wehub-resource-sync
938 lines
36 KiB
Plaintext
938 lines
36 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "UEBilEjLj5wY"
|
|
},
|
|
"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": {
|
|
"colab": {
|
|
"autoexec": {
|
|
"startup": false,
|
|
"wait_interval": 0
|
|
},
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 119
|
|
},
|
|
"colab_type": "code",
|
|
"executionInfo": {
|
|
"elapsed": 536,
|
|
"status": "ok",
|
|
"timestamp": 1524974472601,
|
|
"user": {
|
|
"displayName": "Sebastian Raschka",
|
|
"photoUrl": "//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg",
|
|
"userId": "118404394130788869227"
|
|
},
|
|
"user_tz": 240
|
|
},
|
|
"id": "GOzuY8Yvj5wb",
|
|
"outputId": "c19362ce-f87a-4cc2-84cc-8d7b4b9e6007"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sebastian Raschka \n",
|
|
"\n",
|
|
"CPython 3.7.3\n",
|
|
"IPython 7.9.0\n",
|
|
"\n",
|
|
"torch 1.3.0\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%load_ext watermark\n",
|
|
"%watermark -a 'Sebastian Raschka' -v -p torch"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "rH4XmErYj5wm"
|
|
},
|
|
"source": [
|
|
"# Network in Network CIFAR-10 Classifier"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"based on \n",
|
|
"\n",
|
|
"- Lin, Min, Qiang Chen, and Shuicheng Yan. \"Network in network.\" arXiv preprint arXiv:1312.4400 (2013)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "MkoGLH_Tj5wn"
|
|
},
|
|
"source": [
|
|
"## Imports"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {
|
|
"colab": {
|
|
"autoexec": {
|
|
"startup": false,
|
|
"wait_interval": 0
|
|
}
|
|
},
|
|
"colab_type": "code",
|
|
"id": "ORj09gnrj5wp"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import os\n",
|
|
"import time\n",
|
|
"\n",
|
|
"import numpy as np\n",
|
|
"import pandas as pd\n",
|
|
"\n",
|
|
"import torch\n",
|
|
"import torch.nn as nn\n",
|
|
"import torch.nn.functional as F\n",
|
|
"from torch.utils.data import DataLoader\n",
|
|
"from torch.utils.data import Subset\n",
|
|
"\n",
|
|
"from torchvision import datasets\n",
|
|
"from torchvision import transforms\n",
|
|
"\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"from PIL import Image\n",
|
|
"\n",
|
|
"\n",
|
|
"if torch.cuda.is_available():\n",
|
|
" torch.backends.cudnn.deterministic = True"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"device(type='cpu')"
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"torch.tensor([1]).device"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "I6hghKPxj5w0"
|
|
},
|
|
"source": [
|
|
"## Model Settings"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {
|
|
"colab": {
|
|
"autoexec": {
|
|
"startup": false,
|
|
"wait_interval": 0
|
|
},
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 85
|
|
},
|
|
"colab_type": "code",
|
|
"executionInfo": {
|
|
"elapsed": 23936,
|
|
"status": "ok",
|
|
"timestamp": 1524974497505,
|
|
"user": {
|
|
"displayName": "Sebastian Raschka",
|
|
"photoUrl": "//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg",
|
|
"userId": "118404394130788869227"
|
|
},
|
|
"user_tz": 240
|
|
},
|
|
"id": "NnT0sZIwj5wu",
|
|
"outputId": "55aed925-d17e-4c6a-8c71-0d9b3bde5637"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"##########################\n",
|
|
"### SETTINGS\n",
|
|
"##########################\n",
|
|
"\n",
|
|
"# Hyperparameters\n",
|
|
"RANDOM_SEED = 1\n",
|
|
"LEARNING_RATE = 0.0005\n",
|
|
"BATCH_SIZE = 256\n",
|
|
"NUM_EPOCHS = 100\n",
|
|
"\n",
|
|
"# Architecture\n",
|
|
"NUM_CLASSES = 10\n",
|
|
"\n",
|
|
"# Other\n",
|
|
"DEVICE = \"cuda:3\"\n",
|
|
"GRAYSCALE = False"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Files already downloaded and verified\n",
|
|
"Image batch dimensions: torch.Size([256, 3, 32, 32])\n",
|
|
"Image label dimensions: torch.Size([256])\n",
|
|
"Image batch dimensions: torch.Size([256, 3, 32, 32])\n",
|
|
"Image label dimensions: torch.Size([256])\n",
|
|
"Image batch dimensions: torch.Size([256, 3, 32, 32])\n",
|
|
"Image label dimensions: torch.Size([256])\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"##########################\n",
|
|
"### CIFAR-10 Dataset\n",
|
|
"##########################\n",
|
|
"\n",
|
|
"\n",
|
|
"# Note transforms.ToTensor() scales input images\n",
|
|
"# to 0-1 range\n",
|
|
"\n",
|
|
"\n",
|
|
"train_indices = torch.arange(0, 49000)\n",
|
|
"valid_indices = torch.arange(49000, 50000)\n",
|
|
"\n",
|
|
"\n",
|
|
"train_and_valid = datasets.CIFAR10(root='data', \n",
|
|
" train=True, \n",
|
|
" transform=transforms.ToTensor(),\n",
|
|
" download=True)\n",
|
|
"\n",
|
|
"train_dataset = Subset(train_and_valid, train_indices)\n",
|
|
"valid_dataset = Subset(train_and_valid, valid_indices)\n",
|
|
"\n",
|
|
"\n",
|
|
"test_dataset = datasets.CIFAR10(root='data', \n",
|
|
" train=False, \n",
|
|
" transform=transforms.ToTensor())\n",
|
|
"\n",
|
|
"\n",
|
|
"#####################################################\n",
|
|
"### Data Loaders\n",
|
|
"#####################################################\n",
|
|
"\n",
|
|
"train_loader = DataLoader(dataset=train_dataset, \n",
|
|
" batch_size=BATCH_SIZE,\n",
|
|
" num_workers=8,\n",
|
|
" shuffle=True)\n",
|
|
"\n",
|
|
"valid_loader = DataLoader(dataset=valid_dataset, \n",
|
|
" batch_size=BATCH_SIZE,\n",
|
|
" num_workers=8,\n",
|
|
" shuffle=False)\n",
|
|
"\n",
|
|
"test_loader = DataLoader(dataset=test_dataset, \n",
|
|
" batch_size=BATCH_SIZE,\n",
|
|
" num_workers=8,\n",
|
|
" shuffle=False)\n",
|
|
"\n",
|
|
"#####################################################\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\n",
|
|
"\n",
|
|
"for images, labels in test_loader: \n",
|
|
" print('Image batch dimensions:', images.shape)\n",
|
|
" print('Image label dimensions:', labels.shape)\n",
|
|
" break\n",
|
|
" \n",
|
|
"for images, labels in valid_loader: \n",
|
|
" print('Image batch dimensions:', images.shape)\n",
|
|
" print('Image label dimensions:', labels.shape)\n",
|
|
" break"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"##########################\n",
|
|
"### MODEL\n",
|
|
"##########################\n",
|
|
"\n",
|
|
"\n",
|
|
"class NiN(nn.Module):\n",
|
|
" def __init__(self, num_classes):\n",
|
|
" super(NiN, self).__init__()\n",
|
|
" self.num_classes = num_classes\n",
|
|
" self.classifier = nn.Sequential(\n",
|
|
" nn.Conv2d(3, 192, kernel_size=5, stride=1, padding=2),\n",
|
|
" nn.ReLU(inplace=True),\n",
|
|
" nn.Conv2d(192, 160, kernel_size=1, stride=1, padding=0),\n",
|
|
" nn.ReLU(inplace=True),\n",
|
|
" nn.Conv2d(160, 96, kernel_size=1, stride=1, padding=0),\n",
|
|
" nn.ReLU(inplace=True),\n",
|
|
" nn.MaxPool2d(kernel_size=3, stride=2, padding=1),\n",
|
|
" nn.Dropout(0.5),\n",
|
|
"\n",
|
|
" nn.Conv2d(96, 192, kernel_size=5, stride=1, padding=2),\n",
|
|
" nn.ReLU(inplace=True),\n",
|
|
" nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0),\n",
|
|
" nn.ReLU(inplace=True),\n",
|
|
" nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0),\n",
|
|
" nn.ReLU(inplace=True),\n",
|
|
" nn.AvgPool2d(kernel_size=3, stride=2, padding=1),\n",
|
|
" nn.Dropout(0.5),\n",
|
|
"\n",
|
|
" nn.Conv2d(192, 192, kernel_size=3, stride=1, padding=1),\n",
|
|
" nn.ReLU(inplace=True),\n",
|
|
" nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0),\n",
|
|
" nn.ReLU(inplace=True),\n",
|
|
" nn.Conv2d(192, 10, kernel_size=1, stride=1, padding=0),\n",
|
|
" nn.ReLU(inplace=True),\n",
|
|
" nn.AvgPool2d(kernel_size=8, stride=1, padding=0),\n",
|
|
"\n",
|
|
" )\n",
|
|
"\n",
|
|
" def forward(self, x):\n",
|
|
" x = self.classifier(x)\n",
|
|
" logits = x.view(x.size(0), self.num_classes)\n",
|
|
" probas = torch.softmax(logits, dim=1)\n",
|
|
" return logits, probas"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "RAodboScj5w6"
|
|
},
|
|
"source": [
|
|
"## Training without Pinned Memory"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {
|
|
"colab": {
|
|
"autoexec": {
|
|
"startup": false,
|
|
"wait_interval": 0
|
|
}
|
|
},
|
|
"colab_type": "code",
|
|
"id": "_lza9t_uj5w1"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"torch.manual_seed(RANDOM_SEED)\n",
|
|
"\n",
|
|
"model = NiN(NUM_CLASSES)\n",
|
|
"model.to(DEVICE)\n",
|
|
"\n",
|
|
"optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE) "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {
|
|
"colab": {
|
|
"autoexec": {
|
|
"startup": false,
|
|
"wait_interval": 0
|
|
},
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 1547
|
|
},
|
|
"colab_type": "code",
|
|
"executionInfo": {
|
|
"elapsed": 2384585,
|
|
"status": "ok",
|
|
"timestamp": 1524976888520,
|
|
"user": {
|
|
"displayName": "Sebastian Raschka",
|
|
"photoUrl": "//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg",
|
|
"userId": "118404394130788869227"
|
|
},
|
|
"user_tz": 240
|
|
},
|
|
"id": "Dzh3ROmRj5w7",
|
|
"outputId": "5f8fd8c9-b076-403a-b0b7-fd2d498b48d7"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch: 001/100 | Batch 000/192 | Cost: 2.3043\n",
|
|
"Epoch: 001/100 | Batch 120/192 | Cost: 2.0653\n",
|
|
"Epoch: 001/100 Train Acc.: 24.69% | Validation Acc.: 24.50%\n",
|
|
"Time elapsed: 0.32 min\n",
|
|
"Epoch: 002/100 | Batch 000/192 | Cost: 1.8584\n",
|
|
"Epoch: 002/100 | Batch 120/192 | Cost: 1.7447\n",
|
|
"Epoch: 002/100 Train Acc.: 36.51% | Validation Acc.: 36.90%\n",
|
|
"Time elapsed: 0.64 min\n",
|
|
"Epoch: 003/100 | Batch 000/192 | Cost: 1.6050\n",
|
|
"Epoch: 003/100 | Batch 120/192 | Cost: 1.5591\n",
|
|
"Epoch: 003/100 Train Acc.: 40.50% | Validation Acc.: 37.50%\n",
|
|
"Time elapsed: 0.97 min\n",
|
|
"Epoch: 004/100 | Batch 000/192 | Cost: 1.5428\n",
|
|
"Epoch: 004/100 | Batch 120/192 | Cost: 1.4454\n",
|
|
"Epoch: 004/100 Train Acc.: 46.12% | Validation Acc.: 45.80%\n",
|
|
"Time elapsed: 1.30 min\n",
|
|
"Epoch: 005/100 | Batch 000/192 | Cost: 1.4038\n",
|
|
"Epoch: 005/100 | Batch 120/192 | Cost: 1.4141\n",
|
|
"Epoch: 005/100 Train Acc.: 50.21% | Validation Acc.: 49.90%\n",
|
|
"Time elapsed: 1.63 min\n",
|
|
"Epoch: 006/100 | Batch 000/192 | Cost: 1.3475\n",
|
|
"Epoch: 006/100 | Batch 120/192 | Cost: 1.2627\n",
|
|
"Epoch: 006/100 Train Acc.: 52.66% | Validation Acc.: 54.40%\n",
|
|
"Time elapsed: 1.96 min\n",
|
|
"Epoch: 007/100 | Batch 000/192 | Cost: 1.3238\n",
|
|
"Epoch: 007/100 | Batch 120/192 | Cost: 1.2220\n",
|
|
"Epoch: 007/100 Train Acc.: 54.42% | Validation Acc.: 54.40%\n",
|
|
"Time elapsed: 2.29 min\n",
|
|
"Epoch: 008/100 | Batch 000/192 | Cost: 1.2009\n",
|
|
"Epoch: 008/100 | Batch 120/192 | Cost: 1.2045\n",
|
|
"Epoch: 008/100 Train Acc.: 55.81% | Validation Acc.: 55.30%\n",
|
|
"Time elapsed: 2.63 min\n",
|
|
"Epoch: 009/100 | Batch 000/192 | Cost: 1.2797\n",
|
|
"Epoch: 009/100 | Batch 120/192 | Cost: 1.1397\n",
|
|
"Epoch: 009/100 Train Acc.: 59.10% | Validation Acc.: 60.60%\n",
|
|
"Time elapsed: 2.97 min\n",
|
|
"Epoch: 010/100 | Batch 000/192 | Cost: 1.0562\n",
|
|
"Epoch: 010/100 | Batch 120/192 | Cost: 1.1625\n",
|
|
"Epoch: 010/100 Train Acc.: 59.79% | Validation Acc.: 60.90%\n",
|
|
"Time elapsed: 3.32 min\n",
|
|
"Epoch: 011/100 | Batch 000/192 | Cost: 1.0868\n",
|
|
"Epoch: 011/100 | Batch 120/192 | Cost: 1.0636\n",
|
|
"Epoch: 011/100 Train Acc.: 60.43% | Validation Acc.: 61.00%\n",
|
|
"Time elapsed: 3.66 min\n",
|
|
"Epoch: 012/100 | Batch 000/192 | Cost: 1.0049\n",
|
|
"Epoch: 012/100 | Batch 120/192 | Cost: 1.2247\n",
|
|
"Epoch: 012/100 Train Acc.: 62.14% | Validation Acc.: 62.70%\n",
|
|
"Time elapsed: 4.00 min\n",
|
|
"Epoch: 013/100 | Batch 000/192 | Cost: 0.9232\n",
|
|
"Epoch: 013/100 | Batch 120/192 | Cost: 1.0345\n",
|
|
"Epoch: 013/100 Train Acc.: 61.42% | Validation Acc.: 61.70%\n",
|
|
"Time elapsed: 4.35 min\n",
|
|
"Epoch: 014/100 | Batch 000/192 | Cost: 0.9256\n",
|
|
"Epoch: 014/100 | Batch 120/192 | Cost: 1.1639\n",
|
|
"Epoch: 014/100 Train Acc.: 63.82% | Validation Acc.: 65.80%\n",
|
|
"Time elapsed: 4.69 min\n",
|
|
"Epoch: 015/100 | Batch 000/192 | Cost: 0.9600\n",
|
|
"Epoch: 015/100 | Batch 120/192 | Cost: 1.0263\n",
|
|
"Epoch: 015/100 Train Acc.: 63.94% | Validation Acc.: 64.00%\n",
|
|
"Time elapsed: 5.04 min\n",
|
|
"Epoch: 016/100 | Batch 000/192 | Cost: 0.8859\n",
|
|
"Epoch: 016/100 | Batch 120/192 | Cost: 1.0307\n",
|
|
"Epoch: 016/100 Train Acc.: 65.79% | Validation Acc.: 66.40%\n",
|
|
"Time elapsed: 5.39 min\n",
|
|
"Epoch: 017/100 | Batch 000/192 | Cost: 1.0020\n",
|
|
"Epoch: 017/100 | Batch 120/192 | Cost: 0.9755\n",
|
|
"Epoch: 017/100 Train Acc.: 66.95% | Validation Acc.: 66.60%\n",
|
|
"Time elapsed: 5.73 min\n",
|
|
"Epoch: 018/100 | Batch 000/192 | Cost: 0.9551\n",
|
|
"Epoch: 018/100 | Batch 120/192 | Cost: 0.8429\n",
|
|
"Epoch: 018/100 Train Acc.: 67.56% | Validation Acc.: 66.30%\n",
|
|
"Time elapsed: 6.08 min\n",
|
|
"Epoch: 019/100 | Batch 000/192 | Cost: 1.0420\n",
|
|
"Epoch: 019/100 | Batch 120/192 | Cost: 0.9771\n",
|
|
"Epoch: 019/100 Train Acc.: 69.44% | Validation Acc.: 68.20%\n",
|
|
"Time elapsed: 6.43 min\n",
|
|
"Epoch: 020/100 | Batch 000/192 | Cost: 0.8471\n",
|
|
"Epoch: 020/100 | Batch 120/192 | Cost: 0.8322\n",
|
|
"Epoch: 020/100 Train Acc.: 69.99% | Validation Acc.: 70.20%\n",
|
|
"Time elapsed: 6.77 min\n",
|
|
"Epoch: 021/100 | Batch 000/192 | Cost: 0.8974\n",
|
|
"Epoch: 021/100 | Batch 120/192 | Cost: 0.8585\n",
|
|
"Epoch: 021/100 Train Acc.: 69.52% | Validation Acc.: 69.30%\n",
|
|
"Time elapsed: 7.12 min\n",
|
|
"Epoch: 022/100 | Batch 000/192 | Cost: 0.8691\n",
|
|
"Epoch: 022/100 | Batch 120/192 | Cost: 0.6618\n",
|
|
"Epoch: 022/100 Train Acc.: 68.26% | Validation Acc.: 65.90%\n",
|
|
"Time elapsed: 7.47 min\n",
|
|
"Epoch: 023/100 | Batch 000/192 | Cost: 0.9277\n",
|
|
"Epoch: 023/100 | Batch 120/192 | Cost: 0.9011\n",
|
|
"Epoch: 023/100 Train Acc.: 71.66% | Validation Acc.: 72.10%\n",
|
|
"Time elapsed: 7.81 min\n",
|
|
"Epoch: 024/100 | Batch 000/192 | Cost: 0.7764\n",
|
|
"Epoch: 024/100 | Batch 120/192 | Cost: 0.7561\n",
|
|
"Epoch: 024/100 Train Acc.: 71.70% | Validation Acc.: 68.80%\n",
|
|
"Time elapsed: 8.16 min\n",
|
|
"Epoch: 025/100 | Batch 000/192 | Cost: 0.8113\n",
|
|
"Epoch: 025/100 | Batch 120/192 | Cost: 0.7186\n",
|
|
"Epoch: 025/100 Train Acc.: 73.62% | Validation Acc.: 73.00%\n",
|
|
"Time elapsed: 8.50 min\n",
|
|
"Epoch: 026/100 | Batch 000/192 | Cost: 0.6515\n",
|
|
"Epoch: 026/100 | Batch 120/192 | Cost: 0.6954\n",
|
|
"Epoch: 026/100 Train Acc.: 72.22% | Validation Acc.: 70.20%\n",
|
|
"Time elapsed: 8.85 min\n",
|
|
"Epoch: 027/100 | Batch 000/192 | Cost: 0.7278\n",
|
|
"Epoch: 027/100 | Batch 120/192 | Cost: 0.7117\n",
|
|
"Epoch: 027/100 Train Acc.: 74.82% | Validation Acc.: 72.30%\n",
|
|
"Time elapsed: 9.19 min\n",
|
|
"Epoch: 028/100 | Batch 000/192 | Cost: 0.6732\n",
|
|
"Epoch: 028/100 | Batch 120/192 | Cost: 0.6591\n",
|
|
"Epoch: 028/100 Train Acc.: 74.93% | Validation Acc.: 72.60%\n",
|
|
"Time elapsed: 9.54 min\n",
|
|
"Epoch: 029/100 | Batch 000/192 | Cost: 0.7438\n",
|
|
"Epoch: 029/100 | Batch 120/192 | Cost: 0.6429\n",
|
|
"Epoch: 029/100 Train Acc.: 75.44% | Validation Acc.: 72.80%\n",
|
|
"Time elapsed: 9.88 min\n",
|
|
"Epoch: 030/100 | Batch 000/192 | Cost: 0.7306\n",
|
|
"Epoch: 030/100 | Batch 120/192 | Cost: 0.6643\n",
|
|
"Epoch: 030/100 Train Acc.: 76.34% | Validation Acc.: 74.40%\n",
|
|
"Time elapsed: 10.22 min\n",
|
|
"Epoch: 031/100 | Batch 000/192 | Cost: 0.5957\n",
|
|
"Epoch: 031/100 | Batch 120/192 | Cost: 0.5574\n",
|
|
"Epoch: 031/100 Train Acc.: 76.60% | Validation Acc.: 75.90%\n",
|
|
"Time elapsed: 10.57 min\n",
|
|
"Epoch: 032/100 | Batch 000/192 | Cost: 0.6414\n",
|
|
"Epoch: 032/100 | Batch 120/192 | Cost: 0.6951\n",
|
|
"Epoch: 032/100 Train Acc.: 77.15% | Validation Acc.: 76.10%\n",
|
|
"Time elapsed: 10.91 min\n",
|
|
"Epoch: 033/100 | Batch 000/192 | Cost: 0.6898\n",
|
|
"Epoch: 033/100 | Batch 120/192 | Cost: 0.7784\n",
|
|
"Epoch: 033/100 Train Acc.: 77.15% | Validation Acc.: 74.70%\n",
|
|
"Time elapsed: 11.26 min\n",
|
|
"Epoch: 034/100 | Batch 000/192 | Cost: 0.5633\n",
|
|
"Epoch: 034/100 | Batch 120/192 | Cost: 0.6176\n",
|
|
"Epoch: 034/100 Train Acc.: 77.53% | Validation Acc.: 74.30%\n",
|
|
"Time elapsed: 11.60 min\n",
|
|
"Epoch: 035/100 | Batch 000/192 | Cost: 0.6300\n",
|
|
"Epoch: 035/100 | Batch 120/192 | Cost: 0.6720\n",
|
|
"Epoch: 035/100 Train Acc.: 78.39% | Validation Acc.: 76.10%\n",
|
|
"Time elapsed: 11.94 min\n",
|
|
"Epoch: 036/100 | Batch 000/192 | Cost: 0.7154\n",
|
|
"Epoch: 036/100 | Batch 120/192 | Cost: 0.6519\n",
|
|
"Epoch: 036/100 Train Acc.: 78.49% | Validation Acc.: 75.40%\n",
|
|
"Time elapsed: 12.29 min\n",
|
|
"Epoch: 037/100 | Batch 000/192 | Cost: 0.6381\n",
|
|
"Epoch: 037/100 | Batch 120/192 | Cost: 0.6618\n",
|
|
"Epoch: 037/100 Train Acc.: 79.58% | Validation Acc.: 75.80%\n",
|
|
"Time elapsed: 12.63 min\n",
|
|
"Epoch: 038/100 | Batch 000/192 | Cost: 0.6078\n",
|
|
"Epoch: 038/100 | Batch 120/192 | Cost: 0.5283\n",
|
|
"Epoch: 038/100 Train Acc.: 79.17% | Validation Acc.: 76.00%\n",
|
|
"Time elapsed: 12.97 min\n",
|
|
"Epoch: 039/100 | Batch 000/192 | Cost: 0.5576\n",
|
|
"Epoch: 039/100 | Batch 120/192 | Cost: 0.6219\n",
|
|
"Epoch: 039/100 Train Acc.: 79.91% | Validation Acc.: 76.70%\n",
|
|
"Time elapsed: 13.32 min\n",
|
|
"Epoch: 040/100 | Batch 000/192 | Cost: 0.5660\n",
|
|
"Epoch: 040/100 | Batch 120/192 | Cost: 0.5577\n",
|
|
"Epoch: 040/100 Train Acc.: 80.49% | Validation Acc.: 76.50%\n",
|
|
"Time elapsed: 13.66 min\n",
|
|
"Epoch: 041/100 | Batch 000/192 | Cost: 0.5098\n",
|
|
"Epoch: 041/100 | Batch 120/192 | Cost: 0.6621\n",
|
|
"Epoch: 041/100 Train Acc.: 80.86% | Validation Acc.: 75.70%\n",
|
|
"Time elapsed: 14.00 min\n",
|
|
"Epoch: 042/100 | Batch 000/192 | Cost: 0.4589\n",
|
|
"Epoch: 042/100 | Batch 120/192 | Cost: 0.5637\n",
|
|
"Epoch: 042/100 Train Acc.: 81.11% | Validation Acc.: 77.00%\n",
|
|
"Time elapsed: 14.34 min\n",
|
|
"Epoch: 043/100 | Batch 000/192 | Cost: 0.4507\n",
|
|
"Epoch: 043/100 | Batch 120/192 | Cost: 0.4865\n",
|
|
"Epoch: 043/100 Train Acc.: 82.07% | Validation Acc.: 78.10%\n",
|
|
"Time elapsed: 14.68 min\n",
|
|
"Epoch: 044/100 | Batch 000/192 | Cost: 0.4427\n",
|
|
"Epoch: 044/100 | Batch 120/192 | Cost: 0.5242\n",
|
|
"Epoch: 044/100 Train Acc.: 82.61% | Validation Acc.: 79.10%\n",
|
|
"Time elapsed: 15.02 min\n",
|
|
"Epoch: 045/100 | Batch 000/192 | Cost: 0.4989\n",
|
|
"Epoch: 045/100 | Batch 120/192 | Cost: 0.5811\n",
|
|
"Epoch: 045/100 Train Acc.: 82.55% | Validation Acc.: 79.30%\n",
|
|
"Time elapsed: 15.36 min\n",
|
|
"Epoch: 046/100 | Batch 000/192 | Cost: 0.5303\n",
|
|
"Epoch: 046/100 | Batch 120/192 | Cost: 0.4242\n",
|
|
"Epoch: 046/100 Train Acc.: 81.80% | Validation Acc.: 76.80%\n",
|
|
"Time elapsed: 15.71 min\n",
|
|
"Epoch: 047/100 | Batch 000/192 | Cost: 0.4491\n",
|
|
"Epoch: 047/100 | Batch 120/192 | Cost: 0.4902\n",
|
|
"Epoch: 047/100 Train Acc.: 82.54% | Validation Acc.: 77.90%\n",
|
|
"Time elapsed: 16.05 min\n",
|
|
"Epoch: 048/100 | Batch 000/192 | Cost: 0.4913\n",
|
|
"Epoch: 048/100 | Batch 120/192 | Cost: 0.6474\n",
|
|
"Epoch: 048/100 Train Acc.: 83.31% | Validation Acc.: 79.20%\n",
|
|
"Time elapsed: 16.39 min\n",
|
|
"Epoch: 049/100 | Batch 000/192 | Cost: 0.4585\n",
|
|
"Epoch: 049/100 | Batch 120/192 | Cost: 0.4845\n",
|
|
"Epoch: 049/100 Train Acc.: 83.53% | Validation Acc.: 78.40%\n",
|
|
"Time elapsed: 16.73 min\n",
|
|
"Epoch: 050/100 | Batch 000/192 | Cost: 0.6038\n",
|
|
"Epoch: 050/100 | Batch 120/192 | Cost: 0.5446\n",
|
|
"Epoch: 050/100 Train Acc.: 83.86% | Validation Acc.: 80.50%\n",
|
|
"Time elapsed: 17.08 min\n",
|
|
"Epoch: 051/100 | Batch 000/192 | Cost: 0.3793\n",
|
|
"Epoch: 051/100 | Batch 120/192 | Cost: 0.4499\n",
|
|
"Epoch: 051/100 Train Acc.: 83.11% | Validation Acc.: 76.80%\n",
|
|
"Time elapsed: 17.42 min\n",
|
|
"Epoch: 052/100 | Batch 000/192 | Cost: 0.5527\n",
|
|
"Epoch: 052/100 | Batch 120/192 | Cost: 0.4610\n",
|
|
"Epoch: 052/100 Train Acc.: 84.63% | Validation Acc.: 79.30%\n",
|
|
"Time elapsed: 17.76 min\n",
|
|
"Epoch: 053/100 | Batch 000/192 | Cost: 0.5015\n",
|
|
"Epoch: 053/100 | Batch 120/192 | Cost: 0.4079\n",
|
|
"Epoch: 053/100 Train Acc.: 84.18% | Validation Acc.: 77.60%\n",
|
|
"Time elapsed: 18.11 min\n",
|
|
"Epoch: 054/100 | Batch 000/192 | Cost: 0.5012\n",
|
|
"Epoch: 054/100 | Batch 120/192 | Cost: 0.4912\n",
|
|
"Epoch: 054/100 Train Acc.: 84.41% | Validation Acc.: 77.20%\n",
|
|
"Time elapsed: 18.45 min\n",
|
|
"Epoch: 055/100 | Batch 000/192 | Cost: 0.4015\n",
|
|
"Epoch: 055/100 | Batch 120/192 | Cost: 0.4919\n",
|
|
"Epoch: 055/100 Train Acc.: 85.16% | Validation Acc.: 80.20%\n",
|
|
"Time elapsed: 18.79 min\n",
|
|
"Epoch: 056/100 | Batch 000/192 | Cost: 0.3976\n",
|
|
"Epoch: 056/100 | Batch 120/192 | Cost: 0.4252\n",
|
|
"Epoch: 056/100 Train Acc.: 85.28% | Validation Acc.: 80.30%\n",
|
|
"Time elapsed: 19.14 min\n",
|
|
"Epoch: 057/100 | Batch 000/192 | Cost: 0.3372\n",
|
|
"Epoch: 057/100 | Batch 120/192 | Cost: 0.4634\n",
|
|
"Epoch: 057/100 Train Acc.: 84.29% | Validation Acc.: 78.60%\n",
|
|
"Time elapsed: 19.48 min\n",
|
|
"Epoch: 058/100 | Batch 000/192 | Cost: 0.4438\n",
|
|
"Epoch: 058/100 | Batch 120/192 | Cost: 0.3490\n",
|
|
"Epoch: 058/100 Train Acc.: 85.93% | Validation Acc.: 77.50%\n",
|
|
"Time elapsed: 19.82 min\n",
|
|
"Epoch: 059/100 | Batch 000/192 | Cost: 0.4541\n",
|
|
"Epoch: 059/100 | Batch 120/192 | Cost: 0.4415\n",
|
|
"Epoch: 059/100 Train Acc.: 84.34% | Validation Acc.: 78.40%\n",
|
|
"Time elapsed: 20.16 min\n",
|
|
"Epoch: 060/100 | Batch 000/192 | Cost: 0.3766\n",
|
|
"Epoch: 060/100 | Batch 120/192 | Cost: 0.4851\n",
|
|
"Epoch: 060/100 Train Acc.: 86.02% | Validation Acc.: 80.00%\n",
|
|
"Time elapsed: 20.51 min\n",
|
|
"Epoch: 061/100 | Batch 000/192 | Cost: 0.4967\n",
|
|
"Epoch: 061/100 | Batch 120/192 | Cost: 0.3708\n",
|
|
"Epoch: 061/100 Train Acc.: 85.57% | Validation Acc.: 79.50%\n",
|
|
"Time elapsed: 20.85 min\n",
|
|
"Epoch: 062/100 | Batch 000/192 | Cost: 0.4197\n",
|
|
"Epoch: 062/100 | Batch 120/192 | Cost: 0.3054\n",
|
|
"Epoch: 062/100 Train Acc.: 86.23% | Validation Acc.: 78.40%\n",
|
|
"Time elapsed: 21.19 min\n",
|
|
"Epoch: 063/100 | Batch 000/192 | Cost: 0.4595\n",
|
|
"Epoch: 063/100 | Batch 120/192 | Cost: 0.4200\n",
|
|
"Epoch: 063/100 Train Acc.: 86.52% | Validation Acc.: 79.80%\n",
|
|
"Time elapsed: 21.54 min\n",
|
|
"Epoch: 064/100 | Batch 000/192 | Cost: 0.3806\n",
|
|
"Epoch: 064/100 | Batch 120/192 | Cost: 0.3670\n",
|
|
"Epoch: 064/100 Train Acc.: 86.81% | Validation Acc.: 80.20%\n",
|
|
"Time elapsed: 21.88 min\n",
|
|
"Epoch: 065/100 | Batch 000/192 | Cost: 0.3922\n",
|
|
"Epoch: 065/100 | Batch 120/192 | Cost: 0.3698\n",
|
|
"Epoch: 065/100 Train Acc.: 86.30% | Validation Acc.: 77.90%\n",
|
|
"Time elapsed: 22.22 min\n",
|
|
"Epoch: 066/100 | Batch 000/192 | Cost: 0.3608\n",
|
|
"Epoch: 066/100 | Batch 120/192 | Cost: 0.4444\n",
|
|
"Epoch: 066/100 Train Acc.: 88.01% | Validation Acc.: 80.10%\n",
|
|
"Time elapsed: 22.56 min\n",
|
|
"Epoch: 067/100 | Batch 000/192 | Cost: 0.3374\n",
|
|
"Epoch: 067/100 | Batch 120/192 | Cost: 0.3158\n",
|
|
"Epoch: 067/100 Train Acc.: 87.94% | Validation Acc.: 80.40%\n",
|
|
"Time elapsed: 22.91 min\n",
|
|
"Epoch: 068/100 | Batch 000/192 | Cost: 0.3959\n",
|
|
"Epoch: 068/100 | Batch 120/192 | Cost: 0.2217\n",
|
|
"Epoch: 068/100 Train Acc.: 87.74% | Validation Acc.: 79.70%\n",
|
|
"Time elapsed: 23.25 min\n",
|
|
"Epoch: 069/100 | Batch 000/192 | Cost: 0.3795\n",
|
|
"Epoch: 069/100 | Batch 120/192 | Cost: 0.3398\n",
|
|
"Epoch: 069/100 Train Acc.: 88.28% | Validation Acc.: 79.70%\n",
|
|
"Time elapsed: 23.59 min\n",
|
|
"Epoch: 070/100 | Batch 000/192 | Cost: 0.3098\n",
|
|
"Epoch: 070/100 | Batch 120/192 | Cost: 0.3012\n",
|
|
"Epoch: 070/100 Train Acc.: 87.96% | Validation Acc.: 80.80%\n",
|
|
"Time elapsed: 23.93 min\n",
|
|
"Epoch: 071/100 | Batch 000/192 | Cost: 0.3705\n",
|
|
"Epoch: 071/100 | Batch 120/192 | Cost: 0.2943\n",
|
|
"Epoch: 071/100 Train Acc.: 88.02% | Validation Acc.: 79.90%\n",
|
|
"Time elapsed: 24.27 min\n",
|
|
"Epoch: 072/100 | Batch 000/192 | Cost: 0.3353\n",
|
|
"Epoch: 072/100 | Batch 120/192 | Cost: 0.3237\n",
|
|
"Epoch: 072/100 Train Acc.: 88.34% | Validation Acc.: 80.60%\n",
|
|
"Time elapsed: 24.62 min\n",
|
|
"Epoch: 073/100 | Batch 000/192 | Cost: 0.3683\n",
|
|
"Epoch: 073/100 | Batch 120/192 | Cost: 0.4178\n",
|
|
"Epoch: 073/100 Train Acc.: 88.93% | Validation Acc.: 80.10%\n",
|
|
"Time elapsed: 24.96 min\n",
|
|
"Epoch: 074/100 | Batch 000/192 | Cost: 0.2282\n",
|
|
"Epoch: 074/100 | Batch 120/192 | Cost: 0.1967\n",
|
|
"Epoch: 074/100 Train Acc.: 88.58% | Validation Acc.: 81.40%\n",
|
|
"Time elapsed: 25.30 min\n",
|
|
"Epoch: 075/100 | Batch 000/192 | Cost: 0.2701\n",
|
|
"Epoch: 075/100 | Batch 120/192 | Cost: 0.3722\n",
|
|
"Epoch: 075/100 Train Acc.: 87.93% | Validation Acc.: 79.70%\n",
|
|
"Time elapsed: 25.64 min\n",
|
|
"Epoch: 076/100 | Batch 000/192 | Cost: 0.2850\n",
|
|
"Epoch: 076/100 | Batch 120/192 | Cost: 0.2874\n",
|
|
"Epoch: 076/100 Train Acc.: 88.92% | Validation Acc.: 81.10%\n",
|
|
"Time elapsed: 25.98 min\n",
|
|
"Epoch: 077/100 | Batch 000/192 | Cost: 0.2686\n",
|
|
"Epoch: 077/100 | Batch 120/192 | Cost: 0.4312\n",
|
|
"Epoch: 077/100 Train Acc.: 89.39% | Validation Acc.: 81.60%\n",
|
|
"Time elapsed: 26.33 min\n",
|
|
"Epoch: 078/100 | Batch 000/192 | Cost: 0.2282\n",
|
|
"Epoch: 078/100 | Batch 120/192 | Cost: 0.3395\n",
|
|
"Epoch: 078/100 Train Acc.: 88.67% | Validation Acc.: 78.90%\n",
|
|
"Time elapsed: 26.67 min\n",
|
|
"Epoch: 079/100 | Batch 000/192 | Cost: 0.3127\n",
|
|
"Epoch: 079/100 | Batch 120/192 | Cost: 0.2906\n",
|
|
"Epoch: 079/100 Train Acc.: 90.77% | Validation Acc.: 81.20%\n",
|
|
"Time elapsed: 27.01 min\n",
|
|
"Epoch: 080/100 | Batch 000/192 | Cost: 0.2468\n",
|
|
"Epoch: 080/100 | Batch 120/192 | Cost: 0.3638\n",
|
|
"Epoch: 080/100 Train Acc.: 89.99% | Validation Acc.: 80.40%\n",
|
|
"Time elapsed: 27.35 min\n",
|
|
"Epoch: 081/100 | Batch 000/192 | Cost: 0.2936\n",
|
|
"Epoch: 081/100 | Batch 120/192 | Cost: 0.3772\n",
|
|
"Epoch: 081/100 Train Acc.: 90.76% | Validation Acc.: 80.50%\n",
|
|
"Time elapsed: 27.69 min\n",
|
|
"Epoch: 082/100 | Batch 000/192 | Cost: 0.2584\n",
|
|
"Epoch: 082/100 | Batch 120/192 | Cost: 0.2718\n",
|
|
"Epoch: 082/100 Train Acc.: 91.01% | Validation Acc.: 81.20%\n",
|
|
"Time elapsed: 28.04 min\n",
|
|
"Epoch: 083/100 | Batch 000/192 | Cost: 0.1904\n",
|
|
"Epoch: 083/100 | Batch 120/192 | Cost: 0.3090\n",
|
|
"Epoch: 083/100 Train Acc.: 90.68% | Validation Acc.: 81.30%\n",
|
|
"Time elapsed: 28.38 min\n",
|
|
"Epoch: 084/100 | Batch 000/192 | Cost: 0.2506\n",
|
|
"Epoch: 084/100 | Batch 120/192 | Cost: 0.2825\n",
|
|
"Epoch: 084/100 Train Acc.: 90.43% | Validation Acc.: 80.40%\n",
|
|
"Time elapsed: 28.73 min\n",
|
|
"Epoch: 085/100 | Batch 000/192 | Cost: 0.2307\n",
|
|
"Epoch: 085/100 | Batch 120/192 | Cost: 0.2441\n",
|
|
"Epoch: 085/100 Train Acc.: 90.88% | Validation Acc.: 81.30%\n",
|
|
"Time elapsed: 29.07 min\n",
|
|
"Epoch: 086/100 | Batch 000/192 | Cost: 0.3149\n",
|
|
"Epoch: 086/100 | Batch 120/192 | Cost: 0.3129\n",
|
|
"Epoch: 086/100 Train Acc.: 90.13% | Validation Acc.: 82.40%\n",
|
|
"Time elapsed: 29.41 min\n",
|
|
"Epoch: 087/100 | Batch 000/192 | Cost: 0.3487\n",
|
|
"Epoch: 087/100 | Batch 120/192 | Cost: 0.2559\n",
|
|
"Epoch: 087/100 Train Acc.: 90.74% | Validation Acc.: 81.40%\n",
|
|
"Time elapsed: 29.75 min\n",
|
|
"Epoch: 088/100 | Batch 000/192 | Cost: 0.2412\n",
|
|
"Epoch: 088/100 | Batch 120/192 | Cost: 0.1828\n",
|
|
"Epoch: 088/100 Train Acc.: 91.08% | Validation Acc.: 80.20%\n",
|
|
"Time elapsed: 30.10 min\n",
|
|
"Epoch: 089/100 | Batch 000/192 | Cost: 0.2957\n",
|
|
"Epoch: 089/100 | Batch 120/192 | Cost: 0.2939\n",
|
|
"Epoch: 089/100 Train Acc.: 90.67% | Validation Acc.: 80.30%\n",
|
|
"Time elapsed: 30.44 min\n",
|
|
"Epoch: 090/100 | Batch 000/192 | Cost: 0.2298\n",
|
|
"Epoch: 090/100 | Batch 120/192 | Cost: 0.2900\n",
|
|
"Epoch: 090/100 Train Acc.: 91.63% | Validation Acc.: 79.00%\n",
|
|
"Time elapsed: 30.78 min\n",
|
|
"Epoch: 091/100 | Batch 000/192 | Cost: 0.2558\n",
|
|
"Epoch: 091/100 | Batch 120/192 | Cost: 0.2915\n",
|
|
"Epoch: 091/100 Train Acc.: 91.36% | Validation Acc.: 81.00%\n",
|
|
"Time elapsed: 31.12 min\n",
|
|
"Epoch: 092/100 | Batch 000/192 | Cost: 0.1510\n",
|
|
"Epoch: 092/100 | Batch 120/192 | Cost: 0.1974\n",
|
|
"Epoch: 092/100 Train Acc.: 91.84% | Validation Acc.: 82.20%\n",
|
|
"Time elapsed: 31.47 min\n",
|
|
"Epoch: 093/100 | Batch 000/192 | Cost: 0.2308\n",
|
|
"Epoch: 093/100 | Batch 120/192 | Cost: 0.2247\n",
|
|
"Epoch: 093/100 Train Acc.: 91.50% | Validation Acc.: 80.50%\n",
|
|
"Time elapsed: 31.81 min\n",
|
|
"Epoch: 094/100 | Batch 000/192 | Cost: 0.2712\n",
|
|
"Epoch: 094/100 | Batch 120/192 | Cost: 0.3268\n",
|
|
"Epoch: 094/100 Train Acc.: 91.74% | Validation Acc.: 81.30%\n",
|
|
"Time elapsed: 32.15 min\n",
|
|
"Epoch: 095/100 | Batch 000/192 | Cost: 0.2417\n",
|
|
"Epoch: 095/100 | Batch 120/192 | Cost: 0.2162\n",
|
|
"Epoch: 095/100 Train Acc.: 91.53% | Validation Acc.: 79.00%\n",
|
|
"Time elapsed: 32.49 min\n",
|
|
"Epoch: 096/100 | Batch 000/192 | Cost: 0.2523\n",
|
|
"Epoch: 096/100 | Batch 120/192 | Cost: 0.2598\n",
|
|
"Epoch: 096/100 Train Acc.: 91.56% | Validation Acc.: 81.00%\n",
|
|
"Time elapsed: 32.84 min\n",
|
|
"Epoch: 097/100 | Batch 000/192 | Cost: 0.2027\n",
|
|
"Epoch: 097/100 | Batch 120/192 | Cost: 0.2432\n",
|
|
"Epoch: 097/100 Train Acc.: 92.53% | Validation Acc.: 80.80%\n",
|
|
"Time elapsed: 33.18 min\n",
|
|
"Epoch: 098/100 | Batch 000/192 | Cost: 0.2115\n",
|
|
"Epoch: 098/100 | Batch 120/192 | Cost: 0.2746\n",
|
|
"Epoch: 098/100 Train Acc.: 92.30% | Validation Acc.: 81.10%\n",
|
|
"Time elapsed: 33.52 min\n",
|
|
"Epoch: 099/100 | Batch 000/192 | Cost: 0.1611\n",
|
|
"Epoch: 099/100 | Batch 120/192 | Cost: 0.2142\n",
|
|
"Epoch: 099/100 Train Acc.: 92.66% | Validation Acc.: 80.90%\n",
|
|
"Time elapsed: 33.86 min\n",
|
|
"Epoch: 100/100 | Batch 000/192 | Cost: 0.1935\n",
|
|
"Epoch: 100/100 | Batch 120/192 | Cost: 0.2488\n",
|
|
"Epoch: 100/100 Train Acc.: 92.68% | Validation Acc.: 80.20%\n",
|
|
"Time elapsed: 34.20 min\n",
|
|
"Total Training Time: 34.20 min\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"def compute_accuracy(model, data_loader, device):\n",
|
|
" correct_pred, num_examples = 0, 0\n",
|
|
" for i, (features, targets) in enumerate(data_loader):\n",
|
|
" \n",
|
|
" features = features.to(device)\n",
|
|
" targets = targets.to(device)\n",
|
|
"\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",
|
|
" \n",
|
|
" model.train()\n",
|
|
" \n",
|
|
" for batch_idx, (features, targets) in enumerate(train_loader):\n",
|
|
" \n",
|
|
" ### PREPARE MINIBATCH\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 % 120:\n",
|
|
" print (f'Epoch: {epoch+1:03d}/{NUM_EPOCHS:03d} | '\n",
|
|
" f'Batch {batch_idx:03d}/{len(train_loader):03d} |' \n",
|
|
" f' Cost: {cost:.4f}')\n",
|
|
"\n",
|
|
" # no need to build the computation graph for backprop when computing accuracy\n",
|
|
" with torch.set_grad_enabled(False):\n",
|
|
" train_acc = compute_accuracy(model, train_loader, device=DEVICE)\n",
|
|
" valid_acc = compute_accuracy(model, valid_loader, device=DEVICE)\n",
|
|
" print(f'Epoch: {epoch+1:03d}/{NUM_EPOCHS:03d} Train Acc.: {train_acc:.2f}%'\n",
|
|
" f' | Validation Acc.: {valid_acc:.2f}%')\n",
|
|
" \n",
|
|
" elapsed = (time.time() - start_time)/60\n",
|
|
" print(f'Time elapsed: {elapsed:.2f} min')\n",
|
|
" \n",
|
|
"elapsed = (time.time() - start_time)/60\n",
|
|
"print(f'Total Training Time: {elapsed:.2f} min')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"PIL.Image 6.2.1\n",
|
|
"torch 1.3.0\n",
|
|
"numpy 1.17.4\n",
|
|
"matplotlib 3.1.0\n",
|
|
"pandas 0.24.2\n",
|
|
"torchvision 0.4.1a0+d94043a\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%watermark -iv"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"accelerator": "GPU",
|
|
"colab": {
|
|
"collapsed_sections": [],
|
|
"default_view": {},
|
|
"name": "convnet-vgg16.ipynb",
|
|
"provenance": [],
|
|
"version": "0.3.2",
|
|
"views": {}
|
|
},
|
|
"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": true,
|
|
"toc_position": {
|
|
"height": "calc(100% - 180px)",
|
|
"left": "10px",
|
|
"top": "150px",
|
|
"width": "371px"
|
|
},
|
|
"toc_section_display": true,
|
|
"toc_window_display": true
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|