{ "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 with BatchNorm" ] }, { "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:0\" 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 to lones 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", " self.linear_1_bn = torch.nn.BatchNorm1d(num_hidden_1)\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", " self.linear_2_bn = torch.nn.BatchNorm1d(num_hidden_2)\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", " # note that batchnorm is in the classic\n", " # sense placed before the activation\n", " out = self.linear_1_bn(out)\n", " out = F.relu(out)\n", " \n", " out = self.linear_2(out)\n", " out = self.linear_2_bn(out)\n", " out = F.relu(out)\n", " \n", " logits = self.linear_out(out)\n", " probas = F.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: 2.6465\n", "Epoch: 001/010 | Batch 050/938 | Cost: 1.0305\n", "Epoch: 001/010 | Batch 100/938 | Cost: 0.5404\n", "Epoch: 001/010 | Batch 150/938 | Cost: 0.4430\n", "Epoch: 001/010 | Batch 200/938 | Cost: 0.3235\n", "Epoch: 001/010 | Batch 250/938 | Cost: 0.1927\n", "Epoch: 001/010 | Batch 300/938 | Cost: 0.4007\n", "Epoch: 001/010 | Batch 350/938 | Cost: 0.3802\n", "Epoch: 001/010 | Batch 400/938 | Cost: 0.2528\n", "Epoch: 001/010 | Batch 450/938 | Cost: 0.2257\n", "Epoch: 001/010 | Batch 500/938 | Cost: 0.1454\n", "Epoch: 001/010 | Batch 550/938 | Cost: 0.2160\n", "Epoch: 001/010 | Batch 600/938 | Cost: 0.3425\n", "Epoch: 001/010 | Batch 650/938 | Cost: 0.2175\n", "Epoch: 001/010 | Batch 700/938 | Cost: 0.2307\n", "Epoch: 001/010 | Batch 750/938 | Cost: 0.3723\n", "Epoch: 001/010 | Batch 800/938 | Cost: 0.2452\n", "Epoch: 001/010 | Batch 850/938 | Cost: 0.1285\n", "Epoch: 001/010 | Batch 900/938 | Cost: 0.1302\n", "Epoch: 001/010 training accuracy: 95.63%\n", "Time elapsed: 0.22 min\n", "Epoch: 002/010 | Batch 000/938 | Cost: 0.2137\n", "Epoch: 002/010 | Batch 050/938 | Cost: 0.1923\n", "Epoch: 002/010 | Batch 100/938 | Cost: 0.1739\n", "Epoch: 002/010 | Batch 150/938 | Cost: 0.0742\n", "Epoch: 002/010 | Batch 200/938 | Cost: 0.2186\n", "Epoch: 002/010 | Batch 250/938 | Cost: 0.1424\n", "Epoch: 002/010 | Batch 300/938 | Cost: 0.1131\n", "Epoch: 002/010 | Batch 350/938 | Cost: 0.0575\n", "Epoch: 002/010 | Batch 400/938 | Cost: 0.1232\n", "Epoch: 002/010 | Batch 450/938 | Cost: 0.2385\n", "Epoch: 002/010 | Batch 500/938 | Cost: 0.1344\n", "Epoch: 002/010 | Batch 550/938 | Cost: 0.0950\n", "Epoch: 002/010 | Batch 600/938 | Cost: 0.1565\n", "Epoch: 002/010 | Batch 650/938 | Cost: 0.1312\n", "Epoch: 002/010 | Batch 700/938 | Cost: 0.0859\n", "Epoch: 002/010 | Batch 750/938 | Cost: 0.1722\n", "Epoch: 002/010 | Batch 800/938 | Cost: 0.0630\n", "Epoch: 002/010 | Batch 850/938 | Cost: 0.2606\n", "Epoch: 002/010 | Batch 900/938 | Cost: 0.1681\n", "Epoch: 002/010 training accuracy: 96.94%\n", "Time elapsed: 0.45 min\n", "Epoch: 003/010 | Batch 000/938 | Cost: 0.0676\n", "Epoch: 003/010 | Batch 050/938 | Cost: 0.1975\n", "Epoch: 003/010 | Batch 100/938 | Cost: 0.1241\n", "Epoch: 003/010 | Batch 150/938 | Cost: 0.1723\n", "Epoch: 003/010 | Batch 200/938 | Cost: 0.2233\n", "Epoch: 003/010 | Batch 250/938 | Cost: 0.2249\n", "Epoch: 003/010 | Batch 300/938 | Cost: 0.1027\n", "Epoch: 003/010 | Batch 350/938 | Cost: 0.0369\n", "Epoch: 003/010 | Batch 400/938 | Cost: 0.1460\n", "Epoch: 003/010 | Batch 450/938 | Cost: 0.0430\n", "Epoch: 003/010 | Batch 500/938 | Cost: 0.0821\n", "Epoch: 003/010 | Batch 550/938 | Cost: 0.1188\n", "Epoch: 003/010 | Batch 600/938 | Cost: 0.0424\n", "Epoch: 003/010 | Batch 650/938 | Cost: 0.2548\n", "Epoch: 003/010 | Batch 700/938 | Cost: 0.1219\n", "Epoch: 003/010 | Batch 750/938 | Cost: 0.0623\n", "Epoch: 003/010 | Batch 800/938 | Cost: 0.0557\n", "Epoch: 003/010 | Batch 850/938 | Cost: 0.0999\n", "Epoch: 003/010 | Batch 900/938 | Cost: 0.0595\n", "Epoch: 003/010 training accuracy: 97.93%\n", "Time elapsed: 0.66 min\n", "Epoch: 004/010 | Batch 000/938 | Cost: 0.1017\n", "Epoch: 004/010 | Batch 050/938 | Cost: 0.0885\n", "Epoch: 004/010 | Batch 100/938 | Cost: 0.0252\n", "Epoch: 004/010 | Batch 150/938 | Cost: 0.1987\n", "Epoch: 004/010 | Batch 200/938 | Cost: 0.0377\n", "Epoch: 004/010 | Batch 250/938 | Cost: 0.1986\n", "Epoch: 004/010 | Batch 300/938 | Cost: 0.1076\n", "Epoch: 004/010 | Batch 350/938 | Cost: 0.0270\n", "Epoch: 004/010 | Batch 400/938 | Cost: 0.1977\n", "Epoch: 004/010 | Batch 450/938 | Cost: 0.0623\n", "Epoch: 004/010 | Batch 500/938 | Cost: 0.1706\n", "Epoch: 004/010 | Batch 550/938 | Cost: 0.0296\n", "Epoch: 004/010 | Batch 600/938 | Cost: 0.0899\n", "Epoch: 004/010 | Batch 650/938 | Cost: 0.0479\n", "Epoch: 004/010 | Batch 700/938 | Cost: 0.0615\n", "Epoch: 004/010 | Batch 750/938 | Cost: 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0.0050\n", "Epoch: 010/010 | Batch 500/938 | Cost: 0.0548\n", "Epoch: 010/010 | Batch 550/938 | Cost: 0.0269\n", "Epoch: 010/010 | Batch 600/938 | Cost: 0.0378\n", "Epoch: 010/010 | Batch 650/938 | Cost: 0.0120\n", "Epoch: 010/010 | Batch 700/938 | Cost: 0.0298\n", "Epoch: 010/010 | Batch 750/938 | Cost: 0.0781\n", "Epoch: 010/010 | Batch 800/938 | Cost: 0.0251\n", "Epoch: 010/010 | Batch 850/938 | Cost: 0.0693\n", "Epoch: 010/010 | Batch 900/938 | Cost: 0.0499\n", "Epoch: 010/010 training accuracy: 99.61%\n", "Time elapsed: 2.20 min\n", "Total Training Time: 2.20 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", " 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: 97.82%\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": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" }, "toc": { "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }