{
"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 1.0.1.post2\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 -- All-Convolutional Neural Network"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Simple convolutional neural network that uses stride=2 every 2nd convolutional layer, instead of max pooling, to reduce the feature maps. Loosely based on\n",
"\n",
"- Springenberg, Jost Tobias, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller. \"Striving for simplicity: The all convolutional net.\" arXiv preprint arXiv:1412.6806 (2014)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"import numpy as np\n",
"import torch\n",
"import torch.nn.functional as F\n",
"from torchvision import datasets\n",
"from torchvision import transforms\n",
"from torch.utils.data import DataLoader\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([256, 1, 28, 28])\n",
"Image label dimensions: torch.Size([256])\n"
]
}
],
"source": [
"##########################\n",
"### SETTINGS\n",
"##########################\n",
"\n",
"# Device\n",
"device = torch.device(\"cuda:1\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"# Hyperparameters\n",
"random_seed = 1\n",
"learning_rate = 0.001\n",
"num_epochs = 15\n",
"batch_size = 256\n",
"\n",
"# Architecture\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": "markdown",
"metadata": {},
"source": [
"## Model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"##########################\n",
"### MODEL\n",
"##########################\n",
"\n",
"\n",
"class ConvNet(torch.nn.Module):\n",
"\n",
" def __init__(self, num_classes):\n",
" super(ConvNet, self).__init__()\n",
" \n",
" self.num_classes = num_classes\n",
" # calculate same padding:\n",
" # (w - k + 2*p)/s + 1 = o\n",
" # => p = (s(o-1) - w + k)/2\n",
" \n",
" # 28x28x1 => 28x28x4\n",
" self.conv_1 = torch.nn.Conv2d(in_channels=1,\n",
" out_channels=4,\n",
" kernel_size=(3, 3),\n",
" stride=(1, 1),\n",
" padding=1) # (1(28-1) - 28 + 3) / 2 = 1\n",
" # 28x28x4 => 14x14x4\n",
" self.conv_2 = torch.nn.Conv2d(in_channels=4,\n",
" out_channels=4,\n",
" kernel_size=(3, 3),\n",
" stride=(2, 2),\n",
" padding=1) \n",
" # 14x14x4 => 14x14x8\n",
" self.conv_3 = torch.nn.Conv2d(in_channels=4,\n",
" out_channels=8,\n",
" kernel_size=(3, 3),\n",
" stride=(1, 1),\n",
" padding=1) # (1(14-1) - 14 + 3) / 2 = 1 \n",
" # 14x14x8 => 7x7x8 \n",
" self.conv_4 = torch.nn.Conv2d(in_channels=8,\n",
" out_channels=8,\n",
" kernel_size=(3, 3),\n",
" stride=(2, 2),\n",
" padding=1) \n",
" \n",
" # 7x7x8 => 7x7x16 \n",
" self.conv_5 = torch.nn.Conv2d(in_channels=8,\n",
" out_channels=16,\n",
" kernel_size=(3, 3),\n",
" stride=(1, 1),\n",
" padding=1) # (1(7-1) - 7 + 3) / 2 = 1 \n",
" # 7x7x16 => 4x4x16 \n",
" self.conv_6 = torch.nn.Conv2d(in_channels=16,\n",
" out_channels=16,\n",
" kernel_size=(3, 3),\n",
" stride=(2, 2),\n",
" padding=1) \n",
" \n",
" # 4x4x16 => 4x4xnum_classes \n",
" self.conv_7 = torch.nn.Conv2d(in_channels=16,\n",
" out_channels=self.num_classes,\n",
" kernel_size=(3, 3),\n",
" stride=(1, 1),\n",
" padding=1) # (1(7-1) - 7 + 3) / 2 = 1 \n",
"\n",
"\n",
" \n",
" def forward(self, x):\n",
" out = self.conv_1(x)\n",
" out = F.relu(out)\n",
" \n",
" out = self.conv_2(out)\n",
" out = F.relu(out)\n",
"\n",
" out = self.conv_3(out)\n",
" out = F.relu(out)\n",
"\n",
" out = self.conv_4(out)\n",
" out = F.relu(out)\n",
" \n",
" out = self.conv_5(out)\n",
" out = F.relu(out)\n",
" \n",
" out = self.conv_6(out)\n",
" out = F.relu(out)\n",
" \n",
" out = self.conv_7(out)\n",
" out = F.relu(out)\n",
" \n",
" logits = F.adaptive_avg_pool2d(out, 1)\n",
" # drop width\n",
" logits.squeeze_(-1)\n",
" # drop height\n",
" logits.squeeze_(-1)\n",
" probas = torch.softmax(logits, dim=1)\n",
" return logits, probas\n",
"\n",
" \n",
"torch.manual_seed(random_seed)\n",
"model = ConvNet(num_classes=num_classes)\n",
"\n",
"model = model.to(device)\n",
"\n",
"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 001/015 | Batch 000/235 | Cost: 2.3051\n",
"Epoch: 001/015 | Batch 050/235 | Cost: 2.2926\n",
"Epoch: 001/015 | Batch 100/235 | Cost: 2.0812\n",
"Epoch: 001/015 | Batch 150/235 | Cost: 1.4435\n",
"Epoch: 001/015 | Batch 200/235 | Cost: 0.9232\n",
"Epoch: 001/015 training accuracy: 76.06%\n",
"Time elapsed: 0.23 min\n",
"Epoch: 002/015 | Batch 000/235 | Cost: 0.7001\n",
"Epoch: 002/015 | Batch 050/235 | Cost: 0.5710\n",
"Epoch: 002/015 | Batch 100/235 | Cost: 0.5925\n",
"Epoch: 002/015 | Batch 150/235 | Cost: 0.4022\n",
"Epoch: 002/015 | Batch 200/235 | Cost: 0.4663\n",
"Epoch: 002/015 training accuracy: 85.68%\n",
"Time elapsed: 0.45 min\n",
"Epoch: 003/015 | Batch 000/235 | Cost: 0.4332\n",
"Epoch: 003/015 | Batch 050/235 | Cost: 0.3523\n",
"Epoch: 003/015 | Batch 100/235 | Cost: 0.4114\n",
"Epoch: 003/015 | Batch 150/235 | Cost: 0.4587\n",
"Epoch: 003/015 | Batch 200/235 | Cost: 0.4517\n",
"Epoch: 003/015 training accuracy: 89.33%\n",
"Time elapsed: 0.68 min\n",
"Epoch: 004/015 | Batch 000/235 | Cost: 0.4083\n",
"Epoch: 004/015 | Batch 050/235 | Cost: 0.3158\n",
"Epoch: 004/015 | Batch 100/235 | Cost: 0.2728\n",
"Epoch: 004/015 | Batch 150/235 | Cost: 0.3023\n",
"Epoch: 004/015 | Batch 200/235 | Cost: 0.2709\n",
"Epoch: 004/015 training accuracy: 90.40%\n",
"Time elapsed: 0.90 min\n",
"Epoch: 005/015 | Batch 000/235 | Cost: 0.2514\n",
"Epoch: 005/015 | Batch 050/235 | Cost: 0.3704\n",
"Epoch: 005/015 | Batch 100/235 | Cost: 0.2972\n",
"Epoch: 005/015 | Batch 150/235 | Cost: 0.2335\n",
"Epoch: 005/015 | Batch 200/235 | Cost: 0.3242\n",
"Epoch: 005/015 training accuracy: 91.36%\n",
"Time elapsed: 1.13 min\n",
"Epoch: 006/015 | Batch 000/235 | Cost: 0.3255\n",
"Epoch: 006/015 | Batch 050/235 | Cost: 0.2985\n",
"Epoch: 006/015 | Batch 100/235 | Cost: 0.3501\n",
"Epoch: 006/015 | Batch 150/235 | Cost: 0.2415\n",
"Epoch: 006/015 | Batch 200/235 | Cost: 0.1978\n",
"Epoch: 006/015 training accuracy: 92.82%\n",
"Time elapsed: 1.35 min\n",
"Epoch: 007/015 | Batch 000/235 | Cost: 0.1925\n",
"Epoch: 007/015 | Batch 050/235 | Cost: 0.2179\n",
"Epoch: 007/015 | Batch 100/235 | Cost: 0.3337\n",
"Epoch: 007/015 | Batch 150/235 | Cost: 0.1856\n",
"Epoch: 007/015 | Batch 200/235 | Cost: 0.1333\n",
"Epoch: 007/015 training accuracy: 93.68%\n",
"Time elapsed: 1.58 min\n",
"Epoch: 008/015 | Batch 000/235 | Cost: 0.1776\n",
"Epoch: 008/015 | Batch 050/235 | Cost: 0.2973\n",
"Epoch: 008/015 | Batch 100/235 | Cost: 0.1685\n",
"Epoch: 008/015 | Batch 150/235 | Cost: 0.2062\n",
"Epoch: 008/015 | Batch 200/235 | Cost: 0.2165\n",
"Epoch: 008/015 training accuracy: 94.42%\n",
"Time elapsed: 1.80 min\n",
"Epoch: 009/015 | Batch 000/235 | Cost: 0.2038\n",
"Epoch: 009/015 | Batch 050/235 | Cost: 0.1301\n",
"Epoch: 009/015 | Batch 100/235 | Cost: 0.1977\n",
"Epoch: 009/015 | Batch 150/235 | Cost: 0.2160\n",
"Epoch: 009/015 | Batch 200/235 | Cost: 0.1772\n",
"Epoch: 009/015 training accuracy: 94.61%\n",
"Time elapsed: 2.02 min\n",
"Epoch: 010/015 | Batch 000/235 | Cost: 0.1709\n",
"Epoch: 010/015 | Batch 050/235 | Cost: 0.1695\n",
"Epoch: 010/015 | Batch 100/235 | Cost: 0.2144\n",
"Epoch: 010/015 | Batch 150/235 | Cost: 0.1548\n",
"Epoch: 010/015 | Batch 200/235 | Cost: 0.1033\n",
"Epoch: 010/015 training accuracy: 94.90%\n",
"Time elapsed: 2.25 min\n",
"Epoch: 011/015 | Batch 000/235 | Cost: 0.1651\n",
"Epoch: 011/015 | Batch 050/235 | Cost: 0.1899\n",
"Epoch: 011/015 | Batch 100/235 | Cost: 0.1727\n",
"Epoch: 011/015 | Batch 150/235 | Cost: 0.1216\n",
"Epoch: 011/015 | Batch 200/235 | Cost: 0.1859\n",
"Epoch: 011/015 training accuracy: 94.82%\n",
"Time elapsed: 2.47 min\n",
"Epoch: 012/015 | Batch 000/235 | Cost: 0.2490\n",
"Epoch: 012/015 | Batch 050/235 | Cost: 0.1022\n",
"Epoch: 012/015 | Batch 100/235 | Cost: 0.0793\n",
"Epoch: 012/015 | Batch 150/235 | Cost: 0.2258\n",
"Epoch: 012/015 | Batch 200/235 | Cost: 0.1356\n",
"Epoch: 012/015 training accuracy: 95.35%\n",
"Time elapsed: 2.70 min\n",
"Epoch: 013/015 | Batch 000/235 | Cost: 0.1512\n",
"Epoch: 013/015 | Batch 050/235 | Cost: 0.1758\n",
"Epoch: 013/015 | Batch 100/235 | Cost: 0.1349\n",
"Epoch: 013/015 | Batch 150/235 | Cost: 0.1838\n",
"Epoch: 013/015 | Batch 200/235 | Cost: 0.1166\n",
"Epoch: 013/015 training accuracy: 95.61%\n",
"Time elapsed: 2.92 min\n",
"Epoch: 014/015 | Batch 000/235 | Cost: 0.1210\n",
"Epoch: 014/015 | Batch 050/235 | Cost: 0.1511\n",
"Epoch: 014/015 | Batch 100/235 | Cost: 0.1331\n",
"Epoch: 014/015 | Batch 150/235 | Cost: 0.1058\n",
"Epoch: 014/015 | Batch 200/235 | Cost: 0.1340\n",
"Epoch: 014/015 training accuracy: 95.53%\n",
"Time elapsed: 3.15 min\n",
"Epoch: 015/015 | Batch 000/235 | Cost: 0.2342\n",
"Epoch: 015/015 | Batch 050/235 | Cost: 0.1371\n",
"Epoch: 015/015 | Batch 100/235 | Cost: 0.0944\n",
"Epoch: 015/015 | Batch 150/235 | Cost: 0.1102\n",
"Epoch: 015/015 | Batch 200/235 | Cost: 0.1259\n",
"Epoch: 015/015 training accuracy: 96.36%\n",
"Time elapsed: 3.37 min\n",
"Total Training Time: 3.37 min\n"
]
}
],
"source": [
"def compute_accuracy(model, data_loader):\n",
" correct_pred, num_examples = 0, 0\n",
" for features, targets in data_loader:\n",
" features = features.to(device)\n",
" targets = targets.to(device)\n",
" logits, probas = model(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 = model.train()\n",
" for batch_idx, (features, targets) in enumerate(train_loader):\n",
" \n",
" features = features.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",
" model = model.eval()\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": "markdown",
"metadata": {},
"source": [
"## Evaluation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test accuracy: 96.42%\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 1.0.1.post2\n",
"\n"
]
}
],
"source": [
"%watermark -iv"
]
}
],
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