wehub-resource-sync
552 lines
21 KiB
Plaintext
552 lines
21 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
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"- Author: Sebastian Raschka\n",
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"- GitHub Repository: https://github.com/rasbt/deeplearning-models"
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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": 1,
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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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"Sebastian Raschka \n",
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"\n",
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"CPython 3.6.8\n",
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"IPython 7.2.0\n",
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"\n",
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"torch 1.0.0\n"
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]
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}
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],
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"source": [
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"%load_ext watermark\n",
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"%watermark -a 'Sebastian Raschka' -v -p torch"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"- Runs on CPU or GPU (if available)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Model Zoo -- Multilayer Perceptron with BatchNorm"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Imports"
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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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"import time\n",
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"import numpy as np\n",
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"from torchvision import datasets\n",
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"from torchvision import transforms\n",
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"from torch.utils.data import DataLoader\n",
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"import torch.nn.functional as F\n",
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"import torch\n",
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"\n",
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"\n",
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"if torch.cuda.is_available():\n",
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" torch.backends.cudnn.deterministic = True"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Settings and Dataset"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Image batch dimensions: torch.Size([64, 1, 28, 28])\n",
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"Image label dimensions: torch.Size([64])\n"
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]
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}
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],
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"source": [
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"##########################\n",
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"### SETTINGS\n",
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"##########################\n",
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"\n",
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"# Device\n",
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"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
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"\n",
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"# Hyperparameters\n",
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"random_seed = 1\n",
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"learning_rate = 0.1\n",
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"num_epochs = 10\n",
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"batch_size = 64\n",
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"\n",
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"# Architecture\n",
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"num_features = 784\n",
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"num_hidden_1 = 128\n",
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"num_hidden_2 = 256\n",
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"num_classes = 10\n",
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"\n",
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"\n",
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"##########################\n",
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"### MNIST DATASET\n",
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"##########################\n",
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"\n",
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"# Note transforms.ToTensor() scales input images\n",
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"# to 0-1 range\n",
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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())\n",
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"\n",
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"\n",
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"train_loader = 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 = 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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"\n",
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"# Checking the dataset\n",
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"for images, labels in train_loader: \n",
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" print('Image batch dimensions:', images.shape)\n",
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" print('Image label dimensions:', labels.shape)\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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"##########################\n",
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"### MODEL\n",
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"##########################\n",
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"\n",
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"class MultilayerPerceptron(torch.nn.Module):\n",
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"\n",
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" def __init__(self, num_features, num_classes):\n",
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" super(MultilayerPerceptron, self).__init__()\n",
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" \n",
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" ### 1st hidden layer\n",
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" self.linear_1 = torch.nn.Linear(num_features, num_hidden_1)\n",
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" # The following to lones are not necessary, \n",
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" # but used here to demonstrate how to access the weights\n",
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" # and use a different weight initialization.\n",
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" # By default, PyTorch uses Xavier/Glorot initialization, which\n",
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" # should usually be preferred.\n",
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" self.linear_1.weight.detach().normal_(0.0, 0.1)\n",
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" self.linear_1.bias.detach().zero_()\n",
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" self.linear_1_bn = torch.nn.BatchNorm1d(num_hidden_1)\n",
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" \n",
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" ### 2nd hidden layer\n",
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" self.linear_2 = torch.nn.Linear(num_hidden_1, num_hidden_2)\n",
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" self.linear_2.weight.detach().normal_(0.0, 0.1)\n",
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" self.linear_2.bias.detach().zero_()\n",
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" self.linear_2_bn = torch.nn.BatchNorm1d(num_hidden_2)\n",
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" \n",
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" ### Output layer\n",
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" self.linear_out = torch.nn.Linear(num_hidden_2, num_classes)\n",
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" self.linear_out.weight.detach().normal_(0.0, 0.1)\n",
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" self.linear_out.bias.detach().zero_()\n",
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" \n",
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" def forward(self, x):\n",
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" out = self.linear_1(x)\n",
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" # note that batchnorm is in the classic\n",
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" # sense placed before the activation\n",
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" out = self.linear_1_bn(out)\n",
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" out = F.relu(out)\n",
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" \n",
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" out = self.linear_2(out)\n",
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" out = self.linear_2_bn(out)\n",
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" out = F.relu(out)\n",
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" \n",
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" logits = self.linear_out(out)\n",
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" probas = F.softmax(logits, dim=1)\n",
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" return logits, probas\n",
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"\n",
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" \n",
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"torch.manual_seed(random_seed)\n",
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"model = MultilayerPerceptron(num_features=num_features,\n",
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" num_classes=num_classes)\n",
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"\n",
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"model = model.to(device)\n",
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"\n",
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"optimizer = torch.optim.SGD(model.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": 5,
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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: 001/010 | Batch 000/938 | Cost: 2.6465\n",
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"Epoch: 001/010 training accuracy: 95.63%\n",
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"Epoch: 002/010 training accuracy: 96.94%\n",
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"Epoch: 007/010 | Batch 900/938 | Cost: 0.0172\n",
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"Epoch: 007/010 training accuracy: 99.31%\n",
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"Time elapsed: 1.55 min\n",
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"Epoch: 008/010 | Batch 000/938 | Cost: 0.0331\n",
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"Epoch: 008/010 | Batch 050/938 | Cost: 0.0113\n",
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"Epoch: 008/010 | Batch 100/938 | Cost: 0.0890\n",
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"Epoch: 008/010 | Batch 150/938 | Cost: 0.0309\n",
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"Epoch: 008/010 | Batch 200/938 | Cost: 0.0391\n",
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"Epoch: 008/010 | Batch 250/938 | Cost: 0.0567\n",
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"Epoch: 008/010 | Batch 300/938 | Cost: 0.0330\n",
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"Epoch: 008/010 | Batch 350/938 | Cost: 0.0342\n",
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"Epoch: 008/010 | Batch 400/938 | Cost: 0.0904\n",
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"Epoch: 008/010 | Batch 500/938 | Cost: 0.0359\n",
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"Epoch: 008/010 | Batch 700/938 | Cost: 0.0986\n",
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"Epoch: 008/010 | Batch 850/938 | Cost: 0.0095\n",
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"Epoch: 008/010 training accuracy: 99.36%\n",
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"Time elapsed: 1.76 min\n",
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"Epoch: 009/010 | Batch 000/938 | Cost: 0.0491\n",
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"Epoch: 009/010 | Batch 900/938 | Cost: 0.0147\n",
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"Epoch: 009/010 training accuracy: 99.46%\n",
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"Time elapsed: 1.98 min\n",
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"Epoch: 010/010 | Batch 000/938 | Cost: 0.0172\n",
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"Epoch: 010/010 | Batch 650/938 | Cost: 0.0120\n",
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"Epoch: 010/010 | Batch 700/938 | Cost: 0.0298\n",
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"Epoch: 010/010 | Batch 750/938 | Cost: 0.0781\n",
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"Epoch: 010/010 | Batch 800/938 | Cost: 0.0251\n",
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"Epoch: 010/010 | Batch 850/938 | Cost: 0.0693\n",
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"Epoch: 010/010 | Batch 900/938 | Cost: 0.0499\n",
|
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"Epoch: 010/010 training accuracy: 99.61%\n",
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"Time elapsed: 2.20 min\n",
|
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"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
|
|
}
|