{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "MyujzrAv2Vpk" }, "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2020-11-12T01:21:21.450576Z", "iopub.status.busy": "2020-11-12T01:21:21.449795Z", "iopub.status.idle": "2020-11-12T01:21:21.451899Z", "shell.execute_reply": "2020-11-12T01:21:21.452339Z" }, "id": "rTUqXTqa2Vpm" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "rNnfCHh82Vpq" }, "source": [ "# TensorFlow 애드온 콜백: TQDM 진행률 표시줄" ] }, { "cell_type": "markdown", "metadata": { "id": "4qrDJoTw2Vps" }, "source": [ "\n", " \n", " \n", " \n", " \n", "
TensorFlow.org에서 보기Google Colab에서 실행하기GitHub에서 소스 보기노트북 다운로드하기
" ] }, { "cell_type": "markdown", "metadata": { "id": "bVS_PkvX2Vpt" }, "source": [ "## 개요\n", "\n", "이 노트북은 TensorFlow 애드온에서 TQDMCallback을 사용하는 방법을 보여줍니다." ] }, { "cell_type": "markdown", "metadata": { "id": "sRldODz32Vpu" }, "source": [ "## 설정" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2020-11-12T01:21:21.457966Z", "iopub.status.busy": "2020-11-12T01:21:21.457293Z", "iopub.status.idle": "2020-11-12T01:21:23.370407Z", "shell.execute_reply": "2020-11-12T01:21:23.369746Z" }, "id": "H0yZwcvcR4Gc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting tensorflow-addons\r\n", " Using cached tensorflow_addons-0.11.2-cp36-cp36m-manylinux2010_x86_64.whl (1.1 MB)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Collecting typeguard>=2.7\r\n", " Using cached typeguard-2.10.0-py3-none-any.whl (16 kB)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Installing collected packages: typeguard, tensorflow-addons\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Successfully installed tensorflow-addons-0.11.2 typeguard-2.10.0\r\n" ] } ], "source": [ "!pip install -U tensorflow-addons" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2020-11-12T01:21:23.379410Z", "iopub.status.busy": "2020-11-12T01:21:23.378605Z", "iopub.status.idle": "2020-11-12T01:21:31.147388Z", "shell.execute_reply": "2020-11-12T01:21:31.146734Z" }, "id": "etYr-Suo4KYj" }, "outputs": [], "source": [ "!pip install -q \"tqdm>=4.36.1\"\n", "\n", "import tensorflow as tf\n", "import tensorflow_addons as tfa\n", "\n", "from tensorflow.keras.datasets import mnist\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Dense, Dropout, Flatten" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2020-11-12T01:21:31.152902Z", "iopub.status.busy": "2020-11-12T01:21:31.152131Z", "iopub.status.idle": "2020-11-12T01:21:31.236048Z", "shell.execute_reply": "2020-11-12T01:21:31.236527Z" }, "id": "SfXA0mI13pSE" }, "outputs": [ { "data": { "text/plain": [ "'4.51.0'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import tqdm\n", "\n", "# quietly deep-reload tqdm\n", "import sys\n", "from IPython.lib import deepreload \n", "\n", "stdout = sys.stdout\n", "sys.stdout = open('junk','w')\n", "deepreload.reload(tqdm)\n", "sys.stdout = stdout\n", "\n", "tqdm.__version__" ] }, { "cell_type": "markdown", "metadata": { "id": "2RGuwIwe2Vp7" }, "source": [ "## 데이터 가져오기 및 정규화" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2020-11-12T01:21:31.246481Z", "iopub.status.busy": "2020-11-12T01:21:31.245567Z", "iopub.status.idle": "2020-11-12T01:21:31.725309Z", "shell.execute_reply": "2020-11-12T01:21:31.724678Z" }, "id": "qKfrsOSP2Vp8" }, "outputs": [], "source": [ "# the data, split between train and test sets\n", "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", "# normalize data\n", "x_train, x_test = x_train / 255.0, x_test / 255.0" ] }, { "cell_type": "markdown", "metadata": { "id": "ORtL0s4X2VqB" }, "source": [ "## 간단한 MNIST CNN 모델 빌드하기" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2020-11-12T01:21:31.734786Z", "iopub.status.busy": "2020-11-12T01:21:31.730650Z", "iopub.status.idle": "2020-11-12T01:21:33.562671Z", "shell.execute_reply": "2020-11-12T01:21:33.561840Z" }, "id": "z8uAGGV32VqC" }, "outputs": [], "source": [ "# build the model using the Sequential API\n", "model = Sequential()\n", "model.add(Flatten(input_shape=(28, 28)))\n", "model.add(Dense(128, activation='relu'))\n", "model.add(Dropout(0.2))\n", "model.add(Dense(10, activation='softmax'))\n", "\n", "model.compile(optimizer='adam',\n", " loss = 'sparse_categorical_crossentropy',\n", " metrics=['accuracy'])" ] }, { "cell_type": "markdown", "metadata": { "id": "YWOnH1ga2VqF" }, "source": [ "## 기본 TQDMCallback 사용법" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2020-11-12T01:21:33.569725Z", "iopub.status.busy": "2020-11-12T01:21:33.568966Z", "iopub.status.idle": "2020-11-12T01:21:57.155324Z", "shell.execute_reply": "2020-11-12T01:21:57.150970Z" }, "id": "Vl_oj_OW2VqG" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7011f231059c4177bc2ca4f448ba0ead", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, layout=Layout(flex='2'), max=10.0), HTML(value…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "401af86bde0048f8b83277f476ef4ba1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(HTML(value=''), FloatProgress(value=0.0, layout=Layout(flex='2'), max=938.0), HTML(value='')), …" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Epoch 2/10\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1bdaae161d114fe7af9689dc7ec0b3e9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(HTML(value=''), FloatProgress(value=0.0, layout=Layout(flex='2'), max=938.0), HTML(value='')), …" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Epoch 3/10\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6674d729ac2547d6b551896e972a1437", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(HTML(value=''), FloatProgress(value=0.0, layout=Layout(flex='2'), max=938.0), HTML(value='')), …" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Epoch 4/10\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1e42c5be6a5140e5903c9f55918779f5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(HTML(value=''), FloatProgress(value=0.0, layout=Layout(flex='2'), max=938.0), HTML(value='')), …" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Epoch 5/10\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e04daa1648944492914affab66f4c3a3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(HTML(value=''), FloatProgress(value=0.0, layout=Layout(flex='2'), max=938.0), HTML(value='')), …" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Epoch 6/10\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ae216bd3b2b0481c98157623471d3e10", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(HTML(value=''), FloatProgress(value=0.0, layout=Layout(flex='2'), max=938.0), HTML(value='')), …" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Epoch 7/10\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a2ce43e3ae2243479c0f7a7078739752", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(HTML(value=''), FloatProgress(value=0.0, layout=Layout(flex='2'), max=938.0), HTML(value='')), …" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Epoch 8/10\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3f947f741fd04255b3a8f43b9016620c", "version_major": 2, "version_minor": 0 }, 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"display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# initialize tqdm callback with default parameters\n", "tqdm_callback = tfa.callbacks.TQDMProgressBar()\n", "\n", "# train the model with tqdm_callback\n", "# make sure to set verbose = 0 to disable\n", "# the default progress bar.\n", "model.fit(x_train, y_train,\n", " batch_size=64,\n", " epochs=10,\n", " verbose=0,\n", " callbacks=[tqdm_callback],\n", " validation_data=(x_test, y_test))" ] }, { "cell_type": "markdown", "metadata": { "id": "uFvBfwJN2VqK" }, "source": [ "**아래는 위의 셀을 실행할 때 예상되는 출력입니다.** ![TQDM Progress Bar Figure](https://raw.githubusercontent.com/tensorflow/addons/59961669a0e21eb4c045d4ad38d008a529d566c2/docs/tutorials/assets/tqdm_progress_bar_demo.png)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2020-11-12T01:21:57.160271Z", "iopub.status.busy": "2020-11-12T01:21:57.159589Z", "iopub.status.idle": "2020-11-12T01:21:57.497809Z", "shell.execute_reply": "2020-11-12T01:21:57.497268Z" }, "id": "Np3dD8bhe10E" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "278d33cbfb5a45c4bf162c177f9d9f39", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(HTML(value='Evaluating'), FloatProgress(value=0.0, layout=Layout(flex='2'), max=157.0), HTML(va…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/plain": [ "[0.07110489159822464, 0.9794999957084656]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# TQDMProgressBar() also works with evaluate()\n", "model.evaluate(x_test, y_test, batch_size=64, callbacks=[tqdm_callback], verbose=0)" ] }, { "cell_type": "markdown", "metadata": { "id": "36WRBMo7e10I" }, "source": [ "**아래는 위의 셀을 실행할 때 예상되는 출력입니다.** ![TQDM Evaluate Progress Bar Figure](https://user-images.githubusercontent.com/20843596/79412655-a57cef00-7fc3-11ea-9bea-ade8ee8dad58.PNG)" ] } ], "metadata": { "colab": { "name": "tqdm_progress_bar.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "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": "3.6.9" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "0484f9fd646d4f54850adb41c2570c1b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": 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