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ritog/fashion_mnist_training.ipynb

Created November 20, 2020 18:26
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Fashion_MNIST_Training.ipynb
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fashion_mnist_training.ipynb
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
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"source": [
"<a href=\"https://colab.research.google.com/gist/ghosh-r/9ef1eace15ce2e62a646402573b58e32/fashion_mnist_training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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},
{
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"source": [
"# Classifying Fashion-MNIST\n",
"\n",
"[Fashion-MNIST dataset](https://github.com/zalandoresearch/fashion-mnist) is a set of 28x28 greyscale images of clothes.\n",
"\n",
"<img src='https://github.com/udacity/deep-learning-v2-pytorch/blob/master/intro-to-pytorch/assets/fashion-mnist-sprite.png?raw=1' width=500px>"
]
},
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"outputId": "b8ae8a28-f2e8-417b-9f95-f3b85c0859d8"
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"source": [
"import torch\n",
"from torchvision import datasets, transforms\n",
"\n",
"transform = transforms.Compose([transforms.ToTensor(),\n",
" transforms.Normalize((0.5,), (0.5,))])\n",
"\n",
"trainset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=True, transform=transform)\n",
"trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
"\n",
"testset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=False, transform=transform)\n",
"testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True)"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /root/.pytorch/F_MNIST_data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
],
"name": "stdout"
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{
"output_type": "stream",
"text": [
"Extracting /root/.pytorch/F_MNIST_data/FashionMNIST/raw/train-images-idx3-ubyte.gz to /root/.pytorch/F_MNIST_data/FashionMNIST/raw\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /root/.pytorch/F_MNIST_data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
],
"name": "stdout"
},
{
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{
"output_type": "stream",
"text": [
"Extracting /root/.pytorch/F_MNIST_data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /root/.pytorch/F_MNIST_data/FashionMNIST/raw\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /root/.pytorch/F_MNIST_data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
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{
"output_type": "stream",
"text": [
"Extracting /root/.pytorch/F_MNIST_data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /root/.pytorch/F_MNIST_data/FashionMNIST/raw\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /root/.pytorch/F_MNIST_data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "46b7868342e54857bf59d01774882177",
"version_minor": 0,
"version_major": 2
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"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
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},
"metadata": {
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},
{
"output_type": "stream",
"text": [
"Extracting /root/.pytorch/F_MNIST_data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /root/.pytorch/F_MNIST_data/FashionMNIST/raw\n",
"Processing...\n",
"Done!\n",
"\n",
"\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:480: 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. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
" return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QK5OMe3-v8ve",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "667412ac-ee26-43a4-c1d4-75aebfffd93f"
},
"source": [
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"device"
],
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"device(type='cuda')"
]
},
"metadata": {
"tags": []
},
"execution_count": 2
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Yb7mADELDRno"
},
"source": [
"Here we can see one of the images."
]
},
{
"cell_type": "code",
"metadata": {
"id": "fad8F7gyKxHA"
},
"source": [
"import matplotlib.pyplot as plt"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ll0t3B0RDRno",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
},
"outputId": "d6a27ffd-dc1a-42f6-afc3-303983103f24"
},
"source": [
"image, label = next(iter(trainloader))\n",
"plt.imshow(image[0,0,:], cmap='Greys');"
],
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Td9YYudLjElH",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0126aa4d-68bf-4c56-9857-38eb0756a988"
},
"source": [
"image.shape, label.shape"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(torch.Size([64, 1, 28, 28]), torch.Size([64]))"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SK350OzjDRno"
},
"source": [
"## Building the network"
]
},
{
"cell_type": "code",
"metadata": {
"id": "UN-S9paZvmFc"
},
"source": [
"from torch import nn\n",
"import torch.nn.functional as F"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mKKr0Un6DRno"
},
"source": [
"class Network(nn.Module):\n",
" def __init__(self):\n",
" super(Network, self).__init__()\n",
"\n",
" self.fc1 = nn.Linear(28*28, 256)\n",
" self.fc2 = nn.Linear(256, 256)\n",
" self.fc3 = nn.Linear(256, 64)\n",
" self.fc4 = nn.Linear(64, 64)\n",
" self.fc5 = nn.Linear(64, 10)\n",
"\n",
" def forward(self, x):\n",
" x = F.relu(self.fc1(x))\n",
" x = F.relu(self.fc2(x))\n",
" x = F.relu(self.fc3(x))\n",
" x = F.relu(self.fc4(x))\n",
" x = F.softmax(self.fc5(x), dim=1)\n",
"\n",
" return x"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "IIdo1Gw0JP-3",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "26a3310b-74ba-4c50-e1dd-0eba18a2b966"
},
"source": [
"model = Network()\n",
"model.to(device)\n",
"model"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Network(\n",
" (fc1): Linear(in_features=784, out_features=256, bias=True)\n",
" (fc2): Linear(in_features=256, out_features=256, bias=True)\n",
" (fc3): Linear(in_features=256, out_features=64, bias=True)\n",
" (fc4): Linear(in_features=64, out_features=64, bias=True)\n",
" (fc5): Linear(in_features=64, out_features=10, bias=True)\n",
")"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-5PRVVHJDRno"
},
"source": [
"\n",
"# Training the network"
]
},
{
"cell_type": "code",
"metadata": {
"id": "f4KpTj6rOwUS"
},
"source": [
"from torch import optim"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "32U4RAjKDRno"
},
"source": [
"criterion = nn.CrossEntropyLoss()\n",
"optimizer = optim.SGD(model.parameters(), lr=0.009)"
],
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JLc2gljsDRnp",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2369443e-dc33-4fe9-a2f7-562ad38d5fb9"
},
"source": [
"for epoch in range(25):\n",
" running_loss = 0\n",
" for image, label in trainloader:\n",
" image, label = image.to(device), label.to(device)\n",
" image = image.view(image.shape[0], -1)\n",
"\n",
" optimizer.zero_grad()\n",
"\n",
" output = model(image)\n",
" loss = criterion(output, label)\n",
" loss.backward()\n",
"\n",
" optimizer.step()\n",
"\n",
" running_loss += loss.item()\n",
"\n",
" else:\n",
" print(f\"Training loss: {running_loss/len(trainloader)}\")"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
"Training loss: 2.30225410288585\n",
"Training loss: 2.3014346264572794\n",
"Training loss: 2.300310645276295\n",
"Training loss: 2.2982762065777647\n",
"Training loss: 2.293135287919278\n",
"Training loss: 2.2587068868852627\n",
"Training loss: 2.170536396600036\n",
"Training loss: 2.068142019736487\n",
"Training loss: 1.929231432963536\n",
"Training loss: 1.8503042384505526\n",
"Training loss: 1.8161777144810285\n",
"Training loss: 1.7979587796908707\n",
"Training loss: 1.7853560234183696\n",
"Training loss: 1.7752022881751883\n",
"Training loss: 1.7654367168066598\n",
"Training loss: 1.7565805426538625\n",
"Training loss: 1.7503288355209172\n",
"Training loss: 1.7458801847785266\n",
"Training loss: 1.7418527005832078\n",
"Training loss: 1.7381157127778921\n",
"Training loss: 1.7355601821881113\n",
"Training loss: 1.7328494567352573\n",
"Training loss: 1.7305794240060899\n",
"Training loss: 1.7285002680983879\n",
"Training loss: 1.72686947268972\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UT2VXPhp3k2s"
},
"source": [
"## TODO\n",
"1. Add a validation set\n",
"2. Add a test set, and checking accuracy there\n",
"3. Inference for single files\n",
"4. Add calculation for performance in each class"
]
}
]
}
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