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[WIP] Added TQDMCallback in callbacks #519

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[WIP] Added TQDMCallback in callbacks #519

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368 changes: 368 additions & 0 deletions examples/TQDM_Callbacks.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TensorFlow Addons Callbacks: TQDM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Overview\n",
"This notebook will demonstrate how to use TQDMCallback in TensorFlow Addons."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: ipywidgets in /usr/local/google/home/shunlin/addons/env/lib/python3.6/site-packages (7.5.1)\n",
"Requirement already satisfied: nbformat>=4.2.0 in /usr/local/google/home/shunlin/addons/env/lib/python3.6/site-packages (from ipywidgets) (4.4.0)\n",
"Requirement already satisfied: widgetsnbextension~=3.5.0 in /usr/local/google/home/shunlin/addons/env/lib/python3.6/site-packages (from ipywidgets) (3.5.1)\n",
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"Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/google/home/shunlin/addons/env/lib/python3.6/site-packages (from nbformat>=4.2.0->ipywidgets) (3.0.2)\n",
"Requirement already satisfied: jupyter-core in /usr/local/google/home/shunlin/addons/env/lib/python3.6/site-packages (from nbformat>=4.2.0->ipywidgets) (4.5.0)\n",
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"Requirement already satisfied: backcall in /usr/local/google/home/shunlin/addons/env/lib/python3.6/site-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets) (0.1.0)\n",
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"Enabling notebook extension jupyter-js-widgets/extension...\n",
" - Validating: \u001b[32mOK\u001b[0m\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"!pip install -q tensorflow-gpu==2.0.0rc0\n",
"!pip install -q tensorflow-addons~=0.5\n",
"!pip install -q tqdm\n",
"!pip install -q keras\n",
"\n",
"!pip install ipywidgets\n",
"!jupyter nbextension enable --py widgetsnbextension --sys-prefix\n",
"\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import tensorflow_addons as tfa\n",
"import keras\n",
"from keras.datasets import mnist\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout, Flatten\n",
"from keras.layers import Conv2D, MaxPooling2D\n",
"from keras import backend as K"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# the data, split between train and test sets\n",
"(x_train, y_train), (x_test, y_test) = mnist.load_data()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preprocess Data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_train shape: (60000, 28, 28, 1)\n",
"60000 train samples\n",
"10000 test samples\n"
]
}
],
"source": [
"batch_size = 128\n",
"num_classes = 10\n",
"epochs = 3\n",
"\n",
"# input image dimensions\n",
"img_rows, img_cols = 28, 28\n",
"\n",
"if K.image_data_format() == 'channels_first':\n",
" x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
" x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
" input_shape = (1, img_rows, img_cols)\n",
"else:\n",
" x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
" x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
" input_shape = (img_rows, img_cols, 1)\n",
"\n",
"x_train = x_train.astype('float32')\n",
"x_test = x_test.astype('float32')\n",
"x_train /= 255\n",
"x_test /= 255\n",
"print('x_train shape:', x_train.shape)\n",
"print(x_train.shape[0], 'train samples')\n",
"print(x_test.shape[0], 'test samples')\n",
"\n",
"# convert class vectors to binary class matrices\n",
"y_train = keras.utils.to_categorical(y_train, num_classes)\n",
"y_test = keras.utils.to_categorical(y_test, num_classes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build Simple MNIST CNN Model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Conv2D(32, kernel_size=(3, 3),\n",
" activation='relu',\n",
" input_shape=input_shape))\n",
"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"model.add(Dropout(0.25))\n",
"model.add(Flatten())\n",
"model.add(Dense(128, activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(num_classes, activation='softmax'))\n",
"\n",
"model.compile(loss=keras.losses.categorical_crossentropy,\n",
" optimizer=keras.optimizers.Adadelta(),\n",
" metrics=['accuracy'])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TQDMCallback example usage 1"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2ecc9bbf7e8d4ee2a420d4b6341a33a9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, description='Training', max=3, style=ProgressStyle(description_width='init…"
]
},
"metadata": {},
"output_type": "display_data"
},
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"\n"
]
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"text": [
"\n"
]
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{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.callbacks.History at 0x7f7cfc7693c8>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tqdm_callback = tfa.callbacks.TQDMCallback(leave_outer=True)\n",
"model.fit(x_train, y_train,\n",
" batch_size=batch_size,\n",
" epochs=epochs,\n",
" verbose=0,\n",
" callbacks = [tqdm_callback],\n",
" validation_data=(x_test, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test loss: 2.3033354560852053\n",
"Test accuracy: 0.07720000296831131\n"
]
}
],
"source": [
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"print('Test loss:', score[0])\n",
"print('Test accuracy:', score[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"pygments_lexer": "ipython3",
"version": "3.6.8"
}
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"nbformat_minor": 2
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