Feedforward.ipynb

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        "<a href=\"https://colab.research.google.com/github/Chaitanyate/Deep_learning/blob/master/Feedforward.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import torchvision\n",
        "from torchvision.datasets import MNIST\n",
        "from torchvision.transforms import ToTensor\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "from torch.utils.data.dataloader import DataLoader"
      ],
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      "source": [
        "dataset= MNIST(root='home/',download=True,transform=ToTensor)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to home/MNIST/raw/train-images-idx3-ubyte.gz\n"
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        },
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          "output_type": "stream",
          "text": [
            "Extracting home/MNIST/raw/train-images-idx3-ubyte.gz to home/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to home/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
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          "text": [
            "Extracting home/MNIST/raw/train-labels-idx1-ubyte.gz to home/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to home/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
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          "text": [
            "Extracting home/MNIST/raw/t10k-images-idx3-ubyte.gz to home/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to home/MNIST/raw/t10k-labels-idx1-ubyte.gz\n",
            "\n",
            "\n"
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        {
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          "text": [
            "Extracting home/MNIST/raw/t10k-labels-idx1-ubyte.gz to home/MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/pytorch/torch/csrc/utils/tensor_numpy.cpp:141: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zq78UkxE0rnf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def split_in(n,val):\n",
        "  n_val=int(n*val)\n",
        "  idx=np.random.permutation(n)\n",
        "  return idx[n_val:],idx[:n_val]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnyqAUw91IA8",
        "colab_type": "code",
        "outputId": "f79a7b63-3e16-49a4-fa99-055dbb7f2f73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "train_indices,val_indices=split_in(len(dataset),0.2)\n",
        "print(len(train_indices))\n",
        "print(val_indices[:20])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "48000\n",
            "[50455 57119 27171 33394  4371  1997 43549 31242 52643 46436 16885 28724\n",
            " 23254  6458 23181 23821  6494 37468 43777 37860]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K2uz26ST2I50",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size=100\n",
        "\n",
        "train_sampler=SubsetRandomSampler(train_indices)\n",
        "train_dl=DataLoader(dataset,batch_size,sampler=train_sampler)\n",
        "\n",
        "val_sampler=SubsetRandomSampler(val_indices)\n",
        "val_dl=DataLoader(dataset,batch_size,sampler=val_sampler)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sT7Q672i2-sI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn.functional as F\n",
        "import torch.nn as nn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idvTBevOPcmQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class MnistModel(nn.Module):\n",
        "  def __init__(self,in_size,hidden_size,out_size):\n",
        "    super().__init__()\n",
        "    self.linear1=nn.Linear(in_size,hidden_size)\n",
        "    self.linear2=nn.Linear(hidden_size,out_size)\n",
        "\n",
        "\n",
        "    def forward(self,xb):\n",
        "      xb=xb.view(xb.size(0),-1)\n",
        "\n",
        "      out=self.linear1(xb)\n",
        "      out=F.relu(out)\n",
        "      out=self.linear2(out)\n",
        "      return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0oOQdeYqRjtH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_size=784\n",
        "num_class=10\n",
        "model=MnistModel(input_size,hidden_size=32,out_size=num_class)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cki1djqwR2fV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}