#!/usr/bin/env python # coding: utf-8 # Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks. # - Author: Sebastian Raschka # - GitHub Repository: https://github.com/rasbt/deeplearning-models # In[1]: get_ipython().run_line_magic('load_ext', 'watermark') get_ipython().run_line_magic('watermark', "-a 'Sebastian Raschka' -v -p torch") # # Network in Network CIFAR-10 Classifier # based on # # - Lin, Min, Qiang Chen, and Shuicheng Yan. "Network in network." arXiv preprint arXiv:1312.4400 (2013). # ## Imports # In[2]: import os import time import numpy as np import pandas as pd import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.data import DataLoader from torch.utils.data import Subset from torchvision import datasets from torchvision import transforms import matplotlib.pyplot as plt from PIL import Image if torch.cuda.is_available(): torch.backends.cudnn.deterministic = True # In[10]: torch.tensor([1]).device # ## Model Settings # In[3]: ########################## ### SETTINGS ########################## # Hyperparameters RANDOM_SEED = 1 LEARNING_RATE = 0.0005 BATCH_SIZE = 256 NUM_EPOCHS = 100 # Architecture NUM_CLASSES = 10 # Other DEVICE = "cuda:3" GRAYSCALE = False # In[4]: ########################## ### CIFAR-10 Dataset ########################## # Note transforms.ToTensor() scales input images # to 0-1 range train_indices = torch.arange(0, 49000) valid_indices = torch.arange(49000, 50000) train_and_valid = datasets.CIFAR10(root='data', train=True, transform=transforms.ToTensor(), download=True) train_dataset = Subset(train_and_valid, train_indices) valid_dataset = Subset(train_and_valid, valid_indices) test_dataset = datasets.CIFAR10(root='data', train=False, transform=transforms.ToTensor()) ##################################################### ### Data Loaders ##################################################### train_loader = DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, num_workers=8, shuffle=True) valid_loader = DataLoader(dataset=valid_dataset, batch_size=BATCH_SIZE, num_workers=8, shuffle=False) test_loader = DataLoader(dataset=test_dataset, batch_size=BATCH_SIZE, num_workers=8, shuffle=False) ##################################################### # Checking the dataset for images, labels in train_loader: print('Image batch dimensions:', images.shape) print('Image label dimensions:', labels.shape) break for images, labels in test_loader: print('Image batch dimensions:', images.shape) print('Image label dimensions:', labels.shape) break for images, labels in valid_loader: print('Image batch dimensions:', images.shape) print('Image label dimensions:', labels.shape) break # In[5]: ########################## ### MODEL ########################## class NiN(nn.Module): def __init__(self, num_classes): super(NiN, self).__init__() self.num_classes = num_classes self.classifier = nn.Sequential( nn.Conv2d(3, 192, kernel_size=5, stride=1, padding=2), nn.ReLU(inplace=True), nn.Conv2d(192, 160, kernel_size=1, stride=1, padding=0), nn.ReLU(inplace=True), nn.Conv2d(160, 96, kernel_size=1, stride=1, padding=0), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2, padding=1), nn.Dropout(0.5), nn.Conv2d(96, 192, kernel_size=5, stride=1, padding=2), nn.ReLU(inplace=True), nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0), nn.ReLU(inplace=True), nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0), nn.ReLU(inplace=True), nn.AvgPool2d(kernel_size=3, stride=2, padding=1), nn.Dropout(0.5), nn.Conv2d(192, 192, kernel_size=3, stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0), nn.ReLU(inplace=True), nn.Conv2d(192, 10, kernel_size=1, stride=1, padding=0), nn.ReLU(inplace=True), nn.AvgPool2d(kernel_size=8, stride=1, padding=0), ) def forward(self, x): x = self.classifier(x) logits = x.view(x.size(0), self.num_classes) probas = torch.softmax(logits, dim=1) return logits, probas # ## Training without Pinned Memory # In[6]: torch.manual_seed(RANDOM_SEED) model = NiN(NUM_CLASSES) model.to(DEVICE) optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE) # In[7]: def compute_accuracy(model, data_loader, device): correct_pred, num_examples = 0, 0 for i, (features, targets) in enumerate(data_loader): features = features.to(device) targets = targets.to(device) logits, probas = model(features) _, predicted_labels = torch.max(probas, 1) num_examples += targets.size(0) correct_pred += (predicted_labels == targets).sum() return correct_pred.float()/num_examples * 100 start_time = time.time() for epoch in range(NUM_EPOCHS): model.train() for batch_idx, (features, targets) in enumerate(train_loader): ### PREPARE MINIBATCH features = features.to(DEVICE) targets = targets.to(DEVICE) ### FORWARD AND BACK PROP logits, probas = model(features) cost = F.cross_entropy(logits, targets) optimizer.zero_grad() cost.backward() ### UPDATE MODEL PARAMETERS optimizer.step() ### LOGGING if not batch_idx % 120: print (f'Epoch: {epoch+1:03d}/{NUM_EPOCHS:03d} | ' f'Batch {batch_idx:03d}/{len(train_loader):03d} |' f' Cost: {cost:.4f}') # no need to build the computation graph for backprop when computing accuracy with torch.set_grad_enabled(False): train_acc = compute_accuracy(model, train_loader, device=DEVICE) valid_acc = compute_accuracy(model, valid_loader, device=DEVICE) print(f'Epoch: {epoch+1:03d}/{NUM_EPOCHS:03d} Train Acc.: {train_acc:.2f}%' f' | Validation Acc.: {valid_acc:.2f}%') elapsed = (time.time() - start_time)/60 print(f'Time elapsed: {elapsed:.2f} min') elapsed = (time.time() - start_time)/60 print(f'Total Training Time: {elapsed:.2f} min') # In[8]: get_ipython().run_line_magic('watermark', '-iv')