6252 lines
569 KiB
Plaintext
Executable File
6252 lines
569 KiB
Plaintext
Executable File
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"import torchvision.datasets as datasets\n",
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"import torchvision.transforms as transforms\n",
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"#from torch.autograd import Variable"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"input_size = 784\n",
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"hidden_size = 400\n",
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"out_size = 10\n",
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"epochs = 10\n",
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"batch_size = 100\n",
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"learning_rate = 0.001"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_dataset = datasets.MNIST(root='./data',\n",
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" train=True,\n",
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" transform=transforms.ToTensor(),\n",
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" download=True)\n",
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"\n",
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"test_dataset = datasets.MNIST(root='./data',\n",
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" train=False,\n",
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" transform=transforms.ToTensor())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# inserting data into a loder class to makeit iterable\n",
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"train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
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" batch_size=batch_size,\n",
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" shuffle=True)\n",
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"\n",
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"test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n",
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" batch_size=batch_size,\n",
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" shuffle=False)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Net(nn.Module):\n",
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" \n",
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" def __init__(self,input_size,hidden_size,out_size):\n",
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" super(Net,self).__init__()\n",
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" self.fc1 = nn.Linear(input_size,hidden_size)\n",
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" self.relu = nn.ReLU()\n",
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" self.fc2 = nn.Linear(hidden_size,hidden_size)\n",
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" self.fc3 = nn.Linear(hidden_size,out_size)\n",
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" \n",
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" def forward(self,x):\n",
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" out = self.fc1(x)\n",
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" out = self.relu(out)\n",
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" out = self.fc2(out)\n",
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" out = self.relu(out)\n",
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" out = self.fc3(out)\n",
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" \n",
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" return out\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create a Net class object\n",
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"net = Net(input_size,hidden_size,out_size)\n",
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"CUDA = torch.cuda.is_available()\n",
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"if CUDA:\n",
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" net = net.cuda()\n",
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" \n",
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"# Select the loss function and optimization method\n",
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"criterion = nn.CrossEntropyLoss()\n",
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"optimizer = torch.optim.Adam(net.parameters(),lr=learning_rate)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor([[0., 0., 0., ..., 0., 0., 0.],\n",
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" [0., 0., 0., ..., 0., 0., 0.],\n",
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" [0., 0., 0., ..., 0., 0., 0.],\n",
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" ...,\n",
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" [0., 0., 0., ..., 0., 0., 0.],\n",
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" [0., 0., 0., ..., 0., 0., 0.],\n",
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" [0., 0., 0., ..., 0., 0., 0.]])\n"
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]
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}
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],
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"source": [
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"for i, (images, labels) in enumerate(train_loader):\n",
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" print(images.view(-1,28*28))\n",
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" break"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch: [1/10], Iteration [1/600], Training Loss: 0.000, Training Accuracy: 100.000%\n",
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"Epoch: [1/10], Iteration [2/600], Training Loss: 0.000, Training Accuracy: 100.000%\n",
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"Epoch: [1/10], Iteration [3/600], Training Loss: 0.001, Training Accuracy: 100.000%\n",
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"Epoch: [1/10], Iteration [4/600], Training Loss: 0.000, Training Accuracy: 100.000%\n",
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"Epoch: [1/10], Iteration [5/600], Training Loss: 0.004, Training Accuracy: 100.000%\n",
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"Epoch: [1/10], Iteration [6/600], Training Loss: 0.002, Training Accuracy: 100.000%\n",
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"Epoch: [1/10], Iteration [7/600], Training Loss: 0.000, Training Accuracy: 100.000%\n",
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"Epoch: [1/10], Iteration [8/600], Training Loss: 0.000, Training Accuracy: 100.000%\n",
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"Epoch: [1/10], Iteration [9/600], Training Loss: 0.006, Training Accuracy: 100.000%\n",
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"Epoch: [1/10], Iteration [10/600], Training Loss: 0.000, Training Accuracy: 100.000%\n",
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"Epoch: [1/10], Iteration [11/600], Training Loss: 0.000, Training Accuracy: 100.000%\n",
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"Epoch: [1/10], Iteration [12/600], Training Loss: 0.000, Training Accuracy: 100.000%\n",
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"Epoch: [1/10], Iteration [13/600], Training Loss: 0.000, Training Accuracy: 100.000%\n",
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"Epoch: [1/10], Iteration [14/600], Training Loss: 0.000, Training Accuracy: 100.000%\n",
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"Epoch: [1/10], Iteration [15/600], Training Loss: 0.009, Training Accuracy: 99.933%\n",
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"Epoch: [1/10], Iteration [16/600], Training Loss: 0.000, Training Accuracy: 99.938%\n",
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"Epoch: [1/10], Iteration [17/600], Training Loss: 0.000, Training Accuracy: 99.941%\n",
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"Epoch: [1/10], Iteration [18/600], Training Loss: 0.003, Training Accuracy: 99.944%\n",
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"Epoch: [1/10], Iteration [19/600], Training Loss: 0.000, Training Accuracy: 99.947%\n",
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"Epoch: [1/10], Iteration [20/600], Training Loss: 0.000, Training Accuracy: 99.950%\n",
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"Epoch: [1/10], Iteration [21/600], Training Loss: 0.000, Training Accuracy: 99.952%\n",
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"Epoch: [1/10], Iteration [22/600], Training Loss: 0.000, Training Accuracy: 99.955%\n",
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"Epoch: [1/10], Iteration [23/600], Training Loss: 0.000, Training Accuracy: 99.957%\n",
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"Epoch: [1/10], Iteration [24/600], Training Loss: 0.001, Training Accuracy: 99.958%\n",
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"Epoch: [1/10], Iteration [25/600], Training Loss: 0.024, Training Accuracy: 99.920%\n",
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"Epoch: [1/10], Iteration [26/600], Training Loss: 0.046, Training Accuracy: 99.885%\n",
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"Epoch: [1/10], Iteration [27/600], Training Loss: 0.000, Training Accuracy: 99.889%\n",
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"Epoch: [1/10], Iteration [28/600], Training Loss: 0.000, Training Accuracy: 99.893%\n",
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"Epoch: [1/10], Iteration [29/600], Training Loss: 0.000, Training Accuracy: 99.897%\n",
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"Epoch: [1/10], Iteration [30/600], Training Loss: 0.004, Training Accuracy: 99.900%\n",
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"Epoch: [1/10], Iteration [31/600], Training Loss: 0.001, Training Accuracy: 99.903%\n",
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"Epoch: [1/10], Iteration [32/600], Training Loss: 0.100, Training Accuracy: 99.875%\n",
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"Epoch: [1/10], Iteration [33/600], Training Loss: 0.000, Training Accuracy: 99.879%\n",
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"Epoch: [1/10], Iteration [38/600], Training Loss: 0.000, Training Accuracy: 99.895%\n",
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"Epoch: [1/10], Iteration [42/600], Training Loss: 0.001, Training Accuracy: 99.905%\n",
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"Epoch: [1/10], Iteration [45/600], Training Loss: 0.004, Training Accuracy: 99.911%\n",
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"Epoch: [1/10], Iteration [50/600], Training Loss: 0.000, Training Accuracy: 99.920%\n",
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"Epoch: [1/10], Iteration [66/600], Training Loss: 0.061, Training Accuracy: 99.879%\n",
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"Epoch: [1/10], Iteration [68/600], Training Loss: 0.001, Training Accuracy: 99.882%\n",
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"Epoch: [10/10], Iteration [593/600], Training Loss: 0.002, Training Accuracy: 99.858%\n",
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"Epoch: [10/10], Iteration [595/600], Training Loss: 0.037, Training Accuracy: 99.858%\n",
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"Epoch: [10/10], Iteration [596/600], Training Loss: 0.000, Training Accuracy: 99.858%\n",
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"Epoch: [10/10], Iteration [598/600], Training Loss: 0.030, Training Accuracy: 99.858%\n",
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"Epoch: [10/10], Iteration [599/600], Training Loss: 0.038, Training Accuracy: 99.857%\n",
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"Epoch: [10/10], Iteration [600/600], Training Loss: 0.000, Training Accuracy: 99.857%\n",
|
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"Done training!!!\n"
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]
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}
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],
|
|
"source": [
|
|
"# Train the network\n",
|
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"correct_train = 0\n",
|
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"total_train = 0\n",
|
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"\n",
|
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"for epoch in range(epochs):\n",
|
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" for i, (images, labels) in enumerate(train_loader):\n",
|
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" # Flatten the image from (batch,1,28,28) --> (100,1,28,28), where 1 is the number of channels (grayscale --> 1) and wrap it in a variable\n",
|
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" #images = Variable(images.view(-1,28*28))\n",
|
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" #labels = Variable(labels)\n",
|
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" images = images.view(-1,28*28)\n",
|
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" \n",
|
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" # Send the images and the lables to our graphics card memory\n",
|
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" if CUDA:\n",
|
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" images = images.cuda()\n",
|
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" labels = labels.cuda()\n",
|
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" \n",
|
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" # Set the gradient to zero\n",
|
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" optimizer.zero_grad()\n",
|
|
" # Pass our trainig set to the object net (which is the nn)\n",
|
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" outputs = net(images)\n",
|
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" _, predicted = torch.max(outputs.data,1) # We select the maximum value got and return its index\n",
|
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" \n",
|
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" total_train += labels.size(0)\n",
|
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" \n",
|
|
" if CUDA:\n",
|
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" correct_train += (predicted.cpu() == labels.cpu()).sum()\n",
|
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" else:\n",
|
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" correct_train += (predicted == labels).sum()\n",
|
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" \n",
|
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" loss = criterion(outputs,labels) # Calculate the loss\n",
|
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" loss.backward() # Backpropagate\n",
|
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" optimizer.step() # Update the weights\n",
|
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" \n",
|
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" #if i+1 % 100 == 0:\n",
|
|
" print(\"Epoch: [{}/{}], Iteration [{}/{}], Training Loss: {:.3f}, Training Accuracy: {:.3f}%\".format\n",
|
|
" (epoch+1, epochs, i+1, len(train_dataset)//batch_size, loss.item(), (100*correct_train.double()/total_train)))\n",
|
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" \n",
|
|
"print(\"Done training!!!\")"
|
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]
|
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Final test accuracy: 97 %\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Test the NN\n",
|
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"correct = 0\n",
|
|
"total = 0\n",
|
|
"\n",
|
|
"for images, labels in test_loader:\n",
|
|
" images = images.view(-1,28*28)\n",
|
|
" if CUDA:\n",
|
|
" images = images.cuda()\n",
|
|
" \n",
|
|
" # for each input (sample/row) in the batch the output will contain 10 elements\n",
|
|
" outputs = net(images)\n",
|
|
" _, predicted = torch.max(outputs.data,1)\n",
|
|
" \n",
|
|
" total += labels.size(0)\n",
|
|
" if CUDA:\n",
|
|
" correct += (predicted.cpu() == labels.cpu()).sum()\n",
|
|
" else:\n",
|
|
" correct += (predicted == labels).sum()\n",
|
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" \n",
|
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"\n",
|
|
"print('Final test accuracy: %d %%' % (100*correct/total))\n",
|
|
" "
|
|
]
|
|
},
|
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{
|
|
"cell_type": "code",
|
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"execution_count": null,
|
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"metadata": {},
|
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"outputs": [],
|
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"source": []
|
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}
|
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],
|
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"metadata": {
|
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"kernelspec": {
|
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"display_name": "Python 3",
|
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"language": "python",
|
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"name": "python3"
|
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},
|
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"language_info": {
|
|
"codemirror_mode": {
|
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"name": "ipython",
|
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"version": 3
|
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},
|
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"file_extension": ".py",
|
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"mimetype": "text/x-python",
|
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"name": "python",
|
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"nbconvert_exporter": "python",
|
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"pygments_lexer": "ipython3",
|
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"version": "3.7.7"
|
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}
|
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},
|
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"nbformat": 4,
|
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"nbformat_minor": 4
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}
|