{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
"- Author: Sebastian Raschka\n",
"- GitHub Repository: https://github.com/rasbt/deeplearning-models"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sebastian Raschka \n",
"\n",
"CPython 3.6.8\n",
"IPython 7.2.0\n",
"\n",
"torch 1.0.0\n"
]
}
],
"source": [
"%load_ext watermark\n",
"%watermark -a 'Sebastian Raschka' -v -p torch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Runs on CPU or GPU (if available)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Zoo -- Multilayer Perceptron"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"import numpy as np\n",
"from torchvision import datasets\n",
"from torchvision import transforms\n",
"from torch.utils.data import DataLoader\n",
"import torch.nn.functional as F\n",
"import torch\n",
"\n",
"\n",
"if torch.cuda.is_available():\n",
" torch.backends.cudnn.deterministic = True"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Settings and Dataset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Image batch dimensions: torch.Size([64, 1, 28, 28])\n",
"Image label dimensions: torch.Size([64])\n"
]
}
],
"source": [
"##########################\n",
"### SETTINGS\n",
"##########################\n",
"\n",
"# Device\n",
"device = torch.device(\"cuda:3\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"# Hyperparameters\n",
"random_seed = 1\n",
"learning_rate = 0.1\n",
"num_epochs = 10\n",
"batch_size = 64\n",
"\n",
"# Architecture\n",
"num_features = 784\n",
"num_hidden_1 = 128\n",
"num_hidden_2 = 256\n",
"num_classes = 10\n",
"\n",
"\n",
"##########################\n",
"### MNIST DATASET\n",
"##########################\n",
"\n",
"# Note transforms.ToTensor() scales input images\n",
"# to 0-1 range\n",
"train_dataset = datasets.MNIST(root='data', \n",
" train=True, \n",
" transform=transforms.ToTensor(),\n",
" download=True)\n",
"\n",
"test_dataset = datasets.MNIST(root='data', \n",
" train=False, \n",
" transform=transforms.ToTensor())\n",
"\n",
"\n",
"train_loader = DataLoader(dataset=train_dataset, \n",
" batch_size=batch_size, \n",
" shuffle=True)\n",
"\n",
"test_loader = DataLoader(dataset=test_dataset, \n",
" batch_size=batch_size, \n",
" shuffle=False)\n",
"\n",
"# Checking the dataset\n",
"for images, labels in train_loader: \n",
" print('Image batch dimensions:', images.shape)\n",
" print('Image label dimensions:', labels.shape)\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"##########################\n",
"### MODEL\n",
"##########################\n",
"\n",
"class MultilayerPerceptron(torch.nn.Module):\n",
"\n",
" def __init__(self, num_features, num_classes):\n",
" super(MultilayerPerceptron, self).__init__()\n",
" \n",
" ### 1st hidden layer\n",
" self.linear_1 = torch.nn.Linear(num_features, num_hidden_1)\n",
" # The following two lines are not necessary, \n",
" # but used here to demonstrate how to access the weights\n",
" # and use a different weight initialization.\n",
" # By default, PyTorch uses Xavier/Glorot initialization, which\n",
" # should usually be preferred.\n",
" self.linear_1.weight.detach().normal_(0.0, 0.1)\n",
" self.linear_1.bias.detach().zero_()\n",
" \n",
" ### 2nd hidden layer\n",
" self.linear_2 = torch.nn.Linear(num_hidden_1, num_hidden_2)\n",
" self.linear_2.weight.detach().normal_(0.0, 0.1)\n",
" self.linear_2.bias.detach().zero_()\n",
" \n",
" ### Output layer\n",
" self.linear_out = torch.nn.Linear(num_hidden_2, num_classes)\n",
" self.linear_out.weight.detach().normal_(0.0, 0.1)\n",
" self.linear_out.bias.detach().zero_()\n",
" \n",
" def forward(self, x):\n",
" out = self.linear_1(x)\n",
" out = F.relu(out)\n",
" out = self.linear_2(out)\n",
" out = F.relu(out)\n",
" logits = self.linear_out(out)\n",
" probas = F.log_softmax(logits, dim=1)\n",
" return logits, probas\n",
"\n",
" \n",
"torch.manual_seed(random_seed)\n",
"model = MultilayerPerceptron(num_features=num_features,\n",
" num_classes=num_classes)\n",
"\n",
"model = model.to(device)\n",
"\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 001/010 | Batch 000/938 | Cost: 2.4231\n",
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"Epoch: 001/010 training accuracy: 94.90%\n",
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"Epoch: 002/010 training accuracy: 96.62%\n",
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"Epoch: 003/010 training accuracy: 97.70%\n",
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"Epoch: 004/010 training accuracy: 98.01%\n",
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"Epoch: 005/010 training accuracy: 98.56%\n",
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"Epoch: 006/010 training accuracy: 98.88%\n",
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"Epoch: 007/010 training accuracy: 98.98%\n",
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"Epoch: 008/010 training accuracy: 99.25%\n",
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"Epoch: 009/010 training accuracy: 99.30%\n",
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"Epoch: 010/010 training accuracy: 99.00%\n",
"Time elapsed: 2.23 min\n",
"Total Training Time: 2.23 min\n"
]
}
],
"source": [
"def compute_accuracy(net, data_loader):\n",
" net.eval()\n",
" correct_pred, num_examples = 0, 0\n",
" with torch.no_grad():\n",
" for features, targets in data_loader:\n",
" features = features.view(-1, 28*28).to(device)\n",
" targets = targets.to(device)\n",
" logits, probas = net(features)\n",
" _, predicted_labels = torch.max(probas, 1)\n",
" num_examples += targets.size(0)\n",
" correct_pred += (predicted_labels == targets).sum()\n",
" return correct_pred.float()/num_examples * 100\n",
" \n",
"\n",
"start_time = time.time()\n",
"for epoch in range(num_epochs):\n",
" model.train()\n",
" for batch_idx, (features, targets) in enumerate(train_loader):\n",
" \n",
" features = features.view(-1, 28*28).to(device)\n",
" targets = targets.to(device)\n",
" \n",
" ### FORWARD AND BACK PROP\n",
" logits, probas = model(features)\n",
" cost = F.cross_entropy(logits, targets)\n",
" optimizer.zero_grad()\n",
" \n",
" cost.backward()\n",
" \n",
" ### UPDATE MODEL PARAMETERS\n",
" optimizer.step()\n",
" \n",
" ### LOGGING\n",
" if not batch_idx % 50:\n",
" print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \n",
" %(epoch+1, num_epochs, batch_idx, \n",
" len(train_loader), cost))\n",
"\n",
" with torch.set_grad_enabled(False):\n",
" print('Epoch: %03d/%03d training accuracy: %.2f%%' % (\n",
" epoch+1, num_epochs, \n",
" compute_accuracy(model, train_loader)))\n",
" \n",
" print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\n",
" \n",
"print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test accuracy: 97.54%\n"
]
}
],
"source": [
"print('Test accuracy: %.2f%%' % (compute_accuracy(model, test_loader)))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"numpy 1.15.4\n",
"torch 1.0.0\n",
"\n"
]
}
],
"source": [
"%watermark -iv"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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"toc": {
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"title_cell": "Table of Contents",
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},
"nbformat": 4,
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