wehub-resource-sync
697 lines
22 KiB
Plaintext
697 lines
22 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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"colab_type": "text",
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"id": "UEBilEjLj5wY"
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},
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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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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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},
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"base_uri": "https://localhost:8080/",
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"height": 119
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},
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"colab_type": "code",
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"executionInfo": {
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"elapsed": 536,
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"status": "ok",
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"timestamp": 1524974472601,
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"user": {
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"displayName": "Sebastian Raschka",
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"photoUrl": "//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg",
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"userId": "118404394130788869227"
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},
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"user_tz": 240
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},
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"id": "GOzuY8Yvj5wb",
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"outputId": "c19362ce-f87a-4cc2-84cc-8d7b4b9e6007"
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},
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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 3.7.3\n",
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"IPython 7.9.0\n",
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"\n",
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"torch 1.3.1\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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"colab_type": "text",
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"id": "rH4XmErYj5wm"
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},
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"source": [
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"# LeNet-5 CIFAR10 Classifier"
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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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"This notebook implements the classic LeNet-5 convolutional network [1] and applies it to the CIFAR10 object classification dataset. The basic architecture is shown in the figure below:\n",
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"\n",
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""
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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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"\n",
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"\n",
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"LeNet-5 is commonly regarded as the pioneer of convolutional neural networks, consisting of a very simple architecture (by modern standards). In total, LeNet-5 consists of only 7 layers. 3 out of these 7 layers are convolutional layers (C1, C3, C5), which are connected by two average pooling layers (S2 & S4). The penultimate layer is a fully connexted layer (F6), which is followed by the final output layer. The additional details are summarized below:\n",
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"\n",
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"- All convolutional layers use 5x5 kernels with stride 1.\n",
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"- The two average pooling (subsampling) layers are 2x2 pixels wide with stride 1.\n",
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"- Throughrout the network, tanh sigmoid activation functions are used. (**In this notebook, we replace these with ReLU activations**)\n",
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"- The output layer uses 10 custom Euclidean Radial Basis Function neurons for the output layer. (**In this notebook, we replace these with softmax activations**)\n",
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"\n",
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"**Please note that the original architecture was applied to MNIST-like grayscale images (1 color channel). CIFAR10 has 3 color-channels. I found that using the regular architecture results in very poor performance on CIFAR10 (approx. 50% ACC). Hence, I am multiplying the number of kernels by a factor of 3 (according to the 3 color channels) in each layer, which improves is a little bit (approx. 60% Acc).**\n",
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"\n",
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"### References\n",
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"\n",
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"- [1] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, november 1998."
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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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"colab_type": "text",
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"id": "MkoGLH_Tj5wn"
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},
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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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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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}
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},
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"colab_type": "code",
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"id": "ORj09gnrj5wp"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import time\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.nn.functional as F\n",
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"from torch.utils.data import DataLoader\n",
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"\n",
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"from torchvision import datasets\n",
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"from torchvision import transforms\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"from PIL import Image\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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"colab_type": "text",
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"id": "I6hghKPxj5w0"
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},
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"source": [
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"## Model Settings"
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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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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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},
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"base_uri": "https://localhost:8080/",
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"height": 85
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},
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"colab_type": "code",
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"executionInfo": {
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"elapsed": 23936,
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"status": "ok",
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"timestamp": 1524974497505,
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"user": {
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"displayName": "Sebastian Raschka",
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"photoUrl": "//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg",
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"userId": "118404394130788869227"
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},
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"user_tz": 240
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},
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"id": "NnT0sZIwj5wu",
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"outputId": "55aed925-d17e-4c6a-8c71-0d9b3bde5637"
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},
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"outputs": [],
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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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"# Hyperparameters\n",
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"RANDOM_SEED = 1\n",
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"LEARNING_RATE = 0.001\n",
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"BATCH_SIZE = 128\n",
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"NUM_EPOCHS = 10\n",
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"\n",
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"# Architecture\n",
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"NUM_FEATURES = 32*32\n",
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"NUM_CLASSES = 10\n",
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"\n",
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"# Other\n",
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"DEVICE = \"cuda:0\"\n",
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"GRAYSCALE = False"
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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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"### MNIST 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": 4,
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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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"Files already downloaded and verified\n",
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"Image batch dimensions: torch.Size([128, 3, 32, 32])\n",
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"Image label dimensions: torch.Size([128])\n",
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"Image batch dimensions: torch.Size([128, 3, 32, 32])\n",
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"Image label dimensions: torch.Size([128])\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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"### CIFAR-10 Dataset\n",
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"##########################\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.CIFAR10(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.CIFAR10(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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" num_workers=8,\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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" num_workers=8,\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\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": 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: 1 | Batch index: 0 | Batch size: 128\n",
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"Epoch: 2 | Batch index: 0 | Batch size: 128\n"
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]
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}
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],
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"source": [
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"device = torch.device(DEVICE)\n",
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"torch.manual_seed(0)\n",
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"\n",
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"for epoch in range(2):\n",
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"\n",
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" for batch_idx, (x, y) in enumerate(train_loader):\n",
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" \n",
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" print('Epoch:', epoch+1, end='')\n",
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" print(' | Batch index:', batch_idx, end='')\n",
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" print(' | Batch size:', y.size()[0])\n",
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" \n",
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" x = x.to(device)\n",
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" y = y.to(device)\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": 6,
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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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"\n",
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"class LeNet5(nn.Module):\n",
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"\n",
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" def __init__(self, num_classes, grayscale=False):\n",
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" super(LeNet5, self).__init__()\n",
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" \n",
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" self.grayscale = grayscale\n",
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" self.num_classes = num_classes\n",
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"\n",
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" if self.grayscale:\n",
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" in_channels = 1\n",
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" else:\n",
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" in_channels = 3\n",
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"\n",
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" self.features = nn.Sequential(\n",
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" \n",
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" nn.Conv2d(in_channels, 6*in_channels, kernel_size=5),\n",
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" nn.Tanh(),\n",
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" nn.MaxPool2d(kernel_size=2),\n",
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" nn.Conv2d(6*in_channels, 16*in_channels, kernel_size=5),\n",
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" nn.Tanh(),\n",
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" nn.MaxPool2d(kernel_size=2)\n",
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" )\n",
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"\n",
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" self.classifier = nn.Sequential(\n",
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" nn.Linear(16*5*5*in_channels, 120*in_channels),\n",
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" nn.Tanh(),\n",
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" nn.Linear(120*in_channels, 84*in_channels),\n",
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" nn.Tanh(),\n",
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" nn.Linear(84*in_channels, num_classes),\n",
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" )\n",
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"\n",
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"\n",
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" def forward(self, x):\n",
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" x = self.features(x)\n",
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" x = torch.flatten(x, 1)\n",
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" logits = self.classifier(x)\n",
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" probas = F.softmax(logits, dim=1)\n",
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" return logits, probas"
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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": 7,
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"metadata": {
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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}
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},
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"colab_type": "code",
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"id": "_lza9t_uj5w1"
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},
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"outputs": [],
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"source": [
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"torch.manual_seed(RANDOM_SEED)\n",
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"\n",
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"model = LeNet5(NUM_CLASSES, GRAYSCALE)\n",
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"model.to(DEVICE)\n",
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"\n",
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"optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE) "
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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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"colab_type": "text",
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"id": "RAodboScj5w6"
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},
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"source": [
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"## Training"
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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": 8,
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"metadata": {
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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},
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"base_uri": "https://localhost:8080/",
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"height": 1547
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},
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"colab_type": "code",
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"executionInfo": {
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"elapsed": 2384585,
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"status": "ok",
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"timestamp": 1524976888520,
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"user": {
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"displayName": "Sebastian Raschka",
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"photoUrl": "//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg",
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"userId": "118404394130788869227"
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},
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"user_tz": 240
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},
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"id": "Dzh3ROmRj5w7",
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"outputId": "5f8fd8c9-b076-403a-b0b7-fd2d498b48d7"
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},
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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 0000/0391 | Cost: 2.3068\n",
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"Epoch: 001/010 | Batch 0050/0391 | Cost: 1.8193\n",
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"Epoch: 001/010 | Batch 0100/0391 | Cost: 1.6464\n",
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"Epoch: 001/010 | Batch 0150/0391 | Cost: 1.5757\n",
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"Epoch: 001/010 | Batch 0300/0391 | Cost: 1.3310\n",
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"Epoch: 001/010 | Batch 0350/0391 | Cost: 1.2781\n",
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"Epoch: 001/010 | Train: 54.326%\n",
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"Time elapsed: 0.16 min\n",
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"Epoch: 002/010 | Batch 0000/0391 | Cost: 1.4109\n",
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"Epoch: 002/010 | Batch 0350/0391 | Cost: 1.1474\n",
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"Epoch: 002/010 | Train: 60.528%\n",
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"Time elapsed: 0.31 min\n",
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"Epoch: 003/010 | Batch 0000/0391 | Cost: 1.1268\n",
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"Epoch: 003/010 | Batch 0050/0391 | Cost: 1.1943\n",
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"Epoch: 003/010 | Batch 0150/0391 | Cost: 1.0215\n",
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"Epoch: 003/010 | Batch 0200/0391 | Cost: 1.0243\n",
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"Epoch: 003/010 | Batch 0250/0391 | Cost: 0.7985\n",
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"Epoch: 003/010 | Batch 0300/0391 | Cost: 1.0755\n",
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"Epoch: 003/010 | Batch 0350/0391 | Cost: 1.1030\n",
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"Epoch: 003/010 | Train: 64.586%\n",
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"Time elapsed: 0.46 min\n",
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"Epoch: 004/010 | Batch 0000/0391 | Cost: 1.1329\n",
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"Epoch: 004/010 | Batch 0050/0391 | Cost: 1.0834\n",
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"Epoch: 004/010 | Batch 0100/0391 | Cost: 1.0509\n",
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"Epoch: 004/010 | Batch 0150/0391 | Cost: 0.9873\n",
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"Epoch: 004/010 | Batch 0200/0391 | Cost: 0.8560\n",
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"Epoch: 004/010 | Batch 0250/0391 | Cost: 1.1286\n",
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"Epoch: 004/010 | Batch 0300/0391 | Cost: 0.8377\n",
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"Epoch: 004/010 | Batch 0350/0391 | Cost: 1.1735\n",
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"Epoch: 004/010 | Train: 66.656%\n",
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"Time elapsed: 0.61 min\n",
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"Epoch: 005/010 | Batch 0000/0391 | Cost: 1.1260\n",
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"Epoch: 005/010 | Batch 0050/0391 | Cost: 0.8605\n",
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"Epoch: 005/010 | Batch 0100/0391 | Cost: 0.9007\n",
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"Epoch: 005/010 | Batch 0150/0391 | Cost: 0.9166\n",
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"Epoch: 005/010 | Batch 0200/0391 | Cost: 0.9488\n",
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"Epoch: 005/010 | Batch 0250/0391 | Cost: 1.0388\n",
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"Epoch: 005/010 | Batch 0300/0391 | Cost: 0.9526\n",
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"Epoch: 005/010 | Batch 0350/0391 | Cost: 0.9109\n",
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"Epoch: 005/010 | Train: 71.504%\n",
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"Time elapsed: 0.76 min\n",
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"Epoch: 006/010 | Batch 0000/0391 | Cost: 0.7038\n",
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"Epoch: 006/010 | Batch 0050/0391 | Cost: 0.6849\n",
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"Epoch: 006/010 | Batch 0100/0391 | Cost: 0.6817\n",
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"Epoch: 006/010 | Batch 0150/0391 | Cost: 0.8213\n",
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"Epoch: 006/010 | Batch 0200/0391 | Cost: 0.7984\n",
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"Epoch: 006/010 | Batch 0250/0391 | Cost: 0.9680\n",
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"Epoch: 006/010 | Batch 0300/0391 | Cost: 0.7650\n",
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"Epoch: 006/010 | Batch 0350/0391 | Cost: 0.9355\n",
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"Epoch: 006/010 | Train: 74.812%\n",
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"Time elapsed: 0.91 min\n",
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"Epoch: 007/010 | Batch 0000/0391 | Cost: 0.8488\n",
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"Epoch: 007/010 | Batch 0050/0391 | Cost: 0.8332\n",
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"Epoch: 007/010 | Batch 0100/0391 | Cost: 0.6777\n",
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"Epoch: 007/010 | Batch 0150/0391 | Cost: 0.6288\n",
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"Epoch: 007/010 | Batch 0200/0391 | Cost: 0.6278\n",
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"Epoch: 007/010 | Batch 0250/0391 | Cost: 0.6197\n",
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"Epoch: 007/010 | Batch 0300/0391 | Cost: 0.7163\n",
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"Epoch: 007/010 | Batch 0350/0391 | Cost: 0.7765\n",
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"Epoch: 007/010 | Train: 78.272%\n",
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"Time elapsed: 1.06 min\n",
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"Epoch: 008/010 | Batch 0000/0391 | Cost: 0.5051\n",
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"Epoch: 008/010 | Batch 0050/0391 | Cost: 0.5975\n",
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"Epoch: 008/010 | Batch 0100/0391 | Cost: 0.6060\n",
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"Epoch: 008/010 | Batch 0150/0391 | Cost: 0.6763\n",
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"Epoch: 008/010 | Batch 0200/0391 | Cost: 0.5805\n",
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"Epoch: 008/010 | Batch 0250/0391 | Cost: 0.6076\n",
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"Epoch: 008/010 | Batch 0300/0391 | Cost: 0.5982\n",
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"Epoch: 008/010 | Batch 0350/0391 | Cost: 0.8050\n",
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"Epoch: 008/010 | Train: 82.530%\n",
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"Time elapsed: 1.22 min\n",
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"Epoch: 009/010 | Batch 0000/0391 | Cost: 0.4763\n",
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"Epoch: 009/010 | Batch 0050/0391 | Cost: 0.4632\n",
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"Epoch: 009/010 | Batch 0100/0391 | Cost: 0.6612\n",
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"Epoch: 009/010 | Batch 0150/0391 | Cost: 0.5145\n",
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"Epoch: 009/010 | Batch 0200/0391 | Cost: 0.6276\n",
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"Epoch: 009/010 | Batch 0250/0391 | Cost: 0.7371\n",
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"Epoch: 009/010 | Batch 0300/0391 | Cost: 0.6105\n",
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"Epoch: 009/010 | Batch 0350/0391 | Cost: 0.6129\n",
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"Epoch: 009/010 | Train: 84.632%\n",
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"Time elapsed: 1.37 min\n",
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"Epoch: 010/010 | Batch 0000/0391 | Cost: 0.4477\n",
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"Epoch: 010/010 | Batch 0050/0391 | Cost: 0.3956\n",
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"Epoch: 010/010 | Batch 0100/0391 | Cost: 0.4634\n",
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"Epoch: 010/010 | Batch 0150/0391 | Cost: 0.4358\n",
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"Epoch: 010/010 | Batch 0200/0391 | Cost: 0.5757\n",
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"Epoch: 010/010 | Batch 0250/0391 | Cost: 0.4659\n",
|
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"Epoch: 010/010 | Batch 0300/0391 | Cost: 0.4999\n",
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"Epoch: 010/010 | Batch 0350/0391 | Cost: 0.4897\n",
|
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"Epoch: 010/010 | Train: 88.534%\n",
|
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"Time elapsed: 1.51 min\n",
|
|
"Total Training Time: 1.51 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",
|
|
" 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 %04d/%04d | Cost: %.4f' \n",
|
|
" %(epoch+1, NUM_EPOCHS, batch_idx, \n",
|
|
" len(train_loader), cost))\n",
|
|
"\n",
|
|
" \n",
|
|
"\n",
|
|
" model.eval()\n",
|
|
" with torch.set_grad_enabled(False): # save memory during inference\n",
|
|
" print('Epoch: %03d/%03d | Train: %.3f%%' % (\n",
|
|
" epoch+1, NUM_EPOCHS, \n",
|
|
" compute_accuracy(model, train_loader, device=DEVICE)))\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": {
|
|
"colab_type": "text",
|
|
"id": "paaeEQHQj5xC"
|
|
},
|
|
"source": [
|
|
"## Evaluation"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {
|
|
"colab": {
|
|
"autoexec": {
|
|
"startup": false,
|
|
"wait_interval": 0
|
|
},
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 34
|
|
},
|
|
"colab_type": "code",
|
|
"executionInfo": {
|
|
"elapsed": 6514,
|
|
"status": "ok",
|
|
"timestamp": 1524976895054,
|
|
"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": "gzQMWKq5j5xE",
|
|
"outputId": "de7dc005-5eeb-4177-9f9f-d9b5d1358db9"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Test accuracy: 67.30%\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"with torch.set_grad_enabled(False): # save memory during inference\n",
|
|
" print('Test accuracy: %.2f%%' % (compute_accuracy(model, test_loader, device=DEVICE)))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"torch 1.3.1\n",
|
|
"pandas 0.24.2\n",
|
|
"PIL.Image 6.2.1\n",
|
|
"torchvision 0.4.2\n",
|
|
"matplotlib 3.1.0\n",
|
|
"numpy 1.17.4\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
|
|
}
|