NewFeed.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "NewFeed.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM29piysHT4wdNEcDpR0Xe/",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Chaitanyate/Deep_learning/blob/master/NewFeed.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZhyWi_7c-kcm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "from torchvision.datasets import MNIST\n",
        "import torchvision.transforms as transform"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5eH_JqfD-yps",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "datasets=MNIST(root='home/',download=True,transform=transform.ToTensor())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ykXnBc0p--tC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "eaacf9c0-8a59-4fd4-be0a-72953f3eac6f"
      },
      "source": [
        "import numpy as np\n",
        "len(datasets)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GYaLpmMm_FlW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def split_ind(n,val):\n",
        "  nval=int(val*n)\n",
        "  idx=np.random.permutation(n)\n",
        "  return idx[nval:],idx[:nval]\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3496oQhI_HWk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train,validate=split_ind(len(datasets),0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vHbz1eUg_Jgw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "from torch.utils.data.dataloader import DataLoader "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTd2re_m_UYH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size=100\n",
        "train_sample=SubsetRandomSampler(train)\n",
        "train_loads=DataLoader(datasets,batch_size,sampler=train_sample)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uM0xMdfu_ew5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "val_sample=SubsetRandomSampler(validate)\n",
        "val_loads=DataLoader(datasets,batch_size,sampler=val_sample)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aLBZevzU_hKs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn as nn\n",
        "input_size=784\n",
        "output_size=10\n",
        "hidden_size=32\n",
        "import torch.nn.functional as F\n",
        "use_cuda=torch.cuda.is_available()\n",
        "#devices=torch.device('cuda:0' if use_cuda else 'cpu')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "menkXjqx_n1p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class mnist(nn.Module):\n",
        "  def __init__(self,input_size,hidden_size,output_size):\n",
        "    super(mnist,self).__init__()\n",
        "    self.linear1=nn.Linear(input_size,hidden_size)\n",
        "    self.linear2=nn.Linear(hidden_size,output_size)\n",
        "    #self.relu=nn.Relu().cuda(devices)\n",
        "\n",
        "  def forward(self,xb):\n",
        "      xb=xb.reshape(-1,784)\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": "zh0uBi5C_q1A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=mnist(input_size,hidden_size,output_size)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B3jFcJYV_tpG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "80bf9494-94b6-4204-a097-1464ceacd113"
      },
      "source": [
        "for i,j in train_loads:\n",
        "  print(j)\n",
        "  print(i)\n",
        "  out=model(i)\n",
        "  break\n",
        "print((out)[:2].data)\n"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([0, 6, 9, 3, 6, 5, 9, 7, 5, 7, 4, 3, 4, 9, 1, 6, 8, 0, 6, 7, 1, 8, 1, 2,\n",
            "        4, 4, 0, 7, 3, 8, 3, 1, 5, 1, 9, 0, 7, 4, 1, 2, 1, 0, 4, 2, 9, 3, 0, 6,\n",
            "        2, 9, 2, 4, 7, 0, 8, 3, 1, 3, 7, 0, 8, 2, 8, 3, 1, 5, 1, 3, 1, 4, 2, 3,\n",
            "        8, 1, 3, 9, 4, 9, 5, 2, 2, 9, 8, 6, 9, 6, 0, 8, 7, 4, 5, 7, 4, 2, 8, 0,\n",
            "        8, 6, 1, 6])\n",
            "tensor([[[[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          ...,\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.]]],\n",
            "\n",
            "\n",
            "        [[[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          ...,\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.]]],\n",
            "\n",
            "\n",
            "        [[[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          ...,\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.]]],\n",
            "\n",
            "\n",
            "        ...,\n",
            "\n",
            "\n",
            "        [[[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          ...,\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.]]],\n",
            "\n",
            "\n",
            "        [[[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          ...,\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.]]],\n",
            "\n",
            "\n",
            "        [[[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          ...,\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.]]]])\n",
            "tensor([[-0.0707,  0.0946, -0.1706,  0.0131,  0.0854, -0.0770, -0.0975,  0.0678,\n",
            "         -0.0090, -0.0067],\n",
            "        [-0.0482,  0.0485, -0.0896, -0.0730, -0.0098, -0.1026, -0.0876,  0.0314,\n",
            "         -0.0348, -0.0566]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxlczO2u_v4p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "b9f2909d-10e2-4173-9285-c550256c7e1a"
      },
      "source": [
        "for t in model.parameters():\n",
        "  print(t.shape)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([32, 784])\n",
            "torch.Size([32])\n",
            "torch.Size([10, 32])\n",
            "torch.Size([10])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GZPli-shAUuA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "26a5864d-c194-4516-ba10-d7b9b6dea1ac"
      },
      "source": [
        "def get_default_devices():\n",
        "  if torch.cuda.is_available():\n",
        "    return torch.device('cuda')\n",
        "  else:\n",
        "    return torch.device('cpu')\n",
        "\n",
        "device=get_default_devices()\n",
        "device"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0799mXKeAX90",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def to_Device(data,device):\n",
        "  if isinstance(data,(list,tuple)):\n",
        "    return [to_Device(x,device) for x in data]\n",
        "\n",
        "\n",
        "  return data.to(device,non_blocking=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAfmPWGFAcXu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 972
        },
        "outputId": "f24d4e51-274c-4119-f604-0f8b40a813a6"
      },
      "source": [
        "\n",
        "for images,label in train_loads:\n",
        "  images=to_Device(images,device)\n",
        "  print(images,device)\n",
        "  break\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[[[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          ...,\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.]]],\n",
            "\n",
            "\n",
            "        [[[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          ...,\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.]]],\n",
            "\n",
            "\n",
            "        [[[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          ...,\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.]]],\n",
            "\n",
            "\n",
            "        ...,\n",
            "\n",
            "\n",
            "        [[[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          ...,\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.]]],\n",
            "\n",
            "\n",
            "        [[[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          ...,\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.]]],\n",
            "\n",
            "\n",
            "        [[[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          ...,\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "          [0., 0., 0.,  ..., 0., 0., 0.]]]], device='cuda:0') cuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kExMKh4TAfYN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class DeviceDataLoader():\n",
        "  def __init__(self,dl,device):\n",
        "    self.dl=dl\n",
        "    self.device=device\n",
        "\n",
        "  def __iter__(self):\n",
        "    for b in self.dl:\n",
        "      yield to_Device(b,self.device)\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.dl)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "imwXEW7dAmOJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dl=DeviceDataLoader(train_loads,device)\n",
        "val_dl=DeviceDataLoader(val_loads,device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mXgvKH6JArvC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "cff775bf-7779-4584-ccb7-2c36df469ab1"
      },
      "source": [
        "for xb,yb in val_dl:\n",
        "  print(xb.device)\n",
        "  print(yb)\n",
        "  break"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda:0\n",
            "tensor([7, 7, 3, 5, 3, 3, 7, 4, 3, 1, 2, 8, 4, 2, 3, 8, 6, 6, 6, 3, 3, 7, 6, 5,\n",
            "        7, 2, 4, 9, 6, 1, 9, 1, 5, 8, 4, 2, 5, 9, 7, 3, 7, 6, 0, 4, 2, 9, 6, 9,\n",
            "        3, 7, 6, 6, 4, 6, 1, 4, 0, 3, 9, 1, 2, 8, 5, 4, 7, 5, 1, 0, 2, 2, 8, 5,\n",
            "        5, 0, 8, 2, 8, 7, 3, 9, 9, 6, 7, 2, 9, 4, 1, 8, 1, 2, 3, 2, 0, 8, 8, 6,\n",
            "        7, 9, 6, 1], device='cuda:0')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "py812C-ZAupt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def loss_batch(model,loss_func,xb,yb,opt=None,metric=None):\n",
        "  preds=model(xb)\n",
        "  loss=loss_func(preds,yb)\n",
        "\n",
        "  if opt is not None:\n",
        "    loss.backward()\n",
        "    opt.stop()\n",
        "    opt.zero()\n",
        "\n",
        "    metric_result=None\n",
        "    if metric is not None:\n",
        "      metric_result=metric(preds,yb)\n",
        "\n",
        "    return loss.item(),len(xb),metric_result\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kF-k5OYIAx2b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluate(model,loss_fun,valid_dl,metric):\n",
        "  with torch.no_grad():\n",
        "    results=[loss_batch(model,loss_fun,xb,yb,metric=metric) for xb,yb in valid_dl]\n",
        "    losses,nums,metrics=zip(*results)\n",
        "    total=np.sum(nums)\n",
        "    avg_loss=np.sum(np.multiply(losses,num))/total\n",
        "    avg_metric=np.sum(np.multiply(metrics,num))/total\n",
        "\n",
        "    return avg_metric,avg_loss,total\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "irWnvfocA0ak",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def accuracy(outputs,labels):\n",
        "  _,preds=torch.max(output,dim=1)\n",
        "  return torch.sum(preds==label)/len(label)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EKYfDx71A3aY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def fit(epochs,lr,model,loss_fn,train_dl,valid_dl,metric=None,opt_fn=None):\n",
        "  losses,metrices=[],[]\n",
        "\n",
        "  if opt_fn is None:opt_fn=torch.optim.SGD\n",
        "  opt=torch.optim.SGD(model.parameters(),lr=lr)\n",
        "\n",
        "  for epoch in range(epochs):\n",
        "    for xb,yb in train_dl:\n",
        "      loss=loss_batch(model,loss_fn,xb,yb,opt)\n",
        "    result=evaluate(model,loss_fn,valid_dl,metric,val_loss,total,val_metric=results)\n",
        "\n",
        "    losses.append(val_loss)\n",
        "    metrices.append(val+val_metric)\n",
        "\n",
        "    if metric is None:\n",
        "      print('Epochs[{}/{}].Loss:{:.4f}'.format(epochs+1,epochs,val_loss))\n",
        "\n",
        "    else:\n",
        "      print('Epochs[{}/{}].Loss:{:.4f}'.format(epochs+1,epochs,val_loss,metric.__name__,val_metric))\n",
        "\n",
        "    return losses,metrices\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oF3mz2ZSA5qS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "72541d80-d9a7-4e50-891c-d9eaf663e943"
      },
      "source": [
        "model=mnist(input_size,hidden_size,output_size)\n",
        "to_Device(model,device)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mnist(\n",
              "  (linear1): Linear(in_features=784, out_features=32, bias=True)\n",
              "  (linear2): Linear(in_features=32, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpwRscpQA8tj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "f3351e0a-47ea-496b-c012-a87e6b3c52e4"
      },
      "source": [
        "val_loss,total,val_acc=evaluate(model,F.cross_entropy,val_loads,metric=accuracy)\n",
        "print(\"loss:{:.4f},accuracy:{:.4f}\".format(val_loss,val_acc))\n"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-48-7b93dc6821ca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mval_loss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtotal\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mval_acc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_entropy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mval_loads\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccuracy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"loss:{:.4f},accuracy:{:.4f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_loss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mval_acc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-42-e994e9169068>\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(model, loss_fun, valid_dl, metric)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mloss_fun\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalid_dl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m   \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mresults\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mloss_fun\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvalid_dl\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mlosses\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnums\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mtotal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnums\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-42-e994e9169068>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mloss_fun\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalid_dl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m   \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mresults\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mloss_fun\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvalid_dl\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mlosses\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnums\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mtotal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnums\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-41-918daf38b5aa>\u001b[0m in \u001b[0;36mloss_batch\u001b[0;34m(model, loss_func, xb, yb, opt, metric)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mpreds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m   \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mopt\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    548\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    551\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-24-7c62e548865a>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, xb)\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m       \u001b[0mxb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m784\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m       \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m       \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m       \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    548\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    551\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mlinear\u001b[0;34m(input, weight, bias)\u001b[0m\n\u001b[1;32m   1608\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1609\u001b[0m         \u001b[0;31m# fused op is marginally faster\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1610\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddmm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1611\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1612\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Expected object of device type cuda but got device type cpu for argument #2 'mat1' in call to _th_addmm"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sAoWfiQvBB3-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "losses1,metrices1=fit(5,0.5,model,F.cross_entropy,train_dl,val_dl,accuracy)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L1o3kKxkBLQv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}