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wehub-resource-sync b2987b63e6 chore: import upstream snapshot with attribution
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{
"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 3.7.3\n",
"IPython 7.6.1\n",
"\n",
"torch 1.2.0\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": [
"# Deep Convolutional GAN"
]
},
{
"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",
"import torch.nn as nn\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([128, 1, 28, 28])\n",
"Image label dimensions: torch.Size([128])\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 = 42\n",
"generator_learning_rate = 0.0001\n",
"discriminator_learning_rate = 0.0001\n",
"NUM_EPOCHS = 100\n",
"BATCH_SIZE = 128\n",
"LATENT_DIM = 100\n",
"IMG_SHAPE = (1, 28, 28)\n",
"IMG_SIZE = 1\n",
"for x in IMG_SHAPE:\n",
" IMG_SIZE *= x\n",
"\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",
" num_workers=4,\n",
" shuffle=True)\n",
"\n",
"test_loader = DataLoader(dataset=test_dataset, \n",
" batch_size=BATCH_SIZE,\n",
" num_workers=4,\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",
"class Flatten(nn.Module):\n",
" def forward(self, input):\n",
" return input.view(input.size(0), -1)\n",
" \n",
"class Reshape1(nn.Module):\n",
" def forward(self, input):\n",
" return input.view(input.size(0), 64, 7, 7)\n",
"\n",
"\n",
"class GAN(torch.nn.Module):\n",
"\n",
" def __init__(self):\n",
" super(GAN, self).__init__()\n",
" \n",
" \n",
" self.generator = nn.Sequential(\n",
" \n",
" nn.Linear(LATENT_DIM, 3136, bias=False),\n",
" nn.BatchNorm1d(num_features=3136),\n",
" nn.LeakyReLU(inplace=True, negative_slope=0.0001),\n",
" Reshape1(),\n",
" \n",
" nn.ConvTranspose2d(in_channels=64, out_channels=32, kernel_size=(3, 3), stride=(2, 2), padding=1, bias=False),\n",
" nn.BatchNorm2d(num_features=32),\n",
" nn.LeakyReLU(inplace=True, negative_slope=0.0001),\n",
" #nn.Dropout2d(p=0.2),\n",
" \n",
" nn.ConvTranspose2d(in_channels=32, out_channels=16, kernel_size=(3, 3), stride=(2, 2), padding=1, bias=False),\n",
" nn.BatchNorm2d(num_features=16),\n",
" nn.LeakyReLU(inplace=True, negative_slope=0.0001),\n",
" #nn.Dropout2d(p=0.2),\n",
" \n",
" nn.ConvTranspose2d(in_channels=16, out_channels=8, kernel_size=(3, 3), stride=(1, 1), padding=0, bias=False),\n",
" nn.BatchNorm2d(num_features=8),\n",
" nn.LeakyReLU(inplace=True, negative_slope=0.0001),\n",
" #nn.Dropout2d(p=0.2),\n",
" \n",
" nn.ConvTranspose2d(in_channels=8, out_channels=1, kernel_size=(2, 2), stride=(1, 1), padding=0, bias=False),\n",
" nn.Tanh()\n",
" )\n",
" \n",
" self.discriminator = nn.Sequential(\n",
" nn.Conv2d(in_channels=1, out_channels=8, padding=1, kernel_size=(3, 3), stride=(2, 2), bias=False),\n",
" nn.BatchNorm2d(num_features=8),\n",
" nn.LeakyReLU(inplace=True, negative_slope=0.0001), \n",
" #nn.Dropout2d(p=0.2),\n",
" \n",
" nn.Conv2d(in_channels=8, out_channels=32, padding=1, kernel_size=(3, 3), stride=(2, 2), bias=False),\n",
" nn.BatchNorm2d(num_features=32),\n",
" nn.LeakyReLU(inplace=True, negative_slope=0.0001), \n",
" #nn.Dropout2d(p=0.2),\n",
" \n",
" Flatten(),\n",
"\n",
" nn.Linear(7*7*32, 1),\n",
" #nn.Sigmoid()\n",
" )\n",
"\n",
" \n",
" def generator_forward(self, z):\n",
" img = self.generator(z)\n",
" return img\n",
" \n",
" def discriminator_forward(self, img):\n",
" pred = model.discriminator(img)\n",
" return pred.view(-1)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GAN(\n",
" (generator): Sequential(\n",
" (0): Linear(in_features=100, out_features=3136, bias=False)\n",
" (1): BatchNorm1d(3136, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): LeakyReLU(negative_slope=0.0001, inplace=True)\n",
" (3): Reshape1()\n",
" (4): ConvTranspose2d(64, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (5): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (6): LeakyReLU(negative_slope=0.0001, inplace=True)\n",
" (7): ConvTranspose2d(32, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (8): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (9): LeakyReLU(negative_slope=0.0001, inplace=True)\n",
" (10): ConvTranspose2d(16, 8, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
" (11): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (12): LeakyReLU(negative_slope=0.0001, inplace=True)\n",
" (13): ConvTranspose2d(8, 1, kernel_size=(2, 2), stride=(1, 1), bias=False)\n",
" (14): Tanh()\n",
" )\n",
" (discriminator): Sequential(\n",
" (0): Conv2d(1, 8, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): LeakyReLU(negative_slope=0.0001, inplace=True)\n",
" (3): Conv2d(8, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (5): LeakyReLU(negative_slope=0.0001, inplace=True)\n",
" (6): Flatten()\n",
" (7): Linear(in_features=1568, out_features=1, bias=True)\n",
" )\n",
")\n"
]
}
],
"source": [
"torch.manual_seed(random_seed)\n",
"\n",
"#del model\n",
"model = GAN()\n",
"model = model.to(device)\n",
"\n",
"print(model)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\noutputs = []\\ndef hook(module, input, output):\\n outputs.append(output)\\n\\n#for i, layer in enumerate(model.discriminator):\\n# if isinstance(layer, torch.nn.modules.conv.Conv2d):\\n# model.discriminator[i].register_forward_hook(hook)\\n\\nfor i, layer in enumerate(model.generator):\\n if isinstance(layer, torch.nn.modules.ConvTranspose2d):\\n model.generator[i].register_forward_hook(hook)\\n'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### ## FOR DEBUGGING\n",
"\n",
"\"\"\"\n",
"outputs = []\n",
"def hook(module, input, output):\n",
" outputs.append(output)\n",
"\n",
"#for i, layer in enumerate(model.discriminator):\n",
"# if isinstance(layer, torch.nn.modules.conv.Conv2d):\n",
"# model.discriminator[i].register_forward_hook(hook)\n",
"\n",
"for i, layer in enumerate(model.generator):\n",
" if isinstance(layer, torch.nn.modules.ConvTranspose2d):\n",
" model.generator[i].register_forward_hook(hook)\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"optim_gener = torch.optim.Adam(model.generator.parameters(), lr=generator_learning_rate)\n",
"optim_discr = torch.optim.Adam(model.discriminator.parameters(), lr=discriminator_learning_rate)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Time elapsed: 18.47 min\n",
"Total Training Time: 18.47 min\n"
]
}
],
"source": [
"start_time = time.time() \n",
"\n",
"discr_costs = []\n",
"gener_costs = []\n",
"for epoch in range(NUM_EPOCHS):\n",
" model = model.train()\n",
" for batch_idx, (features, targets) in enumerate(train_loader):\n",
"\n",
" \n",
" # Normalize images to [-1, 1] range\n",
" features = (features - 0.5)*2.\n",
" features = features.view(-1, IMG_SIZE).to(device) \n",
"\n",
" targets = targets.to(device)\n",
"\n",
" valid = torch.ones(targets.size(0)).float().to(device)\n",
" fake = torch.zeros(targets.size(0)).float().to(device)\n",
" \n",
"\n",
" ### FORWARD AND BACK PROP\n",
" \n",
" \n",
" # --------------------------\n",
" # Train Generator\n",
" # --------------------------\n",
" \n",
" # Make new images\n",
" z = torch.zeros((targets.size(0), LATENT_DIM)).uniform_(-1.0, 1.0).to(device)\n",
" generated_features = model.generator_forward(z)\n",
" \n",
" # Loss for fooling the discriminator\n",
" discr_pred = model.discriminator_forward(generated_features.view(targets.size(0), 1, 28, 28))\n",
" \n",
" gener_loss = F.binary_cross_entropy_with_logits(discr_pred, valid)\n",
" \n",
" optim_gener.zero_grad()\n",
" gener_loss.backward()\n",
" optim_gener.step()\n",
" \n",
" # --------------------------\n",
" # Train Discriminator\n",
" # -------------------------- \n",
" \n",
" discr_pred_real = model.discriminator_forward(features.view(targets.size(0), 1, 28, 28))\n",
" real_loss = F.binary_cross_entropy_with_logits(discr_pred_real, valid)\n",
" \n",
" discr_pred_fake = model.discriminator_forward(generated_features.view(targets.size(0), 1, 28, 28).detach())\n",
" fake_loss = F.binary_cross_entropy_with_logits(discr_pred_fake, fake)\n",
" \n",
" discr_loss = 0.5*(real_loss + fake_loss)\n",
"\n",
" optim_discr.zero_grad()\n",
" discr_loss.backward()\n",
" optim_discr.step() \n",
" \n",
" discr_costs.append(discr_loss.item())\n",
" gener_costs.append(gener_loss.item())\n",
" \n",
" \n",
" ### LOGGING\n",
" if not batch_idx % 100:\n",
" print ('Epoch: %03d/%03d | Batch %03d/%03d | Gen/Dis Loss: %.4f/%.4f' \n",
" %(epoch+1, NUM_EPOCHS, batch_idx, \n",
" len(train_loader), gener_loss, discr_loss))\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": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\nfor i in outputs:\\n print(i.size())\\n'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### For Debugging\n",
"\n",
"\"\"\"\n",
"for i in outputs:\n",
" print(i.size())\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluation"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax1 = plt.subplot(1, 1, 1)\n",
"ax1.plot(range(len(gener_costs)), gener_costs, label='Generator loss')\n",
"ax1.plot(range(len(discr_costs)), discr_costs, label='Discriminator loss')\n",
"ax1.set_xlabel('Iterations')\n",
"ax1.set_ylabel('Loss')\n",
"ax1.legend()\n",
"\n",
"###################\n",
"# Set scond x-axis\n",
"ax2 = ax1.twiny()\n",
"newlabel = list(range(NUM_EPOCHS+1))\n",
"iter_per_epoch = len(train_loader)\n",
"newpos = [e*iter_per_epoch for e in newlabel]\n",
"\n",
"ax2.set_xticklabels(newlabel[::10])\n",
"ax2.set_xticks(newpos[::10])\n",
"\n",
"ax2.xaxis.set_ticks_position('bottom')\n",
"ax2.xaxis.set_label_position('bottom')\n",
"ax2.spines['bottom'].set_position(('outward', 45))\n",
"ax2.set_xlabel('Epochs')\n",
"ax2.set_xlim(ax1.get_xlim())\n",
"###################\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x180 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"##########################\n",
"### VISUALIZATION\n",
"##########################\n",
"\n",
"\n",
"model.eval()\n",
"# Make new images\n",
"z = torch.zeros((5, LATENT_DIM)).uniform_(-1.0, 1.0).to(device)\n",
"generated_features = model.generator_forward(z)\n",
"imgs = generated_features.view(-1, 28, 28)\n",
"\n",
"fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(20, 2.5))\n",
"\n",
"\n",
"for i, ax in enumerate(axes):\n",
" axes[i].imshow(imgs[i].to(torch.device('cpu')).detach(), cmap='binary')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------------------------------------------------------\n",
" Layer (type) Output Shape Param #\n",
"================================================================\n",
" Linear-1 [-1, 3136] 313,600\n",
" BatchNorm1d-2 [-1, 3136] 6,272\n",
" LeakyReLU-3 [-1, 3136] 0\n",
" Reshape1-4 [-1, 64, 7, 7] 0\n",
" ConvTranspose2d-5 [-1, 32, 13, 13] 18,432\n",
" BatchNorm2d-6 [-1, 32, 13, 13] 64\n",
" LeakyReLU-7 [-1, 32, 13, 13] 0\n",
" ConvTranspose2d-8 [-1, 16, 25, 25] 4,608\n",
" BatchNorm2d-9 [-1, 16, 25, 25] 32\n",
" LeakyReLU-10 [-1, 16, 25, 25] 0\n",
" ConvTranspose2d-11 [-1, 8, 27, 27] 1,152\n",
" BatchNorm2d-12 [-1, 8, 27, 27] 16\n",
" LeakyReLU-13 [-1, 8, 27, 27] 0\n",
" ConvTranspose2d-14 [-1, 1, 28, 28] 32\n",
" Tanh-15 [-1, 1, 28, 28] 0\n",
"================================================================\n",
"Total params: 344,208\n",
"Trainable params: 344,208\n",
"Non-trainable params: 0\n",
"----------------------------------------------------------------\n",
"Input size (MB): 0.00\n",
"Forward/backward pass size (MB): 0.59\n",
"Params size (MB): 1.31\n",
"Estimated Total Size (MB): 1.91\n",
"----------------------------------------------------------------\n",
"----------------------------------------------------------------\n",
" Layer (type) Output Shape Param #\n",
"================================================================\n",
" Conv2d-1 [-1, 8, 14, 14] 72\n",
" BatchNorm2d-2 [-1, 8, 14, 14] 16\n",
" LeakyReLU-3 [-1, 8, 14, 14] 0\n",
" Conv2d-4 [-1, 32, 7, 7] 2,304\n",
" BatchNorm2d-5 [-1, 32, 7, 7] 64\n",
" LeakyReLU-6 [-1, 32, 7, 7] 0\n",
" Flatten-7 [-1, 1568] 0\n",
" Linear-8 [-1, 1] 1,569\n",
"================================================================\n",
"Total params: 4,025\n",
"Trainable params: 4,025\n",
"Non-trainable params: 0\n",
"----------------------------------------------------------------\n",
"Input size (MB): 0.00\n",
"Forward/backward pass size (MB): 0.08\n",
"Params size (MB): 0.02\n",
"Estimated Total Size (MB): 0.10\n",
"----------------------------------------------------------------\n"
]
}
],
"source": [
"from torchsummary import summary\n",
"model = model.to('cuda:0')\n",
"summary(model.generator, input_size=(100,))\n",
"summary(model.discriminator, input_size=(1, 28, 28))"
]
}
],
"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": "3.7.3"
},
"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": 4
}
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