.backward()\n",
        "  print(weights.grad) # For one iteration, it will output tensor([3., 3., 3., 3.]). For twp iterations, it will output tensor([6., 6., 6., 6.])\n",
        "  weights.grad.zero_()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMNjT7nRHXVW",
        "outputId": "226bb424-4449-4561-c479-fffaaa15d1be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([3., 3., 3., 3.])\n",
            "tensor([3., 3., 3., 3.])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Training & Prediction:"
      ],
      "metadata": {
        "id": "fKffsvq1JjlP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Steps:\n",
        "1.   Design Model(input size, output size, forward) forward is the layers\n",
        "2.   Construct Loss & Optimizer\n",
        "3.   Training Loop\n",
        "    *   Forward Pass: Compute prediction\n",
        "    *   Backward Pass: Gradients\n",
        "    *   Update Weights\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "      \n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "sSzZx7mydJpe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prediction: Manually\n",
        "### Gradient Computation: Autograd\n",
        "### Loss Computation: Manually\n",
        "### Parameter Updates: Manually"
      ],
      "metadata": {
        "id": "GLyqZ3-PcdXN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = torch.tensor([1,2,3,4], dtype = torch.float32)\n",
        "Y = torch.tensor([2,4,6,8], dtype = torch.float32)\n",
        "w = torch.tensor(0.0, dtype = torch.float32, requires_grad = True)\n",
        "\n",
        "# Forward pass \n",
        "def forward(x):\n",
        "  return w * x\n",
        "\n",
        "# Loss\n",
        "def loss(y, y_predicted):\n",
        "  return ((y_predicted - y) ** 2).mean()\n",
        "\n",
        "print(\"Prediction before training: \" + str(forward(5)) + \"\")\n",
        "\n",
        "# Training\n",
        "lr = 0.01\n",
        "n_iters = 100\n",
        "\n",
        "for epoch in range(n_iters):\n",
        "  # Prediction = forward pass\n",
        "  y_pred = forward(X)\n",
        "\n",
        "  # Loss\n",
        "  l = loss(Y, y_pred)\n",
        "\n",
        "  # Gradients = backward pass\n",
        "  l.backward() # dloss/dw\n",
        "\n",
        "  # Update weights\n",
        "  with torch.no_grad():\n",
        "    w-= lr * w.grad\n",
        "\n",
        "  # Zero gradients\n",
        "  w.grad.zero_()\n",
        "\n",
        "  if(epoch % 10 == 0):\n",
        "    print(\"Epoch \" + str(epoch + 1) + \" weight = \" + str(w.item()) + \" loss = \" +str(l.item()) +\"\")\n",
        "\n",
        "print(\"Prediction after training: \" + str(forward(5).item()) + \"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g4WApzPwH943",
        "outputId": "09e79085-bc08-4b5b-e625-a101bb2656cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction before training: tensor(0., grad_fn=<MulBackward0>)\n",
            "Epoch 1 weight = 0.29999998211860657 loss = 30.0\n",
            "Epoch 11 weight = 1.6653136014938354 loss = 1.1627856492996216\n",
            "Epoch 21 weight = 1.934108853340149 loss = 0.0450688973069191\n",
            "Epoch 31 weight = 1.987027645111084 loss = 0.0017468547448515892\n",
            "Epoch 41 weight = 1.9974461793899536 loss = 6.770494655938819e-05\n",
            "Epoch 51 weight = 1.9994971752166748 loss = 2.6243997126584873e-06\n",
            "Epoch 61 weight = 1.9999010562896729 loss = 1.0175587306093803e-07\n",
            "Epoch 71 weight = 1.9999804496765137 loss = 3.9741685498029256e-09\n",
            "Epoch 81 weight = 1.999996304512024 loss = 1.4670220593870908e-10\n",
            "Epoch 91 weight = 1.9999992847442627 loss = 5.076827847005916e-12\n",
            "Prediction after training: 9.999998092651367\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prediction: Manually\n",
        "### Gradient Computation: Autograd\n",
        "### Loss Computation: PyTorch Loss\n",
        "### Parameter Updates: PyTorch Optimizer"
      ],
      "metadata": {
        "id": "DKisEQgAct1O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn"
      ],
      "metadata": {
        "id": "u69Jm8bWeaK2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = torch.tensor([1,2,3,4], dtype = torch.float32)\n",
        "Y = torch.tensor([2,4,6,8], dtype = torch.float32)\n",
        "w = torch.tensor(0.0, dtype = torch.float32, requires_grad = True)\n",
        "\n",
        "# Forward pass \n",
        "def forward(x):\n",
        "  return w * x\n",
        "\n",
        "print(\"Prediction before training: \" + str(forward(5)) + \"\")\n",
        "\n",
        "# Training\n",
        "lr = 0.01\n",
        "n_iters = 100\n",
        "loss = nn.MSELoss() # loss is a callable function\n",
        "optimizer = torch.optim.SGD([w], lr = lr)\n",
        "\n",
        "for epoch in range(n_iters):\n",
        "  # Prediction = forward pass\n",
        "  y_pred = forward(X)\n",
        "\n",
        "  # Loss\n",
        "  l = loss(Y, y_pred)\n",
        "\n",
        "  # Gradients = backward pass\n",
        "  l.backward() # dloss/dw\n",
        "\n",
        "  # Update weights\n",
        "  optimizer.step()\n",
        "\n",
        "  # Zero gradients\n",
        "  optimizer.zero_grad()\n",
        "\n",
        "  if(epoch % 10 == 0):\n",
        "    print(\"Epoch \" + str(epoch + 1) + \" weight = \" + str(w.item()) + \" loss = \" +str(l.item()) +\"\")\n",
        "\n",
        "print(\"Prediction after training: \" + str(forward(5).item()) + \"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "shx6tbP9JM4G",
        "outputId": "62866c4f-2ae2-4696-b191-4f2fdd9c6dfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction before training: tensor(0., grad_fn=<MulBackward0>)\n",
            "Epoch 1 weight = 0.29999998211860657 loss = 30.0\n",
            "Epoch 11 weight = 1.6653136014938354 loss = 1.1627856492996216\n",
            "Epoch 21 weight = 1.934108853340149 loss = 0.0450688973069191\n",
            "Epoch 31 weight = 1.987027645111084 loss = 0.0017468547448515892\n",
            "Epoch 41 weight = 1.9974461793899536 loss = 6.770494655938819e-05\n",
            "Epoch 51 weight = 1.9994971752166748 loss = 2.6243997126584873e-06\n",
            "Epoch 61 weight = 1.9999010562896729 loss = 1.0175587306093803e-07\n",
            "Epoch 71 weight = 1.9999804496765137 loss = 3.9741685498029256e-09\n",
            "Epoch 81 weight = 1.999996304512024 loss = 1.4670220593870908e-10\n",
            "Epoch 91 weight = 1.9999992847442627 loss = 5.076827847005916e-12\n",
            "Prediction after training: 9.999998092651367\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prediction: PyTorch Model\n",
        "### Gradient Computation: Autograd\n",
        "### Loss Computation: PyTorch Loss\n",
        "### Parameter Updates: PyTorch Optimizer"
      ],
      "metadata": {
        "id": "3P6Mr9Nrc4kq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### We usually need to design our model. But, since linear regression is alredy implemented in PyTorch, we simply call nn.Linear. Otherwise, we would need to create a model class"
      ],
      "metadata": {
        "id": "YDZw84YFfjRM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = torch.tensor([[1],[2],[3],[4]], dtype = torch.float32)\n",
        "Y = torch.tensor([[2],[4],[6],[8]], dtype = torch.float32)\n",
        "X_test = torch.tensor([5], dtype = torch.float32)\n",
        "\n",
        "\n",
        "n_samples, n_features = X.shape\n",
        "print(n_samples, n_features)\n",
        "\n",
        "\n",
        "input_size = n_features\n",
        "output_size = n_features\n",
        "\n",
        "\n",
        "# model = nn.Linear(input_size, output_size) we could do this since it is only one layer and already provided by PyTorch\n",
        "\n",
        "\n",
        "# This is the general way\n",
        "class LinearRegression(nn.Module):\n",
        "  def __init__(self, input_dim, output_dim):\n",
        "    super(LinearRegression, self).__init__()\n",
        "\n",
        "    # Define Layers\n",
        "    self.lin = nn.Linear(input_dim, output_dim)\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.lin(x)\n",
        "\n",
        "model = LinearRegression(input_size, output_size)\n",
        "print(\"Prediction before training: \" + str(model(X_test).item()) + \"\")\n",
        "\n",
        "# Training\n",
        "lr = 0.01\n",
        "n_iters = 100\n",
        "loss = nn.MSELoss() # loss is a callable function\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr = lr)\n",
        "\n",
        "for epoch in range(n_iters):\n",
        "  # Prediction = forward pass\n",
        "  y_pred = model(X)\n",
        "\n",
        "  # Loss\n",
        "  l = loss(Y, y_pred)\n",
        "\n",
        "  # Gradients = backward pass\n",
        "  l.backward() # dloss/dw\n",
        "\n",
        "  # Update weights\n",
        "  optimizer.step()\n",
        "\n",
        "  # Zero gradients\n",
        "  optimizer.zero_grad()\n",
        "\n",
        "  if(epoch % 10 == 0):\n",
        "    [w, b] = model.parameters()\n",
        "    print(\"Epoch \" + str(epoch + 1) + \" weight = \" + str(w[0][0].item()) + \" loss = \" +str(l.item()) +\"\")\n",
        "\n",
        "print(\"Prediction after training: \" + str(model(X_test).item()) + \"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DnUqsKdBc51B",
        "outputId": "11750605-9cb4-4cc4-83d8-b745b08436da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4 1\n",
            "Prediction before training: 3.8210551738739014\n",
            "Epoch 1 weight = 0.7693529725074768 loss = 9.630396842956543\n",
            "Epoch 11 weight = 1.483320713043213 loss = 0.4553367793560028\n",
            "Epoch 21 weight = 1.6075764894485474 loss = 0.2059555947780609\n",
            "Epoch 31 weight = 1.636699914932251 loss = 0.18820145726203918\n",
            "Epoch 41 weight = 1.6502512693405151 loss = 0.17709803581237793\n",
            "Epoch 51 weight = 1.6610361337661743 loss = 0.1667861044406891\n",
            "Epoch 61 weight = 1.671121597290039 loss = 0.1570783108472824\n",
            "Epoch 71 weight = 1.6808480024337769 loss = 0.14793552458286285\n",
            "Epoch 81 weight = 1.690277338027954 loss = 0.13932493329048157\n",
            "Epoch 91 weight = 1.6994264125823975 loss = 0.1312154084444046\n",
            "Prediction after training: 9.397339820861816\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Linear Regression:\n",
        "\n",
        "F = wx + b"
      ],
      "metadata": {
        "id": "-WuYvuyxQ4sq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "from sklearn import datasets\n",
        "import matplotlib.pyplot as plt "
      ],
      "metadata": {
        "id": "d55thegHf-5t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1- Generate Dataset:"
      ],
      "metadata": {
        "id": "55vaAVYJRdIF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_numpy, Y_numpy = datasets.make_regression(n_samples = 100, n_features = 1, noise = 20, random_state = 1)"
      ],
      "metadata": {
        "id": "BfZu0e0LRWO0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = torch.from_numpy(X_numpy.astype(np.float32))\n",
        "Y = torch.from_numpy(Y_numpy.astype(np.float32))\n",
        "print(X.shape)\n",
        "print(Y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6GyYYwjRusT",
        "outputId": "46c8e28e-6207-4eb4-bdc1-81793368254e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([100, 1])\n",
            "torch.Size([100])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = Y.view(Y.shape[0],1) # Transform Y from row vector to column vector\n",
        "print(X.shape)\n",
        "print(Y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZ94zjK4SNOb",
        "outputId": "bf1b2e73-bc1b-490e-c9e0-4b4e157dfe26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([100, 1])\n",
            "torch.Size([100, 1])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##2- Model:"
      ],
      "metadata": {
        "id": "DQJwcshhTGQ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_samples, n_features = X.shape\n",
        "input_size = n_features\n",
        "output_size = 1"
      ],
      "metadata": {
        "id": "tHxNIE2RSdy2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class LinearRegression(nn.Module):\n",
        "  def __init__(self, input_dim, output_dim):\n",
        "    super(LinearRegression, self).__init__()\n",
        "\n",
        "    # Define Layers\n",
        "    self.lin = nn.Linear(input_dim, output_dim)\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.lin(x)"
      ],
      "metadata": {
        "id": "PIGTrSoBSjMz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression(input_size, output_size)"
      ],
      "metadata": {
        "id": "LDpjDCojSz-s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xaOB52CybQ6D",
        "outputId": "976a7c2d-938a-480a-cd85-fc80f5966911"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearRegression(\n",
            "  (lin): Linear(in_features=1, out_features=1, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3- Loss & Optimizer:"
      ],
      "metadata": {
        "id": "c3wHRlxjTJFN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "learning_rate = 0.01\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)"
      ],
      "metadata": {
        "id": "3HAdqBQmS3N4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##4- Training:"
      ],
      "metadata": {
        "id": "00c9wPUlT31g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 100\n",
        "for epoch in range(num_epochs):\n",
        "\n",
        "  # Forward pass and loss\n",
        "  y_predicted = model(X)\n",
        "  loss = criterion(y_predicted, Y)\n",
        "\n",
        "  # Backward pass (calculating gradients)\n",
        "  loss.backward()\n",
        "\n",
        "  # Update\n",
        "  optimizer.step()\n",
        "\n",
        "  # Empty gradients\n",
        "  optimizer.zero_grad()\n",
        "\n",
        "  if((epoch+1) % 10 == 0):\n",
        "    print(\"Epoch \" + str(epoch + 1) + \" loss = \" +str(loss.item()) +\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQuv9yvpTnQ3",
        "outputId": "2204eb83-850a-4d19-d4cf-f6201de1efc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 10 loss = 4450.9658203125\n",
            "Epoch 20 loss = 3318.73193359375\n",
            "Epoch 30 loss = 2499.74365234375\n",
            "Epoch 40 loss = 1906.6920166015625\n",
            "Epoch 50 loss = 1476.815185546875\n",
            "Epoch 60 loss = 1164.925048828125\n",
            "Epoch 70 loss = 938.4434204101562\n",
            "Epoch 80 loss = 773.85107421875\n",
            "Epoch 90 loss = 654.1484985351562\n",
            "Epoch 100 loss = 567.034423828125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted = model(X).detach().numpy() # Detach this operation to prevent it from being used in our computation graph\n",
        "plt.plot(X_numpy, Y_numpy, 'ro')\n",
        "plt.plot(X_numpy, predicted, 'b')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "BW_6_s1CT-Px",
        "outputId": "f866a11e-3ae9-4097-a116-5b95dcc35865"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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1VNhNpNGK8t8jUzb9gb+zM3xaRyRGCv4iCSKDN9weC/rd3YXTNBs2aJpGGkLBX6RYpadoRSgb9IH84m3uE8LQkJsqiuOAd5EyFPxF/KLq7VQgNOjnsnfCFmPrfF2RaqUW/EkuJ7mL5DjJW9Lqh0iBGg8oCQ36nOUOUclV6wyrg5PEwSgxfIKR9pVK8CfZAeDbAC4CsBjAVSQXp9EXkQJV7pINDfpdJ7npHf8o/sYbw4Nx3Ltz9UlCykhr5L8UwLiZvWpmhwBsBLAipb5I1vlHyLNC/pco2rgVOb3T2xc8ir/jjvBgHPfuXB2xKGWkFfzPALDX9/M+r60AyUGSYyTHJicnG9Y5yZDiEbJ3WEoB3y7ZyKCfy+SMOoPXzx+M496dqzo/UkZTL/ia2bCZ9ZtZf09PT9rdkVZUbt47rAhbR0fBwixXDZQP+jnVjNZzwTju3bmq8yNlpBX89wOY7/t5ntcmEp9K5r3DRsJHjwJHj4ITu8FVAaWVe/tc9k6QoFF8o07XiuqD6vyIn5k1/AFXSvpVAAsBdAL4JYAzo35nyZIlJlKV3t7cwLzw0dtb9pqgX3P/t/h+6OoyGxkJfu2REXdv0n294QZ3fdjvj4xEP1+L4j7Ucy9pSQDGLCwOhz2R9APAxQB+BeAVAOvKXa/gL1UjgyM4mb9mZMSss7N80A97I8m9mVQSWKOCcSVvVCJVigr+Kuwm7auv71jp5ALFp2DNnQtOBRdSO/a/x6xZAZP7Pl1d9c3Rh92fdFNQIjVQYTfJpgrmvUkEBv5jZ+TmlJubrzeNUgu00mAK/tL8at2pmsug6e7Ot514IoAKa+/4A2/QG0mxetIotUArDabgL80tjp2qBw8e+5ZTbwZn7+R25OYUB15/KmaYekbpOohFGkzBX5pbJTtVoz4ZeL8feVyiITjwAoX3BdxawchIMqP0qNo/InELWwlutoeyfTKqXMZOmRTJ0OwdMjr7plzqpdIopQWgGVM9q30o+LehsADqb+/oiE6BrDVPnyxI8SwJ7t3d0a8r0gKigr+mfSQdYXP5N95YVa2d4kXWig5GB9y9Dx0qvCg3nTQ6CkxNBfc7bFFX5ZOlxSj4SzrC5vKHhyuqtXNsPtxbZA0N+iOjsM7jK+/XxASwZk3480GLuiqfLC1Im7wkHeU2TRUL2ewUVjLHRrzDU8I2ekW9TlS/RkZKF2Ir3Uwm0mDa5CXNJywtsqOjoutD8/RzBddyAbra3PuowN/dHZyBo/LJ0oIU/CUdYZuaBgcj0ygjN2d1neSu8wfouHbI5g5bD6LdudKCFPwlHWGbmtavD2wPrafvX8gNKrFQyc5cwF3j3wns19ERveFKu3OlFYWlATXbQ6meGVGU/hmZp1+uYmfIPW1kJLyt1rLKyvuXJoSIVM/j0n7zETkmlzXj7chFwBrqsSn5vgXBi6xBUy0DA4Wj9tFR9wlhzx53ffFU0Te+kU/19GoBlVX8GiJNTtM+0jzWrQOnD4Tn6ff25dMna51qqSQt01cLCFNTStuUtqRUT2kKoSmbKHqisxPYsMGNssuN4IOUS8tU2qa0kahUTwV/SVXFQd+vuxt4M/jwlbLKHZqiQ1WkjaSS50/yr0nuJ7nde1zse+5WkuMkd5FcllQfpHmFpmxyVnTgB8JLL1SiXFqm0jYlI5Ke8/97MzvLezwEACQXA1gJ4EwAywGsJxmys0faTWTQ7+0Dzj8//ONAHMqtFShtUzIijQXfFQA2mtkHZvZrAOMAlqbQD6lGnYXLQoN+7hCV3OLrT38KrF0bfWhKWD5+JcodmqJDVSQjkg7+N5N8nuQGkqd6bWcA2Ou7Zp/XVoLkIMkxkmOTk5MJd1VC1VG4LDTomyvFEFjc7aGH8oemzJ5d+stf+UpN/wyMjgJz5wKrVrl/w5w5wYvEOlRFMqCu4E9yK8kdAY8VAL4D4GMAzgLwGoDbqr2/mQ2bWb+Z9ff09NTTValHJadpFYkM+rn11HI1cQYGgOuuK73RPfdUn3o5Ogpcc03hesHUFHDttUrjlEyqK/ib2YVm9nsBjwfN7HUzO2JmRwHcifzUzn4A8323mee1SbOqonBZ2YJrfmGLqLNm5aeXNm0qzb4p88YTaN06YGamtP3QoervJdIGksz2Od3342UAdnjfbwGwkuTxJBcCWATgmaT6ITGoIAMmsuAa6KZZikfZYXV3jhzJTy9Ve6hKmKjrVX1TMijJOf+/JfkCyecBfB7AXwKAme0EsAnAiwAeAXCTmQUc1yRNIyIDJjTod88tTdk8dMiVTsgpXlwNK+ccpNrUy6jrlcYpGZRYbR8zWx3x3BAA5c61ityCp283LSd2A6tKLz02Q8OQEXtUjn7QkY1Bakm9HBpyc/7FUz+dnUrjlExSbR+pjJcBQzvqAn+RgoXcShVnEUXp7q4v9XJgALjrrsI00e7ufKkIkYxRVU+pSGgZhrCY3d0dPMr3B9+gLKIwJ59ce0mHHFXeFDlGI3+JVFHKZo5/IxiQ/+o3NZXfJFbNQqsWZUVipeAvgaoK+kDpFM7UFHDccfmRvv9muU1ic+ZU3iEtyorESsFfClQd9HOCpnAOHXLTNb29wbn6QGkWUWdn6a5e1dYRiZ2CvwAIDvqL8Kt8nv7cudE7YaM2goU999ZbpXV0NmxwC7OqrSOSKNXzz7igUf5H8QpewcdLn+jqCg/EUYegADogRSQFqdTzl+YWNNLv7XWllQMDPxBdViGqFLLKJIs0HQX/jAkK+vPmuSn53btRfmE1bAonqhSyyiSLNB1N+2RE0PTO6acDv/lNUWMuaycs/15TNSItQ9M+GRY00v/IR9xIvyTwA/lRetCBKSRw8cWl7SLSchT821RQ0O/udkH/9dfL/PLAgNtNe8MNhTcxq62Wvog0HQX/NhMU9D/8YRe3q66O8NBD8dTSF5Gmo9o+bSJoTv9DHwLefruOm1ZxiIuItBaN/FvckiWlgf+cc9yAva7AD1R0iIuItCYF/xa1dKkL+s89l29bscIF/X/5l5heZGjIlVvwU/17kbag4N9iPv1pF/SffTbfdsMNLug/8EACL1g8598iqcEiEq2u4E/ySpI7SR4l2V/03K0kx0nuIrnM177caxsneUs9r58ln/2sC/rP+E47XrvWxeL1630X+ssq50on1yro0POZGS34irSBehd8dwC4HMB3/Y0kFwNYCeBMAL8LYCvJT3hPfxvAFwDsA/AsyS1m9mKd/Whb55wD/Nu/FbZ9/esuFb9E8QatXOlkoLbdtFrwFWlbdY38zewlM9sV8NQKABvN7AMz+zWAcQBLvce4mb1qZocAbPSulSLnnutG+v7Af911bqQfGPiB4LLK9aRmasFXpG0lNed/BoC9vp/3eW1h7YFIDpIcIzk2OTmZSEebzR//sQv6P/lJvu2aa1zQv/POMr8c90hdBdlE2lbZ4E9yK8kdAY/ER+xmNmxm/WbW39PTk/TLper8813Qf/rpfNvVV7ugv2FDhTeJe6SugmwibavsnL+ZXVjDffcDmO/7eZ7Xhoj2TLrgAuDJJwvb1qwB7r67hpsNDZUWZat3pK5Dz0XaUlLTPlsArCR5PMmFABYBeAbAswAWkVxIshNuUXhLQn1oal/8ohtM+wP/6tVupF9T4Ac0UheRitWV7UPyMgD/B0APgB+T3G5my8xsJ8lNAF4EcBjATWZ2xPudmwE8CqADwAYz21nXv6DFLFsGPPZYYdvAADAyEtMLaKQuIhVQPf8Guegi4JFHCtuuugr43vfS6Y+ItL+oev4q7JawL33JFcf0W7kSuP/+dPojIgKovENiLrnETbv7A/+VV7o5/dgDf5y7ekUkEzTyj9mKFcCWoiXsL38Z+P73E3rBuHf1ikgmaOQfk0svdSN9f+C/7DI30k8s8APx7+oVkUxQ8K/TFVe4oP/gg/m2Sy91Qf9HP2pAB1R/R0RqoOBfoy9/2QV9f4D/kz9xQX/z5gZ2RPV3RKQGCv5V+upXXdD/4Q/zbZdc4oJ+8Vx/Q6j+jojUQMG/Qv/wDy7ob9qUb7voIhf0/+mfUuuWdvWKSE2U7VPG5s3A5ZcXti1fDjz8cDr9CaRdvSJSJY38Q/z8524g7Q/8f/M3bqTfVIFfRKQGGvkXeeYZd06u3wMPuPx9EZF2oeDvCQr6Tz0FnHdeKt0REUlU5oP/s88CS5cWtinoi0i7y2zwHxsD/uAPCtuefBL4/OfT6Y+ISCNlLvhv2wb0FxU4VdAXkazJTPAPCvpPPOHOzhURyZq2D/6/+AXwqU8Vtm3d6s7OFRHJqrry/EleSXInyaMk+33tfSQPktzuPe7wPbeE5Askx0l+kyTr6UM5/sC/davL01fgF5Gsq3fkvwPA5QC+G/DcK2Z2VkD7dwB8HcDPATwEYDmAxLZNPfcc8O67wLnnJvUKIiKtp67gb2YvAUClg3eSpwP4kJn9zPv5XgCXIsHgf/bZSd1ZRKR1JVneYSHJX5D8Z5J/5LWdAWCf75p9XlsgkoMkx0iOTU5OJthVEZFsKTvyJ7kVwO8EPLXOzB4MaAeA1wAsMLMpkksAPEDyzGo7Z2bDAIYBoL+/36r9fRERCVY2+JvZhdXe1Mw+APCB9/02kq8A+ASA/QDm+S6d57WJiEgDJTLtQ7KHZIf3/UcBLALwqpm9BuAdkp/xsny+BiDs04OIiCSk3lTPy0juA/CHAH5M8lHvqXMBPE9yO4AfAFhrZm95z90I4P8CGAfwChJc7BURkWA0a42p9P7+fhsbG0u7GyIiLYPkNjPrD3pOh7mIiGSQgr+ISAYp+IuIZJCCv4hIBin4i4hkkIK/iEgGKfiLiGSQgr+ISAYp+EcZHQX6+oBZs9zX0dG0eyQiEou2P8axZqOjwOAgMD3tfp6YcD8DwMBAev0SEYmBRv5h1q3LB/6c6WnXLiLS4hT8w+zZU127iEgLUfAPs2BBde0iIi2kvYN/PQu2Q0NAV1dhW1eXaxcRaXHtG/xzC7YTE4BZfsG20jeAgQFgeBjo7QVI93V4WIu9ItIW2reef1+fC/jFenuB3bvj6paISNPKZj1/LdiKiISq9xjH/0Xy30k+T3IzyQ/7nruV5DjJXSSX+dqXe23jJG+p5/Ujxb1gqw1fItJG6h35Pw7g98zskwB+BeBWACC5GMBKAGcCWA5gPckO71D3bwO4CMBiAFd518YvzgXbetcPRESaTF3B38weM7PD3o8/AzDP+34FgI1m9oGZ/RrusPal3mPczF41s0MANnrXxi/OBVtt+BKRNhNneYdrAfw/7/sz4N4McvZ5bQCwt6j902E3JDkIYBAAFtQyXTMwEE92jtYPRKTNlB35k9xKckfAY4XvmnUADgOIdR7EzIbNrN/M+nt6euK8dXW04UtE2kzZkb+ZXRj1PMmrAVwC4ALL543uBzDfd9k8rw0R7c1raKiwyBugDV8i0tLqzfZZDuC/AfhPZuafFN8CYCXJ40kuBLAIwDMAngWwiORCkp1wi8Jb6ulDQ2jDl4i0mXrn/L8F4HgAj5MEgJ+Z2Voz20lyE4AX4aaDbjKzIwBA8mYAjwLoALDBzHbW2YfGiGv9QESkCbTvDl8RkYzL5g5fEREJpeAvIpJBCv4iIhmk4C8ikkEts+BLchJAQI3mVMwF8GbanWgi+nsU0t+jkP4ehRr59+g1s8Adsi0T/JsJybGwFfQs0t+jkP4ehfT3KNQsfw9N+4iIZJCCv4hIBin412Y47Q40Gf09CunvUUh/j0JN8ffQnL+ISAZp5C8ikkEK/iIiGaTgX6Oow+uziOSVJHeSPEoy9TS2NJBcTnIXyXGSt6Tdn7SR3EDyDZI70u5L2kjOJ/kUyRe9/0++kXafFPxrF3h4fYbtAHA5gKfT7kgaSHYA+DaAiwAsBnAVycXp9ip1dwNYnnYnmsRhAP/FzBYD+AyAm9L+70PBv0YRh9dnkpm9ZGa70u5HipYCGDezV83sEICNAFaU+Z22ZmZPA3gr7X40AzN7zcye875/F8BLyJ9rngoF/3hcC+DhtDshqToDwF7fz/uQ8v/c0pxI9gE4G8DP0+xHvSd5tTWSWwH8TsBT68zsQe+aRA6vb0aV/D1EJBzJkwH8EMBfmNk7afZFwT9CjYfXt61yf4+M2w9gvu/neV6bCACA5Gy4wD9qZj9Kuz+a9qlRxOH1kk3PAlhEciHJTgArAWxJuU/SJOgOOf9HAC+Z2d+l3R9Awb8e3wJwCtzh9dtJ3pF2h9JE8jKS+wD8IYAfk3w07T41krf4fzOAR+EW8zaZ2c50e5UukvcD+CmA/0ByH8k/S7tPKToHwGoA53vxYjvJi9PskMo7iIhkkEb+IiIZpOAvIpJBCv4iIhmk4C8ikkEK/iIiGaTgLyKSQQr+IiIZ9P8B95XUSx/h704AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Logistic Regression:\n",
        "\n",
        "F = wx + b and sigmoid at the end"
      ],
      "metadata": {
        "id": "RCLfE2-EdYv-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "from sklearn import datasets\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt "
      ],
      "metadata": {
        "id": "EMXIK81EcuAu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##1- Generating Dataset:"
      ],
      "metadata": {
        "id": "18V7a0l0d21t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bc = datasets.load_breast_cancer()\n",
        "x, y = bc.data, bc.target"
      ],
      "metadata": {
        "id": "P0qV7-2_deWR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_samples, n_features = x.shape\n",
        "print(x.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vt3SlhSqdfa6",
        "outputId": "816f1812-3a13-43bc-e92f-0c222d895406"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(569, 30)\n",
            "(569,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 1234)"
      ],
      "metadata": {
        "id": "Cou3_vlzfNP0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAkIvl-Kdfdg",
        "outputId": "50bf325d-228f-4441-b21e-e2fc6ade70d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(455, 30)\n",
            "(114, 30)\n",
            "(455,)\n",
            "(114,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###StandardScaler will make our features have zero mean and unit variance. This is always recommended to do when we deal with logistic regression."
      ],
      "metadata": {
        "id": "HEBXlOA4fycB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)"
      ],
      "metadata": {
        "id": "xLQ2sXzwflqQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = torch.from_numpy(x_train.astype(np.float32))\n",
        "x_test = torch.from_numpy(x_test.astype(np.float32))\n",
        "y_train = torch.from_numpy(y_train.astype(np.float32))\n",
        "y_test = torch.from_numpy(y_test.astype(np.float32))"
      ],
      "metadata": {
        "id": "x2SMPCk9gRR3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CtdcwGFQgZet",
        "outputId": "b3491324-8393-4599-ff66-eb9309aaee76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([455, 30])\n",
            "torch.Size([114, 30])\n",
            "torch.Size([455])\n",
            "torch.Size([114])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = y_train.view(y_train.shape[0],1)\n",
        "y_test = y_test.view(y_test.shape[0],1)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TNCkdUCvggu6",
        "outputId": "77921a88-da97-41fc-e56a-c90c7628156e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([455, 1])\n",
            "torch.Size([114, 1])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##2- Model:"
      ],
      "metadata": {
        "id": "ZA1kVA7jec8b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class LogisticRegression(nn.Module):\n",
        "  def __init__(self, n_input_features):\n",
        "    super(LogisticRegression, self).__init__()\n",
        "\n",
        "    # Define Layers\n",
        "    self.linear = nn.Linear(n_input_features, 1)\n",
        "\n",
        "  def forward(self, x):\n",
        "    y_predicted = torch.sigmoid(self.linear(x))\n",
        "    return y_predicted"
      ],
      "metadata": {
        "id": "pnusNBHjdfjT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression(n_features)"
      ],
      "metadata": {
        "id": "ZkEgc45adffw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3- Loss & Optimizer:"
      ],
      "metadata": {
        "id": "CFW0F-cnhqTW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "learning_rate = 0.01\n",
        "criterion = nn.BCELoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)"
      ],
      "metadata": {
        "id": "qCjQlcCGhpgD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##4- Training Loop:"
      ],
      "metadata": {
        "id": "c8k7t_8dh2bp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 100\n",
        "for epoch in range(num_epochs):\n",
        "  # Forward pass and loss\n",
        "  y_predicted = model(x_train)\n",
        "  loss = criterion(y_predicted, y_train)\n",
        "\n",
        "  # Backward pass (calculating gradients)\n",
        "  loss.backward()\n",
        "\n",
        "  # Update\n",
        "  optimizer.step()\n",
        "\n",
        "  # Empty gradients\n",
        "  optimizer.zero_grad()\n",
        "\n",
        "  if((epoch+1) % 10 == 0):\n",
        "    print(\"Epoch \" + str(epoch + 1) + \" loss = \" +str(loss.item()) +\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k9A6wbdih1vm",
        "outputId": "74e0a511-95e9-474d-9fd4-f35e6177189d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 10 loss = 0.603636622428894\n",
            "Epoch 20 loss = 0.48827680945396423\n",
            "Epoch 30 loss = 0.41575825214385986\n",
            "Epoch 40 loss = 0.36646655201911926\n",
            "Epoch 50 loss = 0.3308294713497162\n",
            "Epoch 60 loss = 0.3038000166416168\n",
            "Epoch 70 loss = 0.28251707553863525\n",
            "Epoch 80 loss = 0.2652551829814911\n",
            "Epoch 90 loss = 0.25091785192489624\n",
            "Epoch 100 loss = 0.23877714574337006\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##5- Evalutaion:"
      ],
      "metadata": {
        "id": "Iqg8uHbdiMVY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# To prevent tracking the gradient calculations for the below variables\n",
        "with torch.no_grad():\n",
        "  y_predicted = model(x_test)\n",
        "  y_predicted_classes = y_predicted.round()\n",
        "  acc = y_predicted_classes.eq(y_test).sum() / float(y_test.shape[0])\n",
        "  print(\"accuracy = \"+ str(acc) +\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_y81L7ciG9K",
        "outputId": "0a73e3fc-e85b-4c83-a2bc-46b34fbbf661"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy = tensor(0.9123)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Dataset & Data Loader Classes:"
      ],
      "metadata": {
        "id": "Tjr70ZWPPajG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### This is useful for large datasets\n",
        "\n",
        "\n",
        "\n",
        "*   **Epoch:** Forward and backward pass for all training samples.\n",
        "*   **batch_size:** No. of training samples in one forward and backward pass.\n",
        "*   **Iterations:** No. of times we need to loop to cover all the training samples (one epoch).\n",
        "\n",
        "Ex. 100 samples with batch size 20 will have 5 iterations to complete one epoch.\n",
        "\n"
      ],
      "metadata": {
        "id": "Qcp0HhrqPqh3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://gist.github.com/tijptjik/9408623/archive/b237fa5848349a14a14e5d4107dc7897c21951f5.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xjnQ_TMWRzNw",
        "outputId": "1390b9e9-bb7f-42a9-c6c9-e59e477012bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-03-09 16:50:02--  https://gist.github.com/tijptjik/9408623/archive/b237fa5848349a14a14e5d4107dc7897c21951f5.zip\n",
            "Resolving gist.github.com (gist.github.com)... 140.82.112.3\n",
            "Connecting to gist.github.com (gist.github.com)|140.82.112.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://codeload.github.com/gist/9408623/zip/b237fa5848349a14a14e5d4107dc7897c21951f5 [following]\n",
            "--2023-03-09 16:50:02--  https://codeload.github.com/gist/9408623/zip/b237fa5848349a14a14e5d4107dc7897c21951f5\n",
            "Resolving codeload.github.com (codeload.github.com)... 140.82.114.10\n",
            "Connecting to codeload.github.com (codeload.github.com)|140.82.114.10|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [application/zip]\n",
            "Saving to: ‘b237fa5848349a14a14e5d4107dc7897c21951f5.zip’\n",
            "\n",
            "\r          b237fa584     [<=>                 ]       0  --.-KB/s               \rb237fa5848349a14a14     [ <=>                ]   4.57K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-03-09 16:50:02 (10.1 MB/s) - ‘b237fa5848349a14a14e5d4107dc7897c21951f5.zip’ saved [4680]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip b237fa5848349a14a14e5d4107dc7897c21951f5.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ULLEMQH_R8pW",
        "outputId": "e177cb31-e9f5-4939-c930-1f3423d5beaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  b237fa5848349a14a14e5d4107dc7897c21951f5.zip\n",
            "b237fa5848349a14a14e5d4107dc7897c21951f5\n",
            "   creating: 9408623-b237fa5848349a14a14e5d4107dc7897c21951f5/\n",
            "  inflating: 9408623-b237fa5848349a14a14e5d4107dc7897c21951f5/wine.csv  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp /content/9408623-b237fa5848349a14a14e5d4107dc7897c21951f5/wine.csv /content/"
      ],
      "metadata": {
        "id": "tsbaxjhgSDfT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "import os\n",
        "os.remove(\"b237fa5848349a14a14e5d4107dc7897c21951f5.zip\")\n",
        "shutil.rmtree(\"9408623-b237fa5848349a14a14e5d4107dc7897c21951f5\")"
      ],
      "metadata": {
        "id": "fjeRHF8jSQ8Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import numpy as np\n",
        "import math"
      ],
      "metadata": {
        "id": "ERqVc5TCiHAo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Dataset:"
      ],
      "metadata": {
        "id": "OrUnCNC4UTBs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class WineDataset(Dataset):\n",
        "  def __init__(self):\n",
        "    # Data loading\n",
        "    xy = np.loadtxt(\"wine.csv\", delimiter = ',', dtype = np.float32, skiprows = 1)\n",
        "    self.x = torch.from_numpy(xy[:, 1:])\n",
        "    self.y = torch.from_numpy(xy[:, [0]]) # We took it like this so that the size will be (no. of samples, 1) intead of (no. of samples, )\n",
        "    self.n_samples = xy.shape[0]\n",
        "  def __getitem__(self, index):\n",
        "    # dataset[0]\n",
        "    return self.x[index], self.y[index]\n",
        "  def __len__(self):\n",
        "    # len(dataset)\n",
        "    return self.n_samples"
      ],
      "metadata": {
        "id": "9_I4be6jRD3z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = WineDataset()"
      ],
      "metadata": {
        "id": "iI55pk-bTtnG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "first_sample = dataset[0]\n",
        "features, labels = first_sample"
      ],
      "metadata": {
        "id": "g4u3lJmoTy7l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(features)\n",
        "print(labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LdZycxNCT481",
        "outputId": "a4889a48-2d2b-48a5-8dbe-4c18679c4587"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([1.4230e+01, 1.7100e+00, 2.4300e+00, 1.5600e+01, 1.2700e+02, 2.8000e+00,\n",
            "        3.0600e+00, 2.8000e-01, 2.2900e+00, 5.6400e+00, 1.0400e+00, 3.9200e+00,\n",
            "        1.0650e+03])\n",
            "tensor([1.])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Dataloader:"
      ],
      "metadata": {
        "id": "98h-IqLTUU08"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting the argument num_workers as a positive integer will turn on multi-process data loading with the specified number of loader worker processes.\n",
        "dataloader = DataLoader(dataset = dataset, batch_size = 4, shuffle = True, num_workers = 2)"
      ],
      "metadata": {
        "id": "d69unw0wUDZI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataiter = iter(dataloader)\n",
        "data = next(dataiter)\n",
        "features, labels = data"
      ],
      "metadata": {
        "id": "4XswaFrCVAjZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Since batch_size = 4, we will have four numbers for the labels, since they are labels of 4 samples.\n",
        "print(features)\n",
        "print(labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RaWX_qm_VBFa",
        "outputId": "6638ac51-d86c-46d1-d286-f07fa47635f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[1.3500e+01, 3.1200e+00, 2.6200e+00, 2.4000e+01, 1.2300e+02, 1.4000e+00,\n",
            "         1.5700e+00, 2.2000e-01, 1.2500e+00, 8.6000e+00, 5.9000e-01, 1.3000e+00,\n",
            "         5.0000e+02],\n",
            "        [1.3630e+01, 1.8100e+00, 2.7000e+00, 1.7200e+01, 1.1200e+02, 2.8500e+00,\n",
            "         2.9100e+00, 3.0000e-01, 1.4600e+00, 7.3000e+00, 1.2800e+00, 2.8800e+00,\n",
            "         1.3100e+03],\n",
            "        [1.2510e+01, 1.2400e+00, 2.2500e+00, 1.7500e+01, 8.5000e+01, 2.0000e+00,\n",
            "         5.8000e-01, 6.0000e-01, 1.2500e+00, 5.4500e+00, 7.5000e-01, 1.5100e+00,\n",
            "         6.5000e+02],\n",
            "        [1.4340e+01, 1.6800e+00, 2.7000e+00, 2.5000e+01, 9.8000e+01, 2.8000e+00,\n",
            "         1.3100e+00, 5.3000e-01, 2.7000e+00, 1.3000e+01, 5.7000e-01, 1.9600e+00,\n",
            "         6.6000e+02]])\n",
            "tensor([[3.],\n",
            "        [1.],\n",
            "        [3.],\n",
            "        [3.]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training Loop:"
      ],
      "metadata": {
        "id": "-5ZmA8GOWFph"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 2\n",
        "total_samples = len(dataset)\n",
        "n_iterations = math.ceil(total_samples/4)"
      ],
      "metadata": {
        "id": "2v7uM8D9VBHl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(total_samples, n_iterations)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30ESt1pdVBK8",
        "outputId": "1e3f5a49-f3b4-4013-ab20-23a690d869ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "178 45\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(num_epochs):\n",
        "  for i, (inputs, labels) in enumerate(dataloader): # enumerate will give us the index and the data\n",
        "    if((i+1) % 5 == 0):\n",
        "      print(\"Epoch: \"+str(epoch+1)+\"/\"+str(num_epochs)+\" step: \"+str(i+1)+\"/\"+str(n_iterations)+\" inputs: \"+str(inputs.size())+\"\"+\" labels: \"+str(labels.size())+\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-k4D7BR3WbvK",
        "outputId": "b942a112-9f07-4e57-e9ab-b11d78c86e7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1/2 step: 5/45 inputs: torch.Size([4, 13]) labels: torch.Size([4, 1])\n",
            "Epoch: 1/2 step: 10/45 inputs: torch.Size([4, 13]) labels: torch.Size([4, 1])\n",
            "Epoch: 1/2 step: 15/45 inputs: torch.Size([4, 13]) labels: torch.Size([4, 1])\n",
            "Epoch: 1/2 step: 20/45 inputs: torch.Size([4, 13]) labels: torch.Size([4, 1])\n",
            "Epoch: 1/2 step: 25/45 inputs: torch.Size([4, 13]) labels: torch.Size([4, 1])\n",
            "Epoch: 1/2 step: 30/45 inputs: torch.Size([4, 13]) labels: torch.Size([4, 1])\n",
            "Epoch: 1/2 step: 35/45 inputs: torch.Size([4, 13]) labels: torch.Size([4, 1])\n",
            "Epoch: 1/2 step: 40/45 inputs: torch.Size([4, 13]) labels: torch.Size([4, 1])\n",
            "Epoch: 1/2 step: 45/45 inputs: torch.Size([2, 13]) labels: torch.Size([2, 1])\n",
            "Epoch: 2/2 step: 5/45 inputs: torch.Size([4, 13]) labels: torch.Size([4, 1])\n",
            "Epoch: 2/2 step: 10/45 inputs: torch.Size([4, 13]) labels: torch.Size([4, 1])\n",
            "Epoch: 2/2 step: 15/45 inputs: torch.Size([4, 13]) labels: torch.Size([4, 1])\n",
            "Epoch: 2/2 step: 20/45 inputs: torch.Size([4, 13]) labels: torch.Size([4, 1])\n",
            "Epoch: 2/2 step: 25/45 inputs: torch.Size([4, 13]) labels: torch.Size([4, 1])\n",
            "Epoch: 2/2 step: 30/45 inputs: torch.Size([4, 13]) labels: torch.Size([4, 1])\n",
            "Epoch: 2/2 step: 35/45 inputs: torch.Size([4, 13]) labels: torch.Size([4, 1])\n",
            "Epoch: 2/2 step: 40/45 inputs: torch.Size([4, 13]) labels: torch.Size([4, 1])\n",
            "Epoch: 2/2 step: 45/45 inputs: torch.Size([2, 13]) labels: torch.Size([2, 1])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Dataset Transforms:"
      ],
      "metadata": {
        "id": "gjPjFs-LYe2J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Transforms can be applied to PIL images, tensors, ndarrays, or custom data \n",
        "during creation of the Dataset\n",
        "\n",
        "* complete list of built-in transforms:\n",
        "https://pytorch.org/docs/stable/torchvision/transforms.html\n",
        "* On Images\n",
        "\n",
        "CenterCrop, Grayscale, Pad, RandomAffine RandomCrop, RandomHorizontalFlip, RandomRotation Resize, Scale\n",
        "* On Tensors\n",
        "\n",
        "LinearTransformation, Normalize, RandomErasing\n",
        "* Conversion\n",
        "\n",
        "ToPILImage: from tensor or ndrarray\n",
        "\n",
        "ToTensor : from numpy.ndarray or PILImage\n",
        "\n",
        "* Generic\n",
        "\n",
        "Use Lambda\n",
        "* Custom\n",
        "\n",
        "Write own class\n",
        "* Compose multiple Transforms\n",
        "\n",
        "composed = transforms. Compose([Rescale(256),\n",
        "RandomCrop(224)])\n",
        "\n",
        "torchvision.transforms.ReScale(256)\n",
        "\n",
        "torchvision.transforms.ToTensor()"
      ],
      "metadata": {
        "id": "YsrJq2fwY-wA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://gist.github.com/tijptjik/9408623/archive/b237fa5848349a14a14e5d4107dc7897c21951f5.zip"
      ],
      "metadata": {
        "id": "psQfW8Utbhvk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip b237fa5848349a14a14e5d4107dc7897c21951f5.zip"
      ],
      "metadata": {
        "id": "Oa3TFyNlbiDS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp /content/9408623-b237fa5848349a14a14e5d4107dc7897c21951f5/wine.csv /content/"
      ],
      "metadata": {
        "id": "g8XVkf2obiFp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "import os\n",
        "os.remove(\"b237fa5848349a14a14e5d4107dc7897c21951f5.zip\")\n",
        "shutil.rmtree(\"9408623-b237fa5848349a14a14e5d4107dc7897c21951f5\")"
      ],
      "metadata": {
        "id": "iBz7_iUPbiJE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import numpy as np\n",
        "import math"
      ],
      "metadata": {
        "id": "-ESPpbTjbn4K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class WineDataset(Dataset):\n",
        "  def __init__(self, transform=None):\n",
        "    # Data loading\n",
        "    xy = np.loadtxt(\"wine.csv\", delimiter = ',', dtype = np.float32, skiprows = 1)\n",
        "    self.n_samples = xy.shape[0]\n",
        "\n",
        "    self.x = xy[:, 1:]\n",
        "    self.y = xy[:, [0]] # We took it like this so that the size will be (no. of samples, 1) intead of (no. of samples, )\n",
        "    \n",
        "    self.transform = transform\n",
        "\n",
        "  def __getitem__(self, index):\n",
        "    # dataset[0]\n",
        "    sample = self.x[index], self.y[index]\n",
        "    if(self.transform):\n",
        "      sample = self.transform(sample)\n",
        "    return sample\n",
        "\n",
        "  def __len__(self):\n",
        "    # len(dataset)\n",
        "    return self.n_samples"
      ],
      "metadata": {
        "id": "k-OVi-uVYpRs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Creating Custom Transforms:"
      ],
      "metadata": {
        "id": "0dSb9lzdeZtC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class ToTensor():\n",
        "  def __call__(self, sample):\n",
        "    inputs, targets = sample\n",
        "    return torch.from_numpy(inputs), torch.from_numpy(targets)"
      ],
      "metadata": {
        "id": "logz7TYib51F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = WineDataset(transform = None)"
      ],
      "metadata": {
        "id": "wfNKJtENcwXw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "first_data = dataset[0]\n",
        "features, labels = first_data "
      ],
      "metadata": {
        "id": "__oRdruxcxPl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(features), type(labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LpjoxwUDcyLg",
        "outputId": "4d0ed658-ca6d-496a-a8e8-7fb25a6b68e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = WineDataset(transform = ToTensor())"
      ],
      "metadata": {
        "id": "gvMfVPzMcWeY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "first_data = dataset[0]\n",
        "features, labels = first_data "
      ],
      "metadata": {
        "id": "fqU_Sy44cd48"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(features), type(labels))\n",
        "print(features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m-uHBVaKcjk4",
        "outputId": "423fce80-ca7c-45d0-d0ee-b899608f21dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'torch.Tensor'> <class 'torch.Tensor'>\n",
            "tensor([1.4230e+01, 1.7100e+00, 2.4300e+00, 1.5600e+01, 1.2700e+02, 2.8000e+00,\n",
            "        3.0600e+00, 2.8000e-01, 2.2900e+00, 5.6400e+00, 1.0400e+00, 3.9200e+00,\n",
            "        1.0650e+03])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class MulTransform():\n",
        "  def __init__(self, factor):\n",
        "    self.factor = factor\n",
        "\n",
        "  def __call__(self, sample):\n",
        "    inputs, target = sample\n",
        "    inputs *= self.factor\n",
        "    return inputs, target"
      ],
      "metadata": {
        "id": "wPZnBodacspR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Composed Transforms:"
      ],
      "metadata": {
        "id": "JEhC9ZhqedL4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "composed = torchvision.transforms.Compose([ToTensor(),MulTransform(2)])"
      ],
      "metadata": {
        "id": "qGENxvoIdP5K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = WineDataset(transform = composed)"
      ],
      "metadata": {
        "id": "Pm-IAGUEdhAq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "first_data = dataset[0]\n",
        "features, labels = first_data "
      ],
      "metadata": {
        "id": "ELP7ak6SdhEE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(features), type(labels))\n",
        "print(features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k3FCUYS9dy-N",
        "outputId": "b298485c-70fe-4e28-915e-131680a35b90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'torch.Tensor'> <class 'torch.Tensor'>\n",
            "tensor([2.8460e+01, 3.4200e+00, 4.8600e+00, 3.1200e+01, 2.5400e+02, 5.6000e+00,\n",
            "        6.1200e+00, 5.6000e-01, 4.5800e+00, 1.1280e+01, 2.0800e+00, 7.8400e+00,\n",
            "        2.1300e+03])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Feed Forward Neural Network:"
      ],
      "metadata": {
        "id": "LRQkoS8VTxn9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "ei69uROPeT5d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Device configuration\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Hyper parameters\n",
        "input_size = 784 # Because our images are of size 28 X 28\n",
        "hidden_size = 100 # We can try different sizes\n",
        "n_classes = 10\n",
        "n_epochs = 2\n",
        "batch_size = 100\n",
        "learning_rate = 0.001"
      ],
      "metadata": {
        "id": "XtqpWw5oUIde"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importing MNIST:"
      ],
      "metadata": {
        "id": "YBWeYofSUquD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = torchvision.datasets.MNIST(root = \"./data\", train = True, transform = transforms.ToTensor(), download = True) # download = True means download it if it is not downloaded in the ./data directory\n",
        "test_dataset = torchvision.datasets.MNIST(root = \"./data\", train = False, transform = transforms.ToTensor(), download = True)\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(dataset = train_dataset, batch_size = batch_size, shuffle = True) # shuffle = True makes it better for training\n",
        "test_loader = torch.utils.data.DataLoader(dataset = test_dataset, batch_size = batch_size, shuffle = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443,
          "referenced_widgets": [
            "91f8a196593a4933be359267bcc2f95d",
            "ac750e8ffae446d1888894ddeb057251",
            "05815eebc77447268c0c917172de2851",
            "1ae3b669fcb94e87abdcb143f23e125e",
            "b739bb5284074f008e5690e85a236d54",
            "2a3d6ba9789347d1926b178b886bcc6d",
            "e889481abc91418498791fec9ac30e89",
            "c02829981c4447558f270d7e52618fc7",
            "5ecb99ad82ce44348d87ff746b60b949",
            "be7a0d83513144f2a7cc1f1130c22d69",
            "1a1469044ec74cd8a9d2be60f0c89220",
            "374d5845f53449e9ab5bb3a52379e9f2",
            "d835ca1173b34e859e8f152f35ce2366",
            "2260db56c0ea4fbdbd30b9e7cc996e4b",
            "ec95204d78c743529b676290db6c9c68",
            "e17cc640ed30467e9d25159848b6f385",
            "40187d3c1a304214aab5b00678d094e5",
            "d53f5a5508bb49be9d43648363aa9fa1",
            "2ec585015275406c89529723b53a734e",
            "f43e0b0d14dd418fa2950da7e2fc4a49",
            "634707fa930c45278fc2168190eff31e",
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            "6805a676566f4d46adb4fe4b0e942b64",
            "391cc4242755483d8522aeefc27e0e1a",
            "39cd6eb0805f44be853d4562836eaacc",
            "a40c04cee05c4ead82a05cabd4adbc9f",
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            "752aa1dffa5645a1b0bd36fb3e18ee92",
            "1c803e0213b245c59e05540a97e76bce",
            "71e7424952fc477ea7a098d0c0f22c70",
            "6e3ad3247ddb442793914f55b24f61e7",
            "6989040893d746359da962d68be2418f",
            "6b6a272d4afd488aad3b5152d7c43e3e",
            "01389ddf4acb47d8934f37ceb1895ffb",
            "160a1177c75f495c9905efe37db1dd1a",
            "0b51f47995ed4e6bbe972c1306a10805",
            "31139d781048469ea5e91395972f3029",
            "69304e09e3034a53bc4e8e24f2c66022",
            "dd38dcfdc1e549148aab52ad20bf5510",
            "f14867644e8240568339911c145c449e",
            "7b0ff1095517448f8f439ef9beeb1352"
          ]
        },
        "id": "HsKnQDAOUplK",
        "outputId": "65b9eeb1-97ed-43ea-c211-24d87c56818d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91f8a196593a4933be359267bcc2f95d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/28881 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "374d5845f53449e9ab5bb3a52379e9f2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "07b45c311a3645e8afbf113dd2d1cdd0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e3ad3247ddb442793914f55b24f61e7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "example = iter(train_loader)\n",
        "samples, labels = next(example)\n",
        "print(samples.shape, labels.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UfDQZpLRV4Ig",
        "outputId": "8d69c85d-4c7a-4815-ab89-b612100ae3d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([100, 1, 28, 28]) torch.Size([100])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(6):\n",
        "  plt.subplot(2,3,i+1) # 2 rows & 3 columns\n",
        "  plt.imshow(samples[i][0], cmap = 'gray')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "rjnGn58sWEAg",
        "outputId": "75041ce9-e259-446b-d0e1-f140cbe6c7f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Network Creation:"
      ],
      "metadata": {
        "id": "VuCWxY_BW38p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### We will not apply softmax in the last layer since we will be using PyTorch's cross entropy loss and it applies the softmax function"
      ],
      "metadata": {
        "id": "2hpkEOakX0pW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class NeuralNet(nn.Module):\n",
        "  def __init__(self, input_size, hidden_size, n_classes):\n",
        "    super(NeuralNet, self).__init__()\n",
        "    self.l1 = nn.Linear(input_size, hidden_size)\n",
        "    self.relu = nn.ReLU()\n",
        "    self.l2 = nn.Linear(hidden_size, n_classes)\n",
        "\n",
        "  def forward(self, x):\n",
        "    out = self.l1(x)\n",
        "    out = self.relu(out)\n",
        "    out = self.l2(out)\n",
        "    return out"
      ],
      "metadata": {
        "id": "GXzC4YEOWsDb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = NeuralNet(input_size, hidden_size, n_classes)"
      ],
      "metadata": {
        "id": "SeV16o-jYAW7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Loss & Optmizer:"
      ],
      "metadata": {
        "id": "uIbgtxMzYJ5O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr = learning_rate)"
      ],
      "metadata": {
        "id": "8YLTTqbGYIEf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Training Loop:"
      ],
      "metadata": {
        "id": "J6TttIUnYdgi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_total_steps = len(train_loader)\n",
        "for epoch in range(n_epochs):\n",
        "  for i, (images,labels) in enumerate(train_loader):# enumerate will give us the index and the data\n",
        "    # images is 100, 1, 28, 28\n",
        "    # we want 100, 784\n",
        "    images = images.reshape(-1, 28*28).to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    # Forward\n",
        "    outputs = model(images)\n",
        "    loss = criterion(outputs, labels)\n",
        "\n",
        "    # Backward\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    if((i+1)%100 == 0):\n",
        "      print(\"Epoch: \"+str(epoch+1)+\"/\"+str(n_epochs)+\", Step: \"+str(i+1)+\"/\"+str(n_total_steps)+\", loss: \"+str(loss.item())+\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7M8yEegpYbY_",
        "outputId": "543d3fbd-4ae2-4f20-c6fd-c7f255bf7a6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1/2, Step: 100/600, loss: 0.37781304121017456\n",
            "Epoch: 1/2, Step: 200/600, loss: 0.3390708863735199\n",
            "Epoch: 1/2, Step: 300/600, loss: 0.19096477329730988\n",
            "Epoch: 1/2, Step: 400/600, loss: 0.2176024317741394\n",
            "Epoch: 1/2, Step: 500/600, loss: 0.28206324577331543\n",
            "Epoch: 1/2, Step: 600/600, loss: 0.24062994122505188\n",
            "Epoch: 2/2, Step: 100/600, loss: 0.12743119895458221\n",
            "Epoch: 2/2, Step: 200/600, loss: 0.16623765230178833\n",
            "Epoch: 2/2, Step: 300/600, loss: 0.20040565729141235\n",
            "Epoch: 2/2, Step: 400/600, loss: 0.1089562326669693\n",
            "Epoch: 2/2, Step: 500/600, loss: 0.1645544022321701\n",
            "Epoch: 2/2, Step: 600/600, loss: 0.1631811559200287\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Testing & Evaluation:"
      ],
      "metadata": {
        "id": "ZQlIkPxTaXFc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "  n_correct = 0\n",
        "  n_samples = 0\n",
        "  for images, labels in test_loader:\n",
        "    images = images.reshape(-1, 28*28).to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    outputs = model(images)\n",
        "\n",
        "    # value, index\n",
        "    _,predictions = torch.max(outputs, 1) # Get the maximum no. along the first dimension\n",
        "\n",
        "    # no. of samples in the current batch (should be 100)\n",
        "    n_samples += labels.shape[0]\n",
        "    \n",
        "    n_correct += (predictions == labels).sum().item()\n",
        "\n",
        "  acc = (100 * n_correct)/n_samples\n",
        "  print(\"Accuracy = \"+ str(acc) +\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Pt41imYZyaN",
        "outputId": "b8e60af9-de6d-4f18-e231-9acecf8ac916"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy = 95.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Convolutional Neural Network:"
      ],
      "metadata": {
        "id": "Jn9EVoIwiESm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "_9zAZrWHjKiG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Device configuration\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Hyper parameters\n",
        "n_epochs = 4\n",
        "batch_size = 4\n",
        "learning_rate = 0.001"
      ],
      "metadata": {
        "id": "OV0rHX9LjOZX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importing CIFAR & Using Transformations:"
      ],
      "metadata": {
        "id": "_tgJR8-Xk2f7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset has PIL images of range [0,1]\n",
        "# We will transform them to tensors of range [-1,1]\n",
        "transform = transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
        "        ])"
      ],
      "metadata": {
        "id": "maYvYU_ejXtv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = torchvision.datasets.CIFAR10(root = \"./data\", train = True, transform = transform, download = True) # download = True means download it if it is not downloaded in the ./data directory\n",
        "test_dataset = torchvision.datasets.CIFAR10(root = \"./data\", train = False, transform = transform, download = True)\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(dataset = train_dataset, batch_size = batch_size, shuffle = True) # shuffle = True makes it better for training\n",
        "test_loader = torch.utils.data.DataLoader(dataset = test_dataset, batch_size = batch_size, shuffle = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121,
          "referenced_widgets": [
            "4448f0738f51465d91f450a70de825db",
            "abfcfafa989f4085958da57441a7b687",
            "1438fbfe83ea4c0ca1510ba3f25bf9ec",
            "701f9637eea44400bfba643026556830",
            "887cd3c235ca495f891595fbfbc2a36a",
            "a00a3463a1944299a50ee6216a1e4033",
            "c25bd7f6b1a74f93b3ec8ebe572330c0",
            "6d29fac1b4774e66924972b2830bfc0b",
            "96f71b3de5a24b358a2ed79f2f26aba1",
            "b6ea10e6b68f48cabdaf9c553acf0280",
            "2dc5b2335feb4907adb407cea6889eae"
          ]
        },
        "id": "2YmuR9agjOcz",
        "outputId": "74582531-9519-4882-97d9-0c92b92292a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4448f0738f51465d91f450a70de825db"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
      ],
      "metadata": {
        "id": "m8iJqJgOkYE_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "example = iter(train_loader)\n",
        "samples, labels = next(example)\n",
        "print(samples.shape, labels.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UxEVaSWwpfqz",
        "outputId": "7185a583-6bfa-4c13-f3a3-2923efbde95c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([4, 3, 32, 32]) torch.Size([4])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Network Creation:"
      ],
      "metadata": {
        "id": "s-ayKnRliWIy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### In the fully connected layers we can change any size except for the input size to the first FC layer and the output of the last FC layer"
      ],
      "metadata": {
        "id": "I7Kotu70o5Oh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Output size calculation after passing through conv/pooling layer:\n",
        "\n",
        "(W-F + 2P)/S + 1\n",
        "\n",
        "W: Size of input image\n",
        "F: Size of kernel\n",
        "P: Padding\n",
        "S: Stride"
      ],
      "metadata": {
        "id": "xuea8PetsyrM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class ConvNet(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(ConvNet, self).__init__()\n",
        "    self.conv1 = nn.Conv2d(3,6,5) # input channel size is 3 becuase images are rgb, output channel size is 6, and kernel size is 5 X 5\n",
        "    self.pool = nn.MaxPool2d(2,2) # kernel size is 2 X 2 and stride is 2\n",
        "    self.conv2 = nn.Conv2d(6,16,5)\n",
        "    self.fc1 = nn.Linear(16*5*5,120) # The last layer produced an output that had 16 channels and 5 X 5 shape for each channel. So, by flattening them we get 16 X 5 X 5\n",
        "    self.fc2 = nn.Linear(120,84) # input size is 120 and output size is 84\n",
        "    self.fc3 = nn.Linear(84,10) # input size is 84 and output size is 10 as we have 10 output classes\n",
        "  def forward(self, x):\n",
        "    x = self.pool(F.relu(self.conv1(x)))\n",
        "    x = self.pool(F.relu(self.conv2(x)))\n",
        "    # To quickly know what size to put in the first linear layer print the shape of x\n",
        "    # print(x.shape)\n",
        "    # Flatten x\n",
        "    x = x.view(-1, 16*5*5) # -1 is the number of batches, so it should be 4\n",
        "    x = F.relu(self.fc1(x))\n",
        "    x = F.relu(self.fc2(x))\n",
        "    x = self.fc3(x)\n",
        "    return x"
      ],
      "metadata": {
        "id": "9XzssM7xk9tf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = ConvNet().to(device)"
      ],
      "metadata": {
        "id": "47RneYkOlIfR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# To quickly know what size to put in the first linear layer\n",
        "# image = torch.ones(1,3,32,32)\n",
        "# print(image.shape)\n",
        "# output = model(image)"
      ],
      "metadata": {
        "id": "_aqky55ougk7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Loss & Optimizer:"
      ],
      "metadata": {
        "id": "de5S2Lb9iPLQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr = learning_rate)"
      ],
      "metadata": {
        "id": "LxGIPZYilOEr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Training Loop:"
      ],
      "metadata": {
        "id": "Oba7YYX0iH1h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_total_steps = len(train_loader)\n",
        "for epoch in range(n_epochs):\n",
        "  for i, (images,labels) in enumerate(train_loader):# enumerate will give us the index and the data\n",
        "    # images is 4, 3, 32, 32\n",
        "    # we want 4, 3, 1024\n",
        "    # input layer = 3 input channels, 6 output channels, 5 kernel size\n",
        "    images = images.to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    # Forward\n",
        "    outputs = model(images)\n",
        "    loss = criterion(outputs, labels)\n",
        "\n",
        "    # Backward\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    if((i+1)%2000 == 0):\n",
        "      print(\"Epoch: \"+str(epoch+1)+\"/\"+str(n_epochs)+\", Step: \"+str(i+1)+\"/\"+str(n_total_steps)+\", loss: \"+str(loss.item())+\"\")\n",
        "print(\"Finished Training!!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wd5Mb6ZtlTRQ",
        "outputId": "e586c7be-09b5-4bc7-e527-ace49429d5b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1/4, Step: 2000/12500, loss: 1.4528508186340332\n",
            "Epoch: 1/4, Step: 4000/12500, loss: 1.4194371700286865\n",
            "Epoch: 1/4, Step: 6000/12500, loss: 0.9429278373718262\n",
            "Epoch: 1/4, Step: 8000/12500, loss: 1.4249820709228516\n",
            "Epoch: 1/4, Step: 10000/12500, loss: 0.7920148968696594\n",
            "Epoch: 1/4, Step: 12000/12500, loss: 1.2776896953582764\n",
            "Epoch: 2/4, Step: 2000/12500, loss: 1.3328248262405396\n",
            "Epoch: 2/4, Step: 4000/12500, loss: 1.4147534370422363\n",
            "Epoch: 2/4, Step: 6000/12500, loss: 1.420710563659668\n",
            "Epoch: 2/4, Step: 8000/12500, loss: 0.8946384191513062\n",
            "Epoch: 2/4, Step: 10000/12500, loss: 1.3212822675704956\n",
            "Epoch: 2/4, Step: 12000/12500, loss: 1.476868748664856\n",
            "Epoch: 3/4, Step: 2000/12500, loss: 0.6034020781517029\n",
            "Epoch: 3/4, Step: 4000/12500, loss: 1.908227801322937\n",
            "Epoch: 3/4, Step: 6000/12500, loss: 0.7130082845687866\n",
            "Epoch: 3/4, Step: 8000/12500, loss: 1.1420385837554932\n",
            "Epoch: 3/4, Step: 10000/12500, loss: 0.6588274836540222\n",
            "Epoch: 3/4, Step: 12000/12500, loss: 0.8213160037994385\n",
            "Epoch: 4/4, Step: 2000/12500, loss: 1.2799288034439087\n",
            "Epoch: 4/4, Step: 4000/12500, loss: 1.945415735244751\n",
            "Epoch: 4/4, Step: 6000/12500, loss: 1.1364613771438599\n",
            "Epoch: 4/4, Step: 8000/12500, loss: 2.2686634063720703\n",
            "Epoch: 4/4, Step: 10000/12500, loss: 0.18536671996116638\n",
            "Epoch: 4/4, Step: 12000/12500, loss: 1.2106815576553345\n",
            "Finished Training!!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Testing & Evaluation:"
      ],
      "metadata": {
        "id": "dr2p4_hqiKBI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "  n_correct = 0\n",
        "  n_samples = 0\n",
        "  n_class_correct = [0 for i in range(10)]\n",
        "  n_class_samples = [0 for i in range(10)]\n",
        "  for images, labels in test_loader:\n",
        "    images = images.to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    outputs = model(images)\n",
        "\n",
        "    # value, index\n",
        "    _,predictions = torch.max(outputs, 1) # Get the maximum no. along the first dimension\n",
        "\n",
        "    # no. of samples in the current batch (should be 100)\n",
        "    n_samples += labels.shape[0]\n",
        "    \n",
        "    n_correct += (predictions == labels).sum().item()\n",
        "\n",
        "    for i in range(batch_size):\n",
        "      label = labels[i]\n",
        "      pred = predictions[i]\n",
        "      if(label == pred):\n",
        "        n_class_correct[label] += 1\n",
        "      n_class_samples[label] += 1\n",
        "\n",
        "  acc = (100 * n_correct)/n_samples\n",
        "  print(\"Accuracy = \"+ str(acc) +\"\")\n",
        "\n",
        "  for i in range(10):\n",
        "    acc = (100 * n_class_correct[i])/n_class_samples[i]\n",
        "    print(\"Accuracy of \"+ classes[i] +\": \"+str(acc)+\"%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZ-QLGBKbo4g",
        "outputId": "e2b29b32-29d0-49e8-ab61-33510fee3947"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy = 61.15\n",
            "Accuracy of plane: 62.9%\n",
            "Accuracy of car: 74.9%\n",
            "Accuracy of bird: 45.5%\n",
            "Accuracy of cat: 40.2%\n",
            "Accuracy of deer: 47.7%\n",
            "Accuracy of dog: 53.9%\n",
            "Accuracy of frog: 74.6%\n",
            "Accuracy of horse: 64.0%\n",
            "Accuracy of ship: 74.7%\n",
            "Accuracy of truck: 73.1%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Transfer Learning & Fine-tuning:"
      ],
      "metadata": {
        "id": "ydsdcfLDkdUu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### A model trained on one task can be loaded and modified a little to be used on another task. Transfer learning is freezing layers that were previously trained (model) and (optional) adding some new trainable layers, or, training the last layers. While fine-tuning is unfreezing the entire model you obtained above (or part of it), and re-training it on the new data with a very low learning rate."
      ],
      "metadata": {
        "id": "_eRrAAlmkfOZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example: take a model that is trained to classify 1000 classes and make it classify only two"
      ],
      "metadata": {
        "id": "Upw1PvFNo6ig"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "from torchvision import datasets, models, transforms\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import time\n",
        "import os\n",
        "import copy"
      ],
      "metadata": {
        "id": "5ffE2hn4zKRk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importing Dataset & Tranformations:"
      ],
      "metadata": {
        "id": "2sUaEcE0qh5O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir data"
      ],
      "metadata": {
        "id": "J8088O2fugKc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/data/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8UQjawvMuleb",
        "outputId": "790d25a7-6b38-4efb-c696-65c22add679e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://github.com/jaddoescad/ants-bees-dataset/archive/refs/heads/master.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y2AW0IJqspDb",
        "outputId": "f718eaec-28f6-4746-92e4-f3637799fa4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-03-13 20:47:42--  https://github.com/jaddoescad/ants-bees-dataset/archive/refs/heads/master.zip\n",
            "Resolving github.com (github.com)... 140.82.114.4\n",
            "Connecting to github.com (github.com)|140.82.114.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://codeload.github.com/jaddoescad/ants-bees-dataset/zip/refs/heads/master [following]\n",
            "--2023-03-13 20:47:42--  https://codeload.github.com/jaddoescad/ants-bees-dataset/zip/refs/heads/master\n",
            "Resolving codeload.github.com (codeload.github.com)... 140.82.113.10\n",
            "Connecting to codeload.github.com (codeload.github.com)|140.82.113.10|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 47285500 (45M) [application/zip]\n",
            "Saving to: ‘master.zip’\n",
            "\n",
            "master.zip          100%[===================>]  45.09M   144MB/s    in 0.3s    \n",
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            "2023-03-13 20:47:42 (144 MB/s) - ‘master.zip’ saved [47285500/47285500]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip master.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AZimTbptuFAm",
        "outputId": "488a99c1-a997-4a61-d590-fde256d3298a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  master.zip\n",
            "37ab5b5f0b73ff225fc7d4cf1861e8d2a3babac6\n",
            "   creating: ants-bees-dataset-master/\n",
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            "  inflating: ants-bees-dataset-master/train/bees/2792000093_e8ae0718cf.jpg  \n",
            "  inflating: ants-bees-dataset-master/train/bees/2801728106_833798c909.jpg  \n",
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            "  inflating: ants-bees-dataset-master/train/bees/2861002136_52c7c6f708.jpg  \n",
            "  inflating: ants-bees-dataset-master/train/bees/2908916142_a7ac8b57a8.jpg  \n",
            "  inflating: ants-bees-dataset-master/train/bees/29494643_e3410f0d37.jpg  \n",
            "  inflating: ants-bees-dataset-master/train/bees/2959730355_416a18c63c.jpg  \n",
            "  inflating: ants-bees-dataset-master/train/bees/2962405283_22718d9617.jpg  \n",
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            "  inflating: ants-bees-dataset-master/train/bees/3030189811_01d095b793.jpg  \n",
            "  inflating: ants-bees-dataset-master/train/bees/3030772428_8578335616.jpg  \n",
            " extracting: ants-bees-dataset-master/train/bees/3044402684_3853071a87.jpg  \n",
            "  inflating: ants-bees-dataset-master/train/bees/3074585407_9854eb3153.jpg  \n",
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            "  inflating: ants-bees-dataset-master/train/bees/3100226504_c0d4f1e3f1.jpg  \n",
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            "  inflating: ants-bees-dataset-master/train/bees/36900412_92b81831ad.jpg  \n",
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            "  inflating: ants-bees-dataset-master/train/bees/92663402_37f379e57a.jpg  \n",
            "  inflating: ants-bees-dataset-master/train/bees/95238259_98470c5b10.jpg  \n",
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            "  inflating: ants-bees-dataset-master/train/bees/98391118_bdb1e80cce.jpg  \n",
            "   creating: ants-bees-dataset-master/val/\n",
            "   creating: ants-bees-dataset-master/val/ants/\n",
            "  inflating: ants-bees-dataset-master/val/ants/10308379_1b6c72e180.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/1053149811_f62a3410d3.jpg  \n",
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            "  inflating: ants-bees-dataset-master/val/ants/153783656_85f9c3ac70.jpg  \n",
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            "  inflating: ants-bees-dataset-master/val/ants/161076144_124db762d6.jpg  \n",
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            "  inflating: ants-bees-dataset-master/val/ants/170652283_ecdaff5d1a.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/17081114_79b9a27724.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/172772109_d0a8e15fb0.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/1743840368_b5ccda82b7.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/181942028_961261ef48.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/183260961_64ab754c97.jpg  \n",
            " extracting: ants-bees-dataset-master/val/ants/2039585088_c6f47c592e.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/205398178_c395c5e460.jpg  \n",
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            "  inflating: ants-bees-dataset-master/val/ants/2127908701_d49dc83c97.jpg  \n",
            " extracting: ants-bees-dataset-master/val/ants/2191997003_379df31291.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/2211974567_ee4606b493.jpg  \n",
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            "  inflating: ants-bees-dataset-master/val/ants/477437164_bc3e6e594a.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/488272201_c5aa281348.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/502717153_3e4865621a.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/518746016_bcc28f8b5b.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/540543309_ddbb193ee5.jpg  \n",
            " extracting: ants-bees-dataset-master/val/ants/562589509_7e55469b97.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/57264437_a19006872f.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/573151833_ebbc274b77.jpg  \n",
            " extracting: ants-bees-dataset-master/val/ants/649407494_9b6bc4949f.jpg  \n",
            " extracting: ants-bees-dataset-master/val/ants/751649788_78dd7d16ce.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/768870506_8f115d3d37.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/800px-Meat_eater_ant_qeen_excavating_hole.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/8124241_36b290d372.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/8398478_50ef10c47a.jpg  \n",
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            "  inflating: ants-bees-dataset-master/val/ants/94999827_36895faade.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/Ant-1818.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/F.pergan.28(f).jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/Hormiga.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/ants-devouring-remains-of-large-dead-insect-on-red-tile-in-Stellenbosch-South-Africa-closeup-1-DHD.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/ants/desert_ant.jpg  \n",
            "   creating: ants-bees-dataset-master/val/bees/\n",
            "  inflating: ants-bees-dataset-master/val/bees/1032546534_06907fe3b3.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/10870992_eebeeb3a12.jpg  \n",
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            "  inflating: ants-bees-dataset-master/val/bees/1297972485_33266a18d9.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/1328423762_f7a88a8451.jpg  \n",
            " extracting: ants-bees-dataset-master/val/bees/1355974687_1341c1face.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/144098310_a4176fd54d.jpg  \n",
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            "  inflating: ants-bees-dataset-master/val/bees/2470492902_3572c90f75.jpg  \n",
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            "  inflating: ants-bees-dataset-master/val/bees/2506114833_90a41c5267.jpg  \n",
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            "  inflating: ants-bees-dataset-master/val/bees/26589803_5ba7000313.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/2668391343_45e272cd07.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/2670536155_c170f49cd0.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/2685605303_9eed79d59d.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/2702408468_d9ed795f4f.jpg  \n",
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            "  inflating: ants-bees-dataset-master/val/bees/2745389517_250a397f31.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/2751836205_6f7b5eff30.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/2782079948_8d4e94a826.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/2809496124_5f25b5946a.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/2815838190_0a9889d995.jpg  \n",
            " extracting: ants-bees-dataset-master/val/bees/2841437312_789699c740.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/2883093452_7e3a1eb53f.jpg  \n",
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            "  inflating: ants-bees-dataset-master/val/bees/348291597_ee836fbb1a.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/350436573_41f4ecb6c8.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/353266603_d3eac7e9a0.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/372228424_16da1f8884.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/400262091_701c00031c.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/416144384_961c326481.jpg  \n",
            " extracting: ants-bees-dataset-master/val/bees/44105569_16720a960c.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/456097971_860949c4fc.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/464594019_1b24a28bb1.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/485743562_d8cc6b8f73.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/540976476_844950623f.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/54736755_c057723f64.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/57459255_752774f1b2.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/576452297_897023f002.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/586474709_ae436da045.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/590318879_68cf112861.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/59798110_2b6a3c8031.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/603709866_a97c7cfc72.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/603711658_4c8cd2201e.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/65038344_52a45d090d.jpg  \n",
            " extracting: ants-bees-dataset-master/val/bees/6a00d8341c630a53ef00e553d0beb18834-800wi.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/72100438_73de9f17af.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/759745145_e8bc776ec8.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/936182217_c4caa5222d.jpg  \n",
            "  inflating: ants-bees-dataset-master/val/bees/abeja.jpg  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.rename(\"/content/data/ants-bees-dataset-master\", \"/content/data/hymenoptera_data\")"
      ],
      "metadata": {
        "id": "rHwtzqkLuNJA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.remove(\"/content/data/master.zip\")"
      ],
      "metadata": {
        "id": "KG5sw1hcuwi0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JKbeTthpuzr9",
        "outputId": "07def0ba-d2c9-452a-d459-e15978471ad7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Device configuration\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "mean = np.array([0.485, 0.456, 0.406])\n",
        "std = np.array([0.229, 0.224, 0.225])\n",
        "\n",
        "data_transforms = {\n",
        "    'train': transforms.Compose([\n",
        "        transforms.RandomResizedCrop(224),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize(mean,std)\n",
        "    ]),\n",
        "    'val': transforms.Compose([\n",
        "        transforms.Resize(256),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize(mean,std)\n",
        "    ]),\n",
        "}"
      ],
      "metadata": {
        "id": "8QyMULikpc7G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = 'data/hymenoptera_data'\n",
        "sets = ['train', 'val']\n",
        "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']}\n",
        "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=0) for x in ['train', 'val']}"
      ],
      "metadata": {
        "id": "SSighiHtqhKH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
        "class_names = image_datasets['train'].classes\n",
        "print(dataset_sizes)\n",
        "print(class_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kh-C8-UHvHdn",
        "outputId": "7389d2c9-0710-4437-a06a-83b524d86db8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'train': 244, 'val': 153}\n",
            "['ants', 'bees']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Training Loop:"
      ],
      "metadata": {
        "id": "bC-JIfwhv3z-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###train() sets the modules in the network in training mode. It tells our model that we are currently in the training phase so the model keeps some layers, like dropout, batch-normalization which behaves differently depends on the current phase, active. whereas the model. eval() does the opposite."
      ],
      "metadata": {
        "id": "PR7iM15mwlwd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###A deep copy creates a new compound object before inserting copies of the items found in the original into it in a recursive manner. It means first constructing a new collection object and then recursively populating it with copies of the child objects found in the original. In the case of deep copy, a copy of the object is copied into another object. It means that any changes made to a copy of the object do not reflect in the original object. "
      ],
      "metadata": {
        "id": "AupCdbC61SvU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Deep Copy.](https://media.geeksforgeeks.org/wp-content/uploads/deep-copy.jpg)\n"
      ],
      "metadata": {
        "id": "F3bWOVW31I63"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###A shallow copy creates a new compound object and then references the objects contained in the original within it, which means it constructs a new collection object and then populates it with references to the child objects found in the original. The copying process does not recurse and therefore won’t create copies of the child objects themselves. In the case of shallow copy, a reference of an object is copied into another object. It means that any changes made to a copy of an object do reflect in the original object. In python, this is implemented using the “copy()” function. "
      ],
      "metadata": {
        "id": "LXZ7qyUU1UHR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Shallow Copy.](https://media.geeksforgeeks.org/wp-content/uploads/shallow-copy.jpg)\n"
      ],
      "metadata": {
        "id": "xVTmTyaG1Bku"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
        "    since = time.time()\n",
        "\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
        "        print('-' * 10)\n",
        "\n",
        "        # Each epoch has a training and validation phase\n",
        "        for phase in ['train', 'val']:\n",
        "            if phase == 'train':\n",
        "                model.train()  # Set model to training mode\n",
        "            else:\n",
        "                model.eval()   # Set model to evaluate mode\n",
        "\n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "\n",
        "            # Iterate over data.\n",
        "            for inputs, labels in dataloaders[phase]:\n",
        "                inputs = inputs.to(device)\n",
        "                labels = labels.to(device)\n",
        "\n",
        "                # forward\n",
        "                # track history if only in train\n",
        "                with torch.set_grad_enabled(phase == 'train'):\n",
        "                    outputs = model(inputs)\n",
        "                    _, preds = torch.max(outputs, 1)\n",
        "                    loss = criterion(outputs, labels)\n",
        "\n",
        "                    # backward + optimize only if in training phase\n",
        "                    if phase == 'train':\n",
        "                        optimizer.zero_grad()\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "\n",
        "                # statistics\n",
        "                running_loss += loss.item() * inputs.size(0)\n",
        "                running_corrects += torch.sum(preds == labels.data)\n",
        "\n",
        "            if phase == 'train':\n",
        "                scheduler.step() #scheduler is used to change the learning rate during training\n",
        "\n",
        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
        "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
        "\n",
        "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
        "                phase, epoch_loss, epoch_acc))\n",
        "\n",
        "            # deep copy the model\n",
        "            if phase == 'val' and epoch_acc > best_acc:\n",
        "                best_acc = epoch_acc\n",
        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
        "\n",
        "        print()\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
        "        time_elapsed // 60, time_elapsed % 60))\n",
        "    print('Best val Acc: {:4f}'.format(best_acc))\n",
        "\n",
        "    # load best model weights\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    return model"
      ],
      "metadata": {
        "id": "vefZVsV4vuxO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Loading Pretrained Model & Changing Last Layer:"
      ],
      "metadata": {
        "id": "j4h-2IW44Rnw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Fine-tuning:"
      ],
      "metadata": {
        "id": "l9It-Rlk9Qt4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = models.resnet18(pretrained = True)\n",
        "# We will replace the number of features in the last layer\n",
        "num_ftrs = model.fc.in_features\n",
        "print(num_ftrs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173,
          "referenced_widgets": [
            "aa4360b92c334e75bddec15286d69e07",
            "5426abf42e4146dcacb99de152816cca",
            "367bffd1c8e3495ca21fa0480a834c3c",
            "8475ac97e4bf46f381c2f73713b38914",
            "7c1c421f06aa4bbda720c130dcd351a1",
            "e38d67ade63144a3b3f3d92803eddb12",
            "834b94e1b7d7481cb71e0f938c665f5b",
            "547c3607f68e4d51bde93ec2212047a6",
            "3eb089fb543e4de595d647153c2dc2da",
            "1364723861f346a9b46ce056cb38f516",
            "2a4b1c752f9d4c0ba9b65e6b23a0d0c0"
          ]
        },
        "id": "bBHdfvpv4NmH",
        "outputId": "8b04e438-e12e-4157-8821-f8b82f654257"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/44.7M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aa4360b92c334e75bddec15286d69e07"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new fully connected layer instead of the last layer\n",
        "model.fc = nn.Linear(num_ftrs, 2)\n",
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IqnbjXwM5ZbM",
        "outputId": "0721ebec-4a31-428d-a9f2-4b1ed4bbf5c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ResNet(\n",
              "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (relu): ReLU(inplace=True)\n",
              "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "  (layer1): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer2): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer3): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer4): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (fc): Linear(in_features=512, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Loss & Optimizer:"
      ],
      "metadata": {
        "id": "6KOxLW_T7SBe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.001)\n",
        "\n",
        "# Scheduler\n",
        "\n",
        "step_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size = 7, gamma = 0.1) # Every 7 epochs, out lr is multiplied by 0.1"
      ],
      "metadata": {
        "id": "fmF0zJo27PHb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = train_model(model, criterion, optimizer, step_lr_scheduler, num_epochs = 4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Rb6uLGi8VMx",
        "outputId": "ef7ba87e-c5ad-440c-e562-3a9e2828fd13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0/3\n",
            "----------\n",
            "train Loss: 0.6482 Acc: 0.6270\n",
            "val Loss: 0.5344 Acc: 0.7647\n",
            "\n",
            "Epoch 1/3\n",
            "----------\n",
            "train Loss: 0.5515 Acc: 0.7295\n",
            "val Loss: 0.3891 Acc: 0.8889\n",
            "\n",
            "Epoch 2/3\n",
            "----------\n",
            "train Loss: 0.5056 Acc: 0.7377\n",
            "val Loss: 0.3279 Acc: 0.9216\n",
            "\n",
            "Epoch 3/3\n",
            "----------\n",
            "train Loss: 0.3958 Acc: 0.8443\n",
            "val Loss: 0.2839 Acc: 0.8954\n",
            "\n",
            "Training complete in 5m 51s\n",
            "Best val Acc: 0.921569\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Transfer Learning: (will be faster than fine-tuning)"
      ],
      "metadata": {
        "id": "7Gqh1Lf_8Tad"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = models.resnet18(pretrained = True)\n",
        "# Freeze all the layers to prevent changing the weights while training\n",
        "for param in model.parameters():\n",
        "  param.requires_grad = False\n",
        "# We will replace the number of features in the last layer\n",
        "num_ftrs = model.fc.in_features\n",
        "print(num_ftrs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VNKMyuql9h3L",
        "outputId": "7245e9a6-1eb4-4b47-ad1c-0d7eeba0efd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This new layer has by param.requires_grad = True by default\n",
        "model.fc = nn.Linear(num_ftrs, 2)\n",
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KlV05G-B-Itg",
        "outputId": "10d1b13f-5918-493b-b344-8553a214f559"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ResNet(\n",
              "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (relu): ReLU(inplace=True)\n",
              "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "  (layer1): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer2): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer3): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer4): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (fc): Linear(in_features=512, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.001)\n",
        "\n",
        "# Scheduler\n",
        "\n",
        "step_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size = 7, gamma = 0.1) # Every 7 epochs, out lr is multiplied by 0.1"
      ],
      "metadata": {
        "id": "CXLw4MPG9jbf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = train_model(model, criterion, optimizer, step_lr_scheduler, num_epochs = 4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-MQAaaC9kb9",
        "outputId": "842fb9c5-cd14-4fd5-a3f7-eb31c665f373"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0/3\n",
            "----------\n",
            "train Loss: 0.7012 Acc: 0.5656\n",
            "val Loss: 0.5657 Acc: 0.7451\n",
            "\n",
            "Epoch 1/3\n",
            "----------\n",
            "train Loss: 0.6220 Acc: 0.6475\n",
            "val Loss: 0.4737 Acc: 0.7843\n",
            "\n",
            "Epoch 2/3\n",
            "----------\n",
            "train Loss: 0.5782 Acc: 0.7008\n",
            "val Loss: 0.3743 Acc: 0.8758\n",
            "\n",
            "Epoch 3/3\n",
            "----------\n",
            "train Loss: 0.4821 Acc: 0.7869\n",
            "val Loss: 0.3222 Acc: 0.9346\n",
            "\n",
            "Training complete in 2m 47s\n",
            "Best val Acc: 0.934641\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Saving & Loading Models:"
      ],
      "metadata": {
        "id": "aMOaGvlFqCWz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn"
      ],
      "metadata": {
        "id": "bAGt7xyd-6AJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Model(nn.Module):\n",
        "  def __init__(self, n_input_features):\n",
        "    super(Model, self).__init__()\n",
        "    self.linear = nn.Linear(n_input_features, 1)\n",
        "  def forward(self, x):\n",
        "    y_pred = torch.sigmoid(self.linear(x))\n",
        "    return y_pred"
      ],
      "metadata": {
        "id": "IuxWkENurE8v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(6)"
      ],
      "metadata": {
        "id": "J_31DlcEyr_A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Lazy Method:"
      ],
      "metadata": {
        "id": "6ts5hVZm3Erz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "File = \"model.pth\"\n",
        "torch.save(model,File) #Saves a dictionary"
      ],
      "metadata": {
        "id": "mDZGCZU7qRcM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = torch.load(File)\n",
        "model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rLgjANNKqXTV",
        "outputId": "7034f3e2-8f0a-466d-8b6c-205f3bfad75e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Model(\n",
              "  (linear): Linear(in_features=6, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for param in model.parameters():\n",
        "  print(param)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AiAVA1ig3hNX",
        "outputId": "035a95d7-0261-49aa-90b0-a0a97751a52a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Parameter containing:\n",
            "tensor([[ 0.0204,  0.1016,  0.1457, -0.1265,  0.2453,  0.1908]],\n",
            "       requires_grad=True)\n",
            "Parameter containing:\n",
            "tensor([-0.1675], requires_grad=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Recommended Method:"
      ],
      "metadata": {
        "id": "yN0JRVdl3ptn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(), File) #Saves a dictionary"
      ],
      "metadata": {
        "id": "5aE0QQ0lqXW1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model = Model(6)\n",
        "loaded_model.load_state_dict(torch.load(File))\n",
        "loaded_model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nWiSCArB315D",
        "outputId": "6e7295c3-eaa7-4325-a1f1-5dafd5ac1e19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Model(\n",
              "  (linear): Linear(in_features=6, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for param in loaded_model.parameters():\n",
        "  print(param)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DzEYprFo4QYl",
        "outputId": "69eabf83-fb8e-4ba8-c12b-bf8dca6073c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Parameter containing:\n",
            "tensor([[ 0.0204,  0.1016,  0.1457, -0.1265,  0.2453,  0.1908]],\n",
            "       requires_grad=True)\n",
            "Parameter containing:\n",
            "tensor([-0.1675], requires_grad=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Examining the state_dict:"
      ],
      "metadata": {
        "id": "rEb82FVE4nSA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.state_dict())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KCLvJW924UAr",
        "outputId": "0dbc346d-b965-44ba-ba00-85262448dccf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "OrderedDict([('linear.weight', tensor([[ 0.0204,  0.1016,  0.1457, -0.1265,  0.2453,  0.1908]])), ('linear.bias', tensor([-0.1675]))])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Save a checkpoint during training:"
      ],
      "metadata": {
        "id": "ibJYnYMZ5Enz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "learning_rate = 0.01\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)\n",
        "print(optimizer.state_dict())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fhePtae349yZ",
        "outputId": "1a5ed278-ac97-4c08-e184-f52119b79141"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'state': {}, 'param_groups': [{'lr': 0.01, 'momentum': 0, 'dampening': 0, 'weight_decay': 0, 'nesterov': False, 'maximize': False, 'foreach': None, 'differentiable': False, 'params': [0, 1]}]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "checkpoint = {\n",
        "    \"epoch\": 90,\n",
        "    \"model_state\": model.state_dict(),\n",
        "    \"optim_state\": optimizer.state_dict()\n",
        "}\n",
        "torch.save(checkpoint, \"checkpoint.pth\")"
      ],
      "metadata": {
        "id": "-8-XNiqp5RQp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_checkpoint = torch.load(\"checkpoint.pth\")"
      ],
      "metadata": {
        "id": "meGVB4cM51uF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epoch = loaded_checkpoint[\"epoch\"]\n",
        "model = Model(6)\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr = 0) #Even with lr = 0, when we load it we will get the lr that was saved before\n",
        "model.load_state_dict(checkpoint[\"model_state\"])\n",
        "optimizer.load_state_dict(checkpoint[\"optim_state\"])"
      ],
      "metadata": {
        "id": "NZIc1LHY6ENt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(optimizer.state_dict())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BKZlr8r561qe",
        "outputId": "5571e064-092e-4442-a7f9-d4bcb3f36b2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'state': {}, 'param_groups': [{'lr': 0.01, 'momentum': 0, 'dampening': 0, 'weight_decay': 0, 'nesterov': False, 'maximize': False, 'foreach': None, 'differentiable': False, 'params': [0, 1]}]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Saving on GPU & Loading on CPU"
      ],
      "metadata": {
        "id": "bkD96X8A7N8w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\")\n",
        "model.to(device)\n",
        "torch.save(model.state_dict(),File)"
      ],
      "metadata": {
        "id": "5oFNnllZ64Kj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cpu\")\n",
        "model = Model(6)\n",
        "model.load_state_dict(torch.load(File, map_location = device))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIMGVrsH7gQB",
        "outputId": "345b0cf1-acbd-4397-c992-09ab71fc0d60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Saving on GPU & Loading on GPU"
      ],
      "metadata": {
        "id": "0dhOyzjP8QYj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\")\n",
        "model.to(device)\n",
        "torch.save(model.state_dict(),File)"
      ],
      "metadata": {
        "id": "jkipB7vg8QYk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(6)\n",
        "model.load_state_dict(torch.load(File))\n",
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2784ce6-6fc7-49e8-dfb6-f32477328a81",
        "id": "MHPnBRDW8QYk"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Model(\n",
              "  (linear): Linear(in_features=6, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Saving on CPU & Loading on GPU"
      ],
      "metadata": {
        "id": "tFlCw_il8hJA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(),File)"
      ],
      "metadata": {
        "id": "uTrRLSMt8hJA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\")\n",
        "model = Model(6)\n",
        "model.load_state_dict(torch.load(File, map_location = \"cuda:0\")) #Any GPU device number that I want\n",
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d973180e-9d8b-4d59-8e04-b0297aa06402",
        "id": "e9g9t0Ja8hJA"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Model(\n",
              "  (linear): Linear(in_features=6, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Recurrent Neural Network:"
      ],
      "metadata": {
        "id": "G0VNBtEW7WHl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###In this section, we will use a recurrent neural net to classify names (say which country this name belongs to)."
      ],
      "metadata": {
        "id": "456n61TI_uWQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Downloading Names Dataset:\n",
        "\n"
      ],
      "metadata": {
        "id": "Be_EYKci8gR1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://download.pytorch.org/tutorial/data.zip"
      ],
      "metadata": {
        "id": "LHOjXXsz8QYk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d81a7d2-d4af-4ec1-b967-e1aba39dc0f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-03-25 18:02:22--  https://download.pytorch.org/tutorial/data.zip\n",
            "Resolving download.pytorch.org (download.pytorch.org)... 52.222.139.90, 52.222.139.21, 52.222.139.109, ...\n",
            "Connecting to download.pytorch.org (download.pytorch.org)|52.222.139.90|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2882130 (2.7M) [application/zip]\n",
            "Saving to: ‘data.zip’\n",
            "\n",
            "data.zip            100%[===================>]   2.75M  4.34MB/s    in 0.6s    \n",
            "\n",
            "2023-03-25 18:02:23 (4.34 MB/s) - ‘data.zip’ saved [2882130/2882130]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os"
      ],
      "metadata": {
        "id": "ktcFLVrH_LUL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip data.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kHErlRvU8fY3",
        "outputId": "6a3bf8e6-39d3-4f90-9cd9-ff18306f57ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  data.zip\n",
            "   creating: data/\n",
            "  inflating: data/eng-fra.txt        \n",
            "   creating: data/names/\n",
            "  inflating: data/names/Arabic.txt   \n",
            "  inflating: data/names/Chinese.txt  \n",
            "  inflating: data/names/Czech.txt    \n",
            "  inflating: data/names/Dutch.txt    \n",
            "  inflating: data/names/English.txt  \n",
            "  inflating: data/names/French.txt   \n",
            "  inflating: data/names/German.txt   \n",
            "  inflating: data/names/Greek.txt    \n",
            "  inflating: data/names/Irish.txt    \n",
            "  inflating: data/names/Italian.txt  \n",
            "  inflating: data/names/Japanese.txt  \n",
            "  inflating: data/names/Korean.txt   \n",
            "  inflating: data/names/Polish.txt   \n",
            "  inflating: data/names/Portuguese.txt  \n",
            "  inflating: data/names/Russian.txt  \n",
            "  inflating: data/names/Scottish.txt  \n",
            "  inflating: data/names/Spanish.txt  \n",
            "  inflating: data/names/Vietnamese.txt  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.remove(\"data.zip\")"
      ],
      "metadata": {
        "id": "Sr_CINp2_NPu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "import unicodedata\n",
        "import string\n",
        "import glob\n",
        "import torch\n",
        "import random"
      ],
      "metadata": {
        "id": "s4F6yayK_W4j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Utilities:"
      ],
      "metadata": {
        "id": "g6yXn0ApbIHS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# alphabet small + capital letters + \" .,;'\"\n",
        "ALL_LETTERS = string.ascii_letters + \" .,;'\"\n",
        "N_LETTERS = len(ALL_LETTERS)"
      ],
      "metadata": {
        "id": "0CB6PXatbKIU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(ALL_LETTERS)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vEZ_UEFYcq5o",
        "outputId": "994d038a-2fa1-4715-9357-9b3468c44d6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ .,;'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn a Unicode string to plain ASCII, thanks to https://stackoverflow.com/a/518232/2809427\n",
        "# Removes any special characters and keeps only ASCII\n",
        "def unicode_to_ascii(s):\n",
        "    return ''.join(\n",
        "        c for c in unicodedata.normalize('NFD', s)\n",
        "        if unicodedata.category(c) != 'Mn'\n",
        "        and c in ALL_LETTERS\n",
        "    )"
      ],
      "metadata": {
        "id": "zYsv5K__bKns"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(unicode_to_ascii('Ślusàrski'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-PzvUS9Kco0p",
        "outputId": "f713e053-56a3-4793-bbb3-0c2d447aad75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Slusarski\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def load_data():\n",
        "    # Build the category_lines dictionary, a list of names per language\n",
        "    category_lines = {}\n",
        "    all_categories = []\n",
        "    \n",
        "    def find_files(path):\n",
        "        return glob.glob(path)\n",
        "    \n",
        "    # Read a file and split into lines\n",
        "    def read_lines(filename):\n",
        "        lines = io.open(filename, encoding='utf-8').read().strip().split('\\n')\n",
        "        return [unicode_to_ascii(line) for line in lines]\n",
        "    \n",
        "    for filename in find_files('data/names/*.txt'):\n",
        "        category = os.path.splitext(os.path.basename(filename))[0]\n",
        "        all_categories.append(category)\n",
        "        \n",
        "        lines = read_lines(filename)\n",
        "        category_lines[category] = lines\n",
        "        \n",
        "    return category_lines, all_categories"
      ],
      "metadata": {
        "id": "ulibn6WJbKp7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "category_lines, all_categories = load_data()\n",
        "print(category_lines['Italian'][:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZoP_RbIid22S",
        "outputId": "1e5a4a79-f114-4eea-ecb5-551f823e5d5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Abandonato', 'Abatangelo', 'Abatantuono', 'Abate', 'Abategiovanni']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To represent a single letter, we use a “one-hot vector” of \n",
        "size (1 x n_letters). \n",
        "\n",
        "A one-hot vector is filled with 0s\n",
        "except for a 1 at index of the current letter, e.g. \"b\" = (0 1 0 0 0 ...).\n",
        "\n",
        "To make a word we join a bunch of those into a\n",
        "2D matrix (line_length x 1 x n_letters). We say line_length because each line is a single word.\n",
        "\n",
        "\n",
        "That extra 1 dimension is because PyTorch assumes\n",
        "everything is in batches - we’re just using a batch size of 1 here."
      ],
      "metadata": {
        "id": "sF_S45oHc5QL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Find letter index from all_letters, e.g. \"a\" = 0\n",
        "def letter_to_index(letter):\n",
        "    return ALL_LETTERS.find(letter)"
      ],
      "metadata": {
        "id": "1No78jFNbKtc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Just for demonstration, turn a letter into a <1 x n_letters> Tensor\n",
        "def letter_to_tensor(letter):\n",
        "    tensor = torch.zeros(1, N_LETTERS)\n",
        "    tensor[0][letter_to_index(letter)] = 1\n",
        "    return tensor"
      ],
      "metadata": {
        "id": "oudlCzDrdZFJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(letter_to_tensor('J'))\n",
        "print(letter_to_tensor('J').size())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yR8GD45bd65J",
        "outputId": "9ae7d2ab-d89f-4bb1-e593-00636b704b9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
            "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
            "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
            "         0., 0., 0.]])\n",
            "torch.Size([1, 57])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn a line into a <line_length x 1 x n_letters>, or an array of one-hot letter vectors\n",
        "def line_to_tensor(line):\n",
        "    tensor = torch.zeros(len(line), 1, N_LETTERS)\n",
        "    for i, letter in enumerate(line):\n",
        "        tensor[i][0][letter_to_index(letter)] = 1\n",
        "    return tensor"
      ],
      "metadata": {
        "id": "CmRRrxuNdZHM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(line_to_tensor('Jones'))\n",
        "print(line_to_tensor('Jones').size())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Fv07lhWeAuO",
        "outputId": "787eeb0b-695d-493b-e46a-1022d03cdb96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
            "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
            "          0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
            "          0., 0., 0., 0., 0., 0.]],\n",
            "\n",
            "        [[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
            "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
            "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
            "          0., 0., 0., 0., 0., 0.]],\n",
            "\n",
            "        [[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
            "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
            "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
            "          0., 0., 0., 0., 0., 0.]],\n",
            "\n",
            "        [[0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
            "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
            "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
            "          0., 0., 0., 0., 0., 0.]],\n",
            "\n",
            "        [[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
            "          0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
            "          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
            "          0., 0., 0., 0., 0., 0.]]])\n",
            "torch.Size([5, 1, 57])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def random_training_example(category_lines, all_categories):\n",
        "    \n",
        "    def random_choice(a):\n",
        "        random_idx = random.randint(0, len(a) - 1)\n",
        "        return a[random_idx]\n",
        "    \n",
        "    category = random_choice(all_categories)\n",
        "    line = random_choice(category_lines[category])\n",
        "    category_tensor = torch.tensor([all_categories.index(category)], dtype=torch.long)\n",
        "    line_tensor = line_to_tensor(line)\n",
        "    return category, line, category_tensor, line_tensor"
      ],
      "metadata": {
        "id": "9jMx9G5-dZKz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Architecture:"
      ],
      "metadata": {
        "id": "pfpGeKqRe7BC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![RNN.](https://i.imgur.com/Z2xbySO.png)\n"
      ],
      "metadata": {
        "id": "TNVWqJgwhRip"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import matplotlib.pyplot as plt\n",
        "# If the utilities were in a different folder we would import them like this:\n",
        "# from utils import ALL_LETTERS, N_LETTERS\n",
        "# from utils import load_data, letter_to_tensor, line_to_tensor, random_training_example"
      ],
      "metadata": {
        "id": "FGZBfmQOe6WH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# There is already a built-in RNN class which we will use later\n",
        "class RNN(nn.Module):\n",
        "  def __init__(self, input_size, hidden_size, output_size):\n",
        "    super(RNN, self).__init__()\n",
        "\n",
        "    self.hidden_size = hidden_size\n",
        "    self.input_to_hidden = nn.Linear(input_size + hidden_size, hidden_size)\n",
        "    self.input_to_output = nn.Linear(input_size + hidden_size, output_size)\n",
        "    self.softmax = nn.LogSoftmax(dim = 1) # Our input is of shape 1=(1,57), we only need the second dimension (57), So, we specify that we want the second dimension as input to the softmax layer\n",
        "\n",
        "  def forward(self, input_tensor, hidden_tensor):\n",
        "    combined = torch.cat((input_tensor, hidden_tensor), 1) # We combined our two tensors along dimension 1\n",
        "        \n",
        "    hidden = self.input_to_hidden(combined)\n",
        "    output = self.input_to_output(combined)\n",
        "    output = self.softmax(output)\n",
        "    return output, hidden\n",
        "  \n",
        "  def initial_hidden(self):\n",
        "        return torch.zeros(1, self.hidden_size)\n"
      ],
      "metadata": {
        "id": "yxr8HRASfxXa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "category_lines, all_categories = load_data()\n",
        "n_categories = len(all_categories)"
      ],
      "metadata": {
        "id": "pFvY5rC78pQr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_hidden = 128 # Hyper parameter\n",
        "rnn = RNN(N_LETTERS, n_hidden, n_categories)"
      ],
      "metadata": {
        "id": "qKyI9fP281fJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# one step\n",
        "input_tensor = letter_to_tensor('A')\n",
        "hidden_tensor = rnn.initial_hidden()"
      ],
      "metadata": {
        "id": "0dzZhZAu84I6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output, next_hidden = rnn(input_tensor, hidden_tensor)\n",
        "print(output.size())\n",
        "print(next_hidden.size())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUlifXEk84LX",
        "outputId": "ce0b9fc6-1764-429d-d0ca-921ce9f847ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 18])\n",
            "torch.Size([1, 128])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# whole sequence/name\n",
        "input_tensor = line_to_tensor('Albert')\n",
        "hidden_tensor = rnn.initial_hidden()"
      ],
      "metadata": {
        "id": "vG2_mzA484N3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output, next_hidden = rnn(input_tensor[0], hidden_tensor) # input_tensor[0] is the first letter\n",
        "print(output.size())\n",
        "print(next_hidden.size())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oyo_mAT584RK",
        "outputId": "2e246645-8d0f-417e-dc20-e1d2b702a77c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 18])\n",
            "torch.Size([1, 128])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The output of the softmax will be the likelihood of each category, so, we choose the biggest number\n",
        "def category_from_output(output):\n",
        "    category_idx = torch.argmax(output).item()\n",
        "    return all_categories[category_idx]"
      ],
      "metadata": {
        "id": "PEpoJfuqAqaC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(category_from_output(output))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bdJF2yvHA2JJ",
        "outputId": "4954d7e8-c54c-4a9d-d006-ffa4eedc1259"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Greek\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Training:"
      ],
      "metadata": {
        "id": "3kW9DlcYBcGO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.NLLLoss() # Negative log likelihood loss\n",
        "learning_rate = 0.005\n",
        "optimizer = torch.optim.SGD(rnn.parameters(), lr=learning_rate)"
      ],
      "metadata": {
        "id": "7hXNxknABKwz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# category_tensor is the actual class label\n",
        "def train(line_tensor, category_tensor):\n",
        "    hidden = rnn.initial_hidden()\n",
        "    \n",
        "    for i in range(line_tensor.size()[0]):\n",
        "        output, hidden = rnn(line_tensor[i], hidden) # We do this for the whole name and after we finish with the last letter, we calculate the loss\n",
        "        \n",
        "    loss = criterion(output, category_tensor)\n",
        "    \n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    \n",
        "    return output, loss.item()"
      ],
      "metadata": {
        "id": "2krGAaZtBi8M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "current_loss = 0\n",
        "all_losses = []\n",
        "plot_steps, print_steps = 1000, 5000\n",
        "n_iters = 100000"
      ],
      "metadata": {
        "id": "YfDT6GUBCfYf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(n_iters):\n",
        "    category, line, category_tensor, line_tensor = random_training_example(category_lines, all_categories)\n",
        "    \n",
        "    output, loss = train(line_tensor, category_tensor)\n",
        "    current_loss += loss \n",
        "    \n",
        "    if (i+1) % plot_steps == 0:\n",
        "        all_losses.append(current_loss / plot_steps)\n",
        "        current_loss = 0\n",
        "        \n",
        "    if (i+1) % print_steps == 0:\n",
        "        guess = category_from_output(output)\n",
        "        correct = \"CORRECT\" if guess == category else f\"WRONG ({category})\"\n",
        "        print(f\"{i+1} {(i+1)/n_iters*100} {loss:.4f} {line} / {guess} {correct}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K10CMHouCqOL",
        "outputId": "51c5f972-6c4b-44e7-9d92-8de7e022f94d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5000 5.0 2.4894 Noh / Chinese WRONG (Korean)\n",
            "10000 10.0 0.5214 Giannakos / Greek CORRECT\n",
            "15000 15.0 2.6884 Daele / Irish WRONG (Dutch)\n",
            "20000 20.0 0.6571 Nakatoni / Japanese CORRECT\n",
            "25000 25.0 2.1837 Hiro / Portuguese WRONG (Japanese)\n",
            "30000 30.0 0.5044 Sotiris / Greek CORRECT\n",
            "35000 35.0 0.8844 Delacroix / French CORRECT\n",
            "40000 40.0 3.5625 Fonseca / Dutch WRONG (Portuguese)\n",
            "45000 45.0 1.3413 Guerra / Portuguese WRONG (Spanish)\n",
            "50000 50.0 0.6482 Ban / Chinese CORRECT\n",
            "55000 55.00000000000001 0.4844 Achteren / Dutch CORRECT\n",
            "60000 60.0 0.3718 Sarraf / Arabic CORRECT\n",
            "65000 65.0 1.3559 Leclair / Scottish WRONG (French)\n",
            "70000 70.0 0.8900 Ortega / Spanish CORRECT\n",
            "75000 75.0 0.1659 Vescovi / Italian CORRECT\n",
            "80000 80.0 2.2688 Bazzi / Italian WRONG (Arabic)\n",
            "85000 85.0 3.2687 Simon / English WRONG (German)\n",
            "90000 90.0 0.2480 Vlahos / Greek CORRECT\n",
            "95000 95.0 1.7333 Nyashin / Japanese WRONG (Russian)\n",
            "100000 100.0 1.2350 Ding / Vietnamese WRONG (Chinese)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.plot(all_losses)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "pu-gL3amDywo",
        "outputId": "d6210760-281a-438a-c737-405afaa5c102"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prediction:"
      ],
      "metadata": {
        "id": "vIV_8wjLD6UH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(input_line):\n",
        "    print(f\"\\n> {input_line}\")\n",
        "    with torch.no_grad():\n",
        "        line_tensor = line_to_tensor(input_line)\n",
        "        \n",
        "        hidden = rnn.initial_hidden()\n",
        "    \n",
        "        for i in range(line_tensor.size()[0]):\n",
        "            output, hidden = rnn(line_tensor[i], hidden)\n",
        "        \n",
        "        guess = category_from_output(output)\n",
        "        print(guess)"
      ],
      "metadata": {
        "id": "gm5Gfwg7DyzI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "while True:\n",
        "    sentence = input(\"Input:\")\n",
        "    if sentence == \"quit\":\n",
        "        break\n",
        "    \n",
        "    predict(sentence)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QRdVzo_3Dy2g",
        "outputId": "2a64f6b6-db4e-42e1-c756-e039f1538e9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input:Nader\n",
            "\n",
            "> Nader\n",
            "Arabic\n",
            "Input:Antipas\n",
            "\n",
            "> Antipas\n",
            "Dutch\n",
            "Input:Aihara\n",
            "\n",
            "> Aihara\n",
            "Japanese\n",
            "Input:Baumann\n",
            "\n",
            "> Baumann\n",
            "Dutch\n",
            "Input:Bäcker\n",
            "\n",
            "> Bäcker\n",
            "German\n",
            "Input:quit\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Built-in RNN, LSTM & GRU:"
      ],
      "metadata": {
        "id": "xuZ2c6W0wepg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###We are going to use image data here to show that even with images as inputs, we must turn them into sequences."
      ],
      "metadata": {
        "id": "ENJoZun95beF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms"
      ],
      "metadata": {
        "id": "yN9YzMGoEgwp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Device configuration\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ],
      "metadata": {
        "id": "8rjltqgYwoSk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyper-parameters \n",
        "# input_size = 784 # 28x28\n",
        "num_classes = 10\n",
        "num_epochs = 2\n",
        "batch_size = 100\n",
        "learning_rate = 0.001\n",
        "\n",
        "# We will look at the image, one row at a time\n",
        "input_size = 28\n",
        "sequence_length = 28\n",
        "hidden_size = 128\n",
        "num_layers = 2 # We will stack two RNNs and the second RNN will take as an input the output of the first RNN"
      ],
      "metadata": {
        "id": "0FoiE8uawoVL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Loading Dataset:"
      ],
      "metadata": {
        "id": "t0eO1dBcwuAC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = torchvision.datasets.MNIST(root='./data', \n",
        "                                           train=True, \n",
        "                                           transform=transforms.ToTensor(),  \n",
        "                                           download=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423,
          "referenced_widgets": [
            "08d865a4ea0747c7abf7c048158f580c",
            "ec4830908e744a62a128cac7ef195de8",
            "0f232b6a34de41d6b8f0faa75f5de4c8",
            "1572a35b2df74fdcae9190f09912ace4",
            "8443cacb665e4acd9804bd15cb18d31b",
            "3ff6c1d530c7472f9b7e028ed8c5e038",
            "3c85f343b59342afadc3ac24f474a61f",
            "b0087a21890748e395678b23bcfc39c5",
            "02311e6ca4a84b1798e8e0acec6afd1b",
            "fad3d5b899594e67b5cecdb23646bd7b",
            "060a3b6497f347ffaa6bbd16863c2429",
            "5fe88dc7e8a7417fa9458abcf57df556",
            "c1621682d2d243e382f04861147e4d1e",
            "a20cdb51e8af439eab1d7fde447b6183",
            "d5009fea670944798bba45167034be15",
            "ee42c3b32a67474e8f39e99d73cad437",
            "ee0be4052b98477686ad8ba7c04137f1",
            "7d9ee5f86db147ed93305d1f177b0b4c",
            "665633575b57488d89ee0f4b5dcc5c41",
            "76cb84ce4c104bbfa9549f9a9994f519",
            "d40a8fac5dd54e56b9848dda9935ab3c",
            "9343be0105b34df7a12d4dc815e8d1a9",
            "5d1f06fb5e494305a594af7503e3eee8",
            "bd53c0da47454c55a4f32500d2fe629d",
            "5791fc73cd3e49dc827b81c45c0bb803",
            "fc24457e1ecb498c9617d1fcc72e137a",
            "b36aead2966f4dfab7e6a7a82afcef30",
            "5cb57618175c4ccf8951dfca37f8053f",
            "893c9dba278b453292a2bc643ecc8944",
            "23d52ca97fcf4df4b104060ace68c816",
            "81529396a8494b488098cde54dae7306",
            "8a75975a92184870b0644157e608fe2a",
            "f962b3d000394a30b336a56b531ef095",
            "d6cf83ce5c4d49159121e3208fc8a713",
            "ff32272603fa4b14b2636e5fd2d9c241",
            "01013f13409344b7ab7341454ce6c795",
            "3058887d3b6343709f546b806e6f4378",
            "ee11f0b4645e405bab530bf5b9217ce2",
            "659966169e0641728d6f752b023ae825",
            "d9ff6ab6b0e34a07b77f42e9fd3aabc5",
            "13c7fc7aab5f4a8fa79200b176c147c0",
            "7b1a91615eb94c36a7ed9d84a39943fa",
            "a79f19edf0874d138e75095b0e27a122",
            "274500131a7d4b87bb93627dec644f8a"
          ]
        },
        "id": "Nt4F36TuwoXw",
        "outputId": "709e33e3-76b8-4f5a-c575-d61dabe6e64e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
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        },
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = torchvision.datasets.MNIST(root='./data', \n",
        "                                          train=False, \n",
        "                                          transform=transforms.ToTensor())"
      ],
      "metadata": {
        "id": "f7vPw_MGwobV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
        "                                           batch_size=batch_size, \n",
        "                                           shuffle=True)"
      ],
      "metadata": {
        "id": "mwGCHtJlwz4B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_loader = torch.utils.data.DataLoader(dataset=test_dataset, \n",
        "                                          batch_size=batch_size, \n",
        "                                          shuffle=False)"
      ],
      "metadata": {
        "id": "291Ws_L6wz6J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Model Architecture:"
      ],
      "metadata": {
        "id": "5XvbnWFEw6Gb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class RNN(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, num_layers, num_classes):\n",
        "        super(RNN, self).__init__()\n",
        "        self.num_layers = num_layers\n",
        "        self.hidden_size = hidden_size\n",
        "        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True) # batch_first = True means we must have batch as first dimension\n",
        "        # So, our input needs to have the shape: (batch_size, sequence_length, input_size)\n",
        "        \n",
        "        # or:\n",
        "        #self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True)\n",
        "        #self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n",
        "\n",
        "        # We only need the last hidden_size as input to our linear layer\n",
        "        self.fc = nn.Linear(hidden_size, num_classes)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        # Set initial hidden states (and cell states for LSTM)\n",
        "        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) \n",
        "        #c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) \n",
        "        \n",
        "        # x: (n, 28, 28), h0: (2, n, 128)\n",
        "        \n",
        "        # Forward propagate RNN\n",
        "        # The other output is the hidden state for step n which we don't need\n",
        "        out, _ = self.rnn(x, h0)  \n",
        "        # or:\n",
        "        #out, _ = self.lstm(x, (h0,c0))  \n",
        "        \n",
        "        # out: tensor of shape (batch_size, seq_length, hidden_size)\n",
        "        # out: (n, 28, 128)\n",
        "        \n",
        "        # Decode the hidden state of the last time step\n",
        "        out = out[:, -1, :]\n",
        "        # out: (n, 128)\n",
        "         \n",
        "        out = self.fc(out)\n",
        "        # out: (n, 10)\n",
        "        return out"
      ],
      "metadata": {
        "id": "2Jc1hAQJwz8s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)"
      ],
      "metadata": {
        "id": "216IH8_Cw0AM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  "
      ],
      "metadata": {
        "id": "hZEGsWYoxACu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Training:"
      ],
      "metadata": {
        "id": "iOCJHO7xxCNb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_total_steps = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(train_loader):  \n",
        "        # origin shape: [N, 1, 28, 28]\n",
        "        # we want it to be resized: [N, 28, 28]\n",
        "        images = images.reshape(-1, sequence_length, input_size).to(device)\n",
        "        labels = labels.to(device)\n",
        "        \n",
        "        # Forward pass\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "        \n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        if (i+1) % 100 == 0:\n",
        "            print (f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PujmPnICxAFG",
        "outputId": "d302c824-7c4c-41ae-eb03-44f3e65cbce7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/2], Step [100/600], Loss: 0.8669\n",
            "Epoch [1/2], Step [200/600], Loss: 0.7431\n",
            "Epoch [1/2], Step [300/600], Loss: 0.6026\n",
            "Epoch [1/2], Step [400/600], Loss: 0.5060\n",
            "Epoch [1/2], Step [500/600], Loss: 0.4642\n",
            "Epoch [1/2], Step [600/600], Loss: 0.3704\n",
            "Epoch [2/2], Step [100/600], Loss: 0.4183\n",
            "Epoch [2/2], Step [200/600], Loss: 0.3961\n",
            "Epoch [2/2], Step [300/600], Loss: 0.3605\n",
            "Epoch [2/2], Step [400/600], Loss: 0.1822\n",
            "Epoch [2/2], Step [500/600], Loss: 0.2809\n",
            "Epoch [2/2], Step [600/600], Loss: 0.2397\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Testing:"
      ],
      "metadata": {
        "id": "6O-BSQWr5Rui"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# In test phase, we don't need to compute gradients (for memory efficiency)\n",
        "with torch.no_grad():\n",
        "    n_correct = 0\n",
        "    n_samples = 0\n",
        "    for images, labels in test_loader:\n",
        "        images = images.reshape(-1, sequence_length, input_size).to(device)\n",
        "        labels = labels.to(device)\n",
        "        outputs = model(images)\n",
        "        # max returns (value ,index)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        n_samples += labels.size(0)\n",
        "        n_correct += (predicted == labels).sum().item()\n",
        "\n",
        "    acc = 100.0 * n_correct / n_samples\n",
        "    print(f'Accuracy of the network on the 10000 test images: {acc} %')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "slgO6TzdxAHk",
        "outputId": "68167838-478e-43ea-813b-684432c42f3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the network on the 10000 test images: 92.8 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8cbd6GVjxALF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#PyTorch Lightning:"
      ],
      "metadata": {
        "id": "jE_AvYVVwEzf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## What PyTorch Lightning offers:"
      ],
      "metadata": {
        "id": "U2kOETkzwMjC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1- Setting model to training or evaluation:\n",
        "\n",
        "model.train()\n",
        "model.eval()\n",
        "\n",
        "2- No need to set model to device: (easily turn on/off GPU/TPU support)\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model.to(device)\n",
        "\n",
        "3- No need to care about zero_grad() or backward or step:\n",
        "\n",
        "optimizer.zero_grad()\n",
        "loss.backward()\n",
        "optimizer.step()\n",
        "\n",
        "4- We don't need to worry about using no_grad() or detach:\n",
        "\n",
        "with torch.no_grad()\n",
        "...\n",
        "\n",
        "x = x.detach()\n",
        "\n",
        "5- Tensorboard support\n",
        "6- Printing hints"
      ],
      "metadata": {
        "id": "zMA3aH3uxwsq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pytorch-lightning"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rMvKUkt3ycHP",
        "outputId": "9a79637f-2175-4aac-afd1-162c2660e46e"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Installing collected packages: multidict, lightning-utilities, frozenlist, async-timeout, yarl, torchmetrics, aiosignal, aiohttp, pytorch-lightning\n",
            "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 frozenlist-1.3.3 lightning-utilities-0.8.0 multidict-6.0.4 pytorch-lightning-2.0.0 torchmetrics-0.11.4 yarl-1.8.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import torch.nn.functional as F\n",
        "import matplotlib.pyplot as plt\n",
        "import pytorch_lightning as pl\n",
        "from pytorch_lightning import Trainer"
      ],
      "metadata": {
        "id": "0hDwmo22xpvA"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyper-parameters\n",
        "input_size = 784  # 28x28\n",
        "hidden_size = 500\n",
        "num_classes = 10\n",
        "num_epochs = 2\n",
        "batch_size = 100\n",
        "learning_rate = 0.001"
      ],
      "metadata": {
        "id": "lFdoEWXjxsOh"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Network Architecture:"
      ],
      "metadata": {
        "id": "M-ZT12cFx3r1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class LitNeuralNet(pl.LightningModule):\n",
        "    def __init__(self, input_size, hidden_size, num_classes):\n",
        "        super(LitNeuralNet, self).__init__()\n",
        "        self.validation_step_outputs = []\n",
        "        self.input_size = input_size\n",
        "        self.l1 = nn.Linear(input_size, hidden_size)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.l2 = nn.Linear(hidden_size, num_classes)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.l1(x)\n",
        "        out = self.relu(out)\n",
        "        out = self.l2(out)\n",
        "        # no activation and no softmax at the end\n",
        "        return out\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        images, labels = batch\n",
        "        images = images.reshape(-1, 28 * 28)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = self(images)\n",
        "        loss = F.cross_entropy(outputs, labels)\n",
        "        \n",
        "        tensorboard_logs = {'train_loss': loss}\n",
        "        # use key 'log'\n",
        "        return {\"loss\": loss, 'log': tensorboard_logs}\n",
        "\n",
        "    # define what happens for testing here\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        # MNIST dataset\n",
        "        train_dataset = torchvision.datasets.MNIST(\n",
        "            root=\"./data\", train=True, transform=transforms.ToTensor(), download=True\n",
        "        )\n",
        "        # Data loader\n",
        "        train_loader = torch.utils.data.DataLoader(\n",
        "            dataset=train_dataset, batch_size=batch_size, num_workers=4, shuffle=False\n",
        "        )\n",
        "        return train_loader\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        test_dataset = torchvision.datasets.MNIST(\n",
        "            root=\"./data\", train=False, transform=transforms.ToTensor(), download=True\n",
        "        )\n",
        "\n",
        "        test_loader = torch.utils.data.DataLoader(\n",
        "            dataset=test_dataset, batch_size=batch_size, num_workers=4, shuffle=False\n",
        "        )\n",
        "        return test_loader\n",
        "    \n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        images, labels = batch\n",
        "        images = images.reshape(-1, 28 * 28)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = self(images)\n",
        "                        \n",
        "        loss = F.cross_entropy(outputs, labels)\n",
        "\n",
        "        self.validation_step_outputs.append(loss)\n",
        "        return {\"val_loss\": loss}\n",
        "    \n",
        "\n",
        "    ## Used for accumilating steps; as it's executed after each validation epoch\n",
        "    def on_validation_epoch_end(self):\n",
        "        # outputs = list of dictionaries\n",
        "        avg_loss =torch.stack(self.validation_step_outputs).mean()\n",
        "        tensorboard_logs = {'avg_val_loss': avg_loss}\n",
        "        # use key 'log'\n",
        "        self.validation_step_outputs.clear()  # free memory\n",
        "        return {'val_loss': avg_loss, 'log': tensorboard_logs}\n",
        "    \n",
        "\n",
        "    ## If we want a testing phase then we could create two functions. test_step and test_dataloader\n",
        "    \n",
        "    def configure_optimizers(self):\n",
        "        return torch.optim.Adam(self.parameters(), lr=learning_rate)"
      ],
      "metadata": {
        "id": "XNt6X2FCxsQ2"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LitNeuralNet(input_size, hidden_size, num_classes)\n",
        "    \n",
        "# gpus=8\n",
        "# fast_dev_run=True -> runs single batch through training and validation to quickly test wether the model works or not\n",
        "# train_percent_check=0.1 -> train only on 10% of data\n",
        "# auto_lr_find = True will run an algorithm to find the best lr\n",
        "# deterministic = True will help reproduce my results\n",
        "trainer = Trainer(max_epochs=num_epochs)\n",
        "trainer.fit(model)\n",
        "      \n",
        "# advanced features\n",
        "# distributed_backend\n",
        "# (DDP) implements data parallelism at the module level which can run across multiple machines.\n",
        "# 16 bit precision\n",
        "# log_gpu_memory\n",
        "# TPU support\n",
        "\n",
        "# auto_lr_find: automatically finds a good learning rate before training\n",
        "# deterministic: makes training reproducable\n",
        "# gradient_clip_val: 0 default"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 787,
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        },
        "id": "XLXp2GknxsTO",
        "outputId": "6ac43fe2-49ef-431c-b4fb-b4b2b8587280"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name | Type   | Params\n",
            "--------------------------------\n",
            "0 | l1   | Linear | 392 K \n",
            "1 | relu | ReLU   | 0     \n",
            "2 | l2   | Linear | 5.0 K \n",
            "--------------------------------\n",
            "397 K     Trainable params\n",
            "0         Non-trainable params\n",
            "397 K     Total params\n",
            "1.590     Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Sanity Checking: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "52912bd33dc74a02a3f679c9bd75eeb3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7c8edd12776b4208bd8a4dede8053192"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "124a0b1b7dcc41af8d726f2cc46238df"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "979aef89ee0f4283898aded021e8c656"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "19ffd4fe542f48af9da41961dc91a64d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "78fb2f1a98054bca9ab2827385064ba7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "13bbc81837b34479b626606ba2307de6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8a97162d5ab94718a9d5962943da9328"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=2` reached.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext tensorboard"
      ],
      "metadata": {
        "id": "8zpWqmjV7cAi"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorboard import notebook\n",
        "notebook.list() # View open TensorBoard instances"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHBGY33H70hT",
        "outputId": "38a8ef1a-2771-4c5a-95ec-47afe38e4cc6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No known TensorBoard instances running.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%tensorboard --logdir /content/lightning_logs --port 6006"
      ],
      "metadata": {
        "id": "V8e0vHb06sQl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Control TensorBoard display. If no port is provided, \n",
        "# the most recently launched TensorBoard is used\n",
        "notebook.display(port=6006, height=1000) "
      ],
      "metadata": {
        "id": "Ldfmt499756m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K-SglZXgxsWz"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}