{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "lEMI1oxe8KNy"
},
"source": [
"Updated 21/Nov/2021 by Yoshihisa Nitta "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "X4_Pjeum8NUa"
},
"source": [
"\n",
"# Analysis of Variational Auto Encoder for CelebA dataset with Tensorflow 2 on Google Colab\n",
"\n",
"Train Variational Auto Encoder further on CelebA dataset.\n",
"It is assumed that it is in the state after executing VAE_CelebA_Train2.ipynb.\n",
"\n",
"## CelebA データセットに対する Variational Auto Encoder をGoogle Colab 上の Tensorflow 2 で解析する\n",
"\n",
"CelebA データセットに対して変分オートエンコーダを学習させた結果を解析する。\n",
"VAE_CelebA_Train2.ipynb を実行した後の状態であることを前提としている。"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"executionInfo": {
"elapsed": 3,
"status": "ok",
"timestamp": 1637560371390,
"user": {
"displayName": "Yoshihisa Nitta",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
"userId": "15888006800030996813"
},
"user_tz": -540
},
"id": "CnbfjOX_7wEa"
},
"outputs": [],
"source": [
"#! pip install tensorflow==2.7.0"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 3182,
"status": "ok",
"timestamp": 1637560374570,
"user": {
"displayName": "Yoshihisa Nitta",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
"userId": "15888006800030996813"
},
"user_tz": -540
},
"id": "woOXJdh57sIx",
"outputId": "56dd187e-9172-429f-d064-eb320815fd5b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.7.0\n"
]
}
],
"source": [
"%tensorflow_version 2.x\n",
"\n",
"import tensorflow as tf\n",
"print(tf.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bXj23n8r9Tac"
},
"source": [
"# Check the Google Colab runtime environment\n",
"\n",
"## Google Colab 実行環境を調べる"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 485,
"status": "ok",
"timestamp": 1637560375050,
"user": {
"displayName": "Yoshihisa Nitta",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
"userId": "15888006800030996813"
},
"user_tz": -540
},
"id": "4xRE6QCs9QO1",
"outputId": "81cbe2e6-755f-4f81-ef14-933b0f3d291b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mon Nov 22 05:52:54 2021 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 495.44 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 34C P0 27W / 250W | 0MiB / 16280MiB | 0% Default |\n",
"| | | N/A |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"| No running processes found |\n",
"+-----------------------------------------------------------------------------+\n",
"processor\t: 0\n",
"vendor_id\t: GenuineIntel\n",
"cpu family\t: 6\n",
"model\t\t: 85\n",
"model name\t: Intel(R) Xeon(R) CPU @ 2.00GHz\n",
"stepping\t: 3\n",
"microcode\t: 0x1\n",
"cpu MHz\t\t: 2000.180\n",
"cache size\t: 39424 KB\n",
"physical id\t: 0\n",
"siblings\t: 2\n",
"core id\t\t: 0\n",
"cpu cores\t: 1\n",
"apicid\t\t: 0\n",
"initial apicid\t: 0\n",
"fpu\t\t: yes\n",
"fpu_exception\t: yes\n",
"cpuid level\t: 13\n",
"wp\t\t: yes\n",
"flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities\n",
"bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa\n",
"bogomips\t: 4000.36\n",
"clflush size\t: 64\n",
"cache_alignment\t: 64\n",
"address sizes\t: 46 bits physical, 48 bits virtual\n",
"power management:\n",
"\n",
"processor\t: 1\n",
"vendor_id\t: GenuineIntel\n",
"cpu family\t: 6\n",
"model\t\t: 85\n",
"model name\t: Intel(R) Xeon(R) CPU @ 2.00GHz\n",
"stepping\t: 3\n",
"microcode\t: 0x1\n",
"cpu MHz\t\t: 2000.180\n",
"cache size\t: 39424 KB\n",
"physical id\t: 0\n",
"siblings\t: 2\n",
"core id\t\t: 0\n",
"cpu cores\t: 1\n",
"apicid\t\t: 1\n",
"initial apicid\t: 1\n",
"fpu\t\t: yes\n",
"fpu_exception\t: yes\n",
"cpuid level\t: 13\n",
"wp\t\t: yes\n",
"flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities\n",
"bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa\n",
"bogomips\t: 4000.36\n",
"clflush size\t: 64\n",
"cache_alignment\t: 64\n",
"address sizes\t: 46 bits physical, 48 bits virtual\n",
"power management:\n",
"\n",
"Ubuntu 18.04.5 LTS \\n \\l\n",
"\n",
" total used free shared buff/cache available\n",
"Mem: 12G 758M 9G 1.2M 2.0G 11G\n",
"Swap: 0B 0B 0B\n"
]
}
],
"source": [
"! nvidia-smi\n",
"! cat /proc/cpuinfo\n",
"! cat /etc/issue\n",
"! free -h"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zeGrymCg9ZtL"
},
"source": [
"# Mount Google Drive from Google Colab\n",
"\n",
"## Google Colab から GoogleDrive をマウントする"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 53294,
"status": "ok",
"timestamp": 1637560428343,
"user": {
"displayName": "Yoshihisa Nitta",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
"userId": "15888006800030996813"
},
"user_tz": -540
},
"id": "9B4MX6GC9Vf9",
"outputId": "94dcefe3-65f3-4586-bc42-02c39bbdd5d8"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mounted at /content/drive\n"
]
}
],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 8,
"status": "ok",
"timestamp": 1637560428344,
"user": {
"displayName": "Yoshihisa Nitta",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
"userId": "15888006800030996813"
},
"user_tz": -540
},
"id": "P4voAIIh9aiO",
"outputId": "90c36ba2-1ff3-42fb-e72f-20e651d557ff"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MyDrive Shareddrives\n"
]
}
],
"source": [
"! ls /content/drive"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cED1p2U998IE"
},
"source": [
"# Download source file from Google Drive or nw.tsuda.ac.jp\n",
"\n",
"Download from nw.tsuda.ac.jp above only if the specifications of Google Drive change and you cannot download from Google Drive.\n",
"\n",
"# Google Drive または nw.tsuda.ac.jp からファイルをダウンロードする\n",
"\n",
"Google Drive の仕様が変わってダウンロードができない場合にのみ、上の nw.tsuda.ac.jp からダウンロードすること。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 2498,
"status": "ok",
"timestamp": 1637560430838,
"user": {
"displayName": "Yoshihisa Nitta",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
"userId": "15888006800030996813"
},
"user_tz": -540
},
"id": "K13qk7Td9mH_",
"outputId": "4794f114-2577-4d00-c99b-c9c3dccfa639"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=1ZCihR7JkMOity4wCr66ZCp-3ZOlfwwo3\n",
"To: /content/nw/VariationalAutoEncoder.py\n",
"\r",
" 0% 0.00/18.7k [00:00, ?B/s]\r",
"100% 18.7k/18.7k [00:00<00:00, 16.5MB/s]\n"
]
}
],
"source": [
"# Download source file\n",
"nw_path = './nw'\n",
"! rm -rf {nw_path}\n",
"! mkdir -p {nw_path}\n",
"\n",
"if True: # from Google Drive\n",
" url_model = 'https://drive.google.com/uc?id=1ZCihR7JkMOity4wCr66ZCp-3ZOlfwwo3'\n",
" ! (cd {nw_path}; gdown {url_model})\n",
"else: # from nw.tsuda.ac.jp\n",
" URL_NW = 'https://nw.tsuda.ac.jp/lec/GoogleColab/pub'\n",
" url_model = f'{URL_NW}/models/VariationalAutoEncoder.py'\n",
" ! wget -nd {url_model} -P {nw_path}"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 8,
"status": "ok",
"timestamp": 1637560430839,
"user": {
"displayName": "Yoshihisa Nitta",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
"userId": "15888006800030996813"
},
"user_tz": -540
},
"id": "WmOyk35j-AZ7",
"outputId": "aad37661-56aa-42dc-8b3a-26dab49ca74b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"import tensorflow as tf\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import os\n",
"import pickle\n",
"import datetime\n",
"\n",
"class Sampling(tf.keras.layers.Layer):\n",
" def __init__(self, **kwargs):\n",
" super().__init__(**kwargs)\n",
"\n",
" def call(self, inputs):\n",
" mu, log_var = inputs\n",
" epsilon = tf.keras.backend.random_normal(shape=tf.keras.backend.shape(mu), mean=0., stddev=1.)\n",
" return mu + tf.keras.backend.exp(log_var / 2) * epsilon\n",
"\n",
"\n",
"class VAEModel(tf.keras.models.Model):\n",
" def __init__(self, encoder, decoder, r_loss_factor, **kwargs):\n",
" super().__init__(**kwargs)\n",
" self.encoder = encoder\n",
" self.decoder = decoder\n",
" self.r_loss_factor = r_loss_factor\n",
"\n",
"\n",
" @tf.function\n",
" def loss_fn(self, x):\n",
" z_mean, z_log_var, z = self.encoder(x)\n",
" reconstruction = self.decoder(z)\n",
" reconstruction_loss = tf.reduce_mean(\n",
" tf.square(x - reconstruction), axis=[1,2,3]\n",
" ) * self.r_loss_factor\n",
" kl_loss = tf.reduce_sum(\n",
" 1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var),\n",
" axis = 1\n",
" ) * (-0.5)\n",
" total_loss = reconstruction_loss + kl_loss\n",
" return total_loss, reconstruction_loss, kl_loss\n",
"\n",
"\n",
" @tf.function\n",
" def compute_loss_and_grads(self, x):\n",
" with tf.GradientTape() as tape:\n",
" total_loss, reconstruction_loss, kl_loss = self.loss_fn(x)\n",
" grads = tape.gradient(total_loss, self.trainable_weights)\n",
" return total_loss, reconstruction_loss, kl_loss, grads\n",
"\n",
"\n",
" def train_step(self, data):\n",
" if isinstance(data, tuple):\n",
" data = data[0]\n",
" total_loss, reconstruction_loss, kl_loss, grads = self.compute_loss_and_grads(data)\n",
" self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n",
" return {\n",
" \"loss\": tf.math.reduce_mean(total_loss),\n",
" \"reconstruction_loss\": tf.math.reduce_mean(reconstruction_loss),\n",
" \"kl_loss\": tf.math.reduce_mean(kl_loss),\n",
" }\n",
"\n",
" def call(self,inputs):\n",
" _, _, z = self.encoder(inputs)\n",
" return self.decoder(z)\n",
"\n",
"\n",
"class VariationalAutoEncoder():\n",
" def __init__(self, \n",
" input_dim,\n",
" encoder_conv_filters,\n",
" encoder_conv_kernel_size,\n",
" encoder_conv_strides,\n",
" decoder_conv_t_filters,\n",
" decoder_conv_t_kernel_size,\n",
" decoder_conv_t_strides,\n",
" z_dim,\n",
" r_loss_factor, ### added\n",
" use_batch_norm = False,\n",
" use_dropout = False,\n",
" epoch = 0\n",
" ):\n",
" self.name = 'variational_autoencoder'\n",
" self.input_dim = input_dim\n",
" self.encoder_conv_filters = encoder_conv_filters\n",
" self.encoder_conv_kernel_size = encoder_conv_kernel_size\n",
" self.encoder_conv_strides = encoder_conv_strides\n",
" self.decoder_conv_t_filters = decoder_conv_t_filters\n",
" self.decoder_conv_t_kernel_size = decoder_conv_t_kernel_size\n",
" self.decoder_conv_t_strides = decoder_conv_t_strides\n",
" self.z_dim = z_dim\n",
" self.r_loss_factor = r_loss_factor ### added\n",
" \n",
" self.use_batch_norm = use_batch_norm\n",
" self.use_dropout = use_dropout\n",
"\n",
" self.epoch = epoch\n",
" \n",
" self.n_layers_encoder = len(encoder_conv_filters)\n",
" self.n_layers_decoder = len(decoder_conv_t_filters)\n",
" \n",
" self._build()\n",
" \n",
"\n",
" def _build(self):\n",
" ### THE ENCODER\n",
" encoder_input = tf.keras.layers.Input(shape=self.input_dim, name='encoder_input')\n",
" x = encoder_input\n",
" \n",
" for i in range(self.n_layers_encoder):\n",
" x = conv_layer = tf.keras.layers.Conv2D(\n",
" filters = self.encoder_conv_filters[i],\n",
" kernel_size = self.encoder_conv_kernel_size[i],\n",
" strides = self.encoder_conv_strides[i],\n",
" padding = 'same',\n",
" name = 'encoder_conv_' + str(i)\n",
" )(x)\n",
"\n",
" if self.use_batch_norm: ### The order of layers is opposite to AutoEncoder\n",
" x = tf.keras.layers.BatchNormalization()(x) ### AE: LeakyReLU -> BatchNorm\n",
" x = tf.keras.layers.LeakyReLU()(x) ### VAE: BatchNorm -> LeakyReLU\n",
" \n",
" if self.use_dropout:\n",
" x = tf.keras.layers.Dropout(rate = 0.25)(x)\n",
" \n",
" shape_before_flattening = tf.keras.backend.int_shape(x)[1:]\n",
" \n",
" x = tf.keras.layers.Flatten()(x)\n",
" \n",
" self.mu = tf.keras.layers.Dense(self.z_dim, name='mu')(x)\n",
" self.log_var = tf.keras.layers.Dense(self.z_dim, name='log_var')(x) \n",
" self.z = Sampling(name='encoder_output')([self.mu, self.log_var])\n",
" \n",
" self.encoder = tf.keras.models.Model(encoder_input, [self.mu, self.log_var, self.z], name='encoder')\n",
" \n",
" \n",
" ### THE DECODER\n",
" decoder_input = tf.keras.layers.Input(shape=(self.z_dim,), name='decoder_input')\n",
" x = decoder_input\n",
" x = tf.keras.layers.Dense(np.prod(shape_before_flattening))(x)\n",
" x = tf.keras.layers.Reshape(shape_before_flattening)(x)\n",
" \n",
" for i in range(self.n_layers_decoder):\n",
" x = conv_t_layer = tf.keras.layers.Conv2DTranspose(\n",
" filters = self.decoder_conv_t_filters[i],\n",
" kernel_size = self.decoder_conv_t_kernel_size[i],\n",
" strides = self.decoder_conv_t_strides[i],\n",
" padding = 'same',\n",
" name = 'decoder_conv_t_' + str(i)\n",
" )(x)\n",
" \n",
" if i < self.n_layers_decoder - 1:\n",
" if self.use_batch_norm: ### The order of layers is opposite to AutoEncoder\n",
" x = tf.keras.layers.BatchNormalization()(x) ### AE: LeakyReLU -> BatchNorm\n",
" x = tf.keras.layers.LeakyReLU()(x) ### VAE: BatchNorm -> LeakyReLU \n",
" if self.use_dropout:\n",
" x = tf.keras.layers.Dropout(rate=0.25)(x)\n",
" else:\n",
" x = tf.keras.layers.Activation('sigmoid')(x)\n",
" \n",
" decoder_output = x\n",
" self.decoder = tf.keras.models.Model(decoder_input, decoder_output, name='decoder') ### added (name)\n",
" \n",
" ### THE FULL AUTOENCODER\n",
" self.model = VAEModel(self.encoder, self.decoder, self.r_loss_factor)\n",
" \n",
" \n",
" def save(self, folder):\n",
" self.save_params(os.path.join(folder, 'params.pkl'))\n",
" self.save_weights(folder)\n",
"\n",
"\n",
" @staticmethod\n",
" def load(folder, epoch=None): # VariationalAutoEncoder.load(folder)\n",
" params = VariationalAutoEncoder.load_params(os.path.join(folder, 'params.pkl'))\n",
" VAE = VariationalAutoEncoder(*params)\n",
" if epoch is None:\n",
" VAE.load_weights(folder)\n",
" else:\n",
" VAE.load_weights(folder, epoch-1)\n",
" VAE.epoch = epoch\n",
" return VAE\n",
"\n",
" \n",
" def save_params(self, filepath):\n",
" dpath, fname = os.path.split(filepath)\n",
" if dpath != '' and not os.path.exists(dpath):\n",
" os.makedirs(dpath)\n",
" with open(filepath, 'wb') as f:\n",
" pickle.dump([\n",
" self.input_dim,\n",
" self.encoder_conv_filters,\n",
" self.encoder_conv_kernel_size,\n",
" self.encoder_conv_strides,\n",
" self.decoder_conv_t_filters,\n",
" self.decoder_conv_t_kernel_size,\n",
" self.decoder_conv_t_strides,\n",
" self.z_dim,\n",
" self.r_loss_factor,\n",
" self.use_batch_norm,\n",
" self.use_dropout,\n",
" self.epoch\n",
" ], f)\n",
"\n",
"\n",
" @staticmethod\n",
" def load_params(filepath):\n",
" with open(filepath, 'rb') as f:\n",
" params = pickle.load(f)\n",
" return params\n",
"\n",
"\n",
" def save_weights(self, folder, epoch=None):\n",
" if epoch is None:\n",
" self.save_model_weights(self.encoder, os.path.join(folder, f'weights/encoder-weights.h5'))\n",
" self.save_model_weights(self.decoder, os.path.join(folder, f'weights/decoder-weights.h5'))\n",
" else:\n",
" self.save_model_weights(self.encoder, os.path.join(folder, f'weights/encoder-weights_{epoch}.h5'))\n",
" self.save_model_weights(self.decoder, os.path.join(folder, f'weights/decoder-weights_{epoch}.h5'))\n",
"\n",
"\n",
" def save_model_weights(self, model, filepath):\n",
" dpath, fname = os.path.split(filepath)\n",
" if dpath != '' and not os.path.exists(dpath):\n",
" os.makedirs(dpath)\n",
" model.save_weights(filepath)\n",
"\n",
"\n",
" def load_weights(self, folder, epoch=None):\n",
" if epoch is None:\n",
" self.encoder.load_weights(os.path.join(folder, f'weights/encoder-weights.h5'))\n",
" self.decoder.load_weights(os.path.join(folder, f'weights/decoder-weights.h5'))\n",
" else:\n",
" self.encoder.load_weights(os.path.join(folder, f'weights/encoder-weights_{epoch}.h5'))\n",
" self.decoder.load_weights(os.path.join(folder, f'weights/decoder-weights_{epoch}.h5'))\n",
"\n",
"\n",
" def save_images(self, imgs, filepath):\n",
" z_mean, z_log_var, z = self.encoder.predict(imgs)\n",
" reconst_imgs = self.decoder.predict(z)\n",
" txts = [ f'{p[0]:.3f}, {p[1]:.3f}' for p in z ]\n",
" AutoEncoder.showImages(imgs, reconst_imgs, txts, 1.4, 1.4, 0.5, filepath)\n",
" \n",
"\n",
" def compile(self, learning_rate):\n",
" self.learning_rate = learning_rate\n",
" optimizer = tf.keras.optimizers.Adam(lr=learning_rate)\n",
" self.model.compile(optimizer=optimizer) # CAUTION!!!: loss(y_true, y_pred) function is not specified.\n",
" \n",
" \n",
" def train_with_fit(\n",
" self,\n",
" x_train,\n",
" batch_size,\n",
" epochs,\n",
" run_folder='run/'\n",
" ):\n",
" history = self.model.fit(\n",
" x_train,\n",
" x_train,\n",
" batch_size = batch_size,\n",
" shuffle=True,\n",
" initial_epoch = self.epoch,\n",
" epochs = epochs\n",
" )\n",
" if (self.epoch < epochs):\n",
" self.epoch = epochs\n",
"\n",
" if run_folder != None:\n",
" self.save(run_folder)\n",
" self.save_weights(run_folder, self.epoch-1)\n",
" \n",
" return history\n",
"\n",
"\n",
" def train_generator_with_fit(\n",
" self,\n",
" data_flow,\n",
" epochs,\n",
" run_folder='run/'\n",
" ):\n",
" history = self.model.fit(\n",
" data_flow,\n",
" initial_epoch = self.epoch,\n",
" epochs = epochs\n",
" )\n",
" if (self.epoch < epochs):\n",
" self.epoch = epochs\n",
"\n",
" if run_folder != None:\n",
" self.save(run_folder)\n",
" self.save_weights(run_folder, self.epoch-1)\n",
" \n",
" return history\n",
"\n",
"\n",
" def train_tf(\n",
" self,\n",
" x_train,\n",
" batch_size = 32,\n",
" epochs = 10,\n",
" shuffle = False,\n",
" run_folder = 'run/',\n",
" optimizer = None,\n",
" save_epoch_interval = 100,\n",
" validation_data = None\n",
" ):\n",
" start_time = datetime.datetime.now()\n",
" steps = x_train.shape[0] // batch_size\n",
"\n",
" total_losses = []\n",
" reconstruction_losses = []\n",
" kl_losses = []\n",
"\n",
" val_total_losses = []\n",
" val_reconstruction_losses = []\n",
" val_kl_losses = []\n",
"\n",
" for epoch in range(self.epoch, epochs):\n",
" epoch_loss = 0\n",
" indices = tf.range(x_train.shape[0], dtype=tf.int32)\n",
" if shuffle:\n",
" indices = tf.random.shuffle(indices)\n",
" x_ = x_train[indices]\n",
"\n",
" step_total_losses = []\n",
" step_reconstruction_losses = []\n",
" step_kl_losses = []\n",
" for step in range(steps):\n",
" start = batch_size * step\n",
" end = start + batch_size\n",
"\n",
" total_loss, reconstruction_loss, kl_loss, grads = self.model.compute_loss_and_grads(x_[start:end])\n",
" optimizer.apply_gradients(zip(grads, self.model.trainable_weights))\n",
" \n",
" step_total_losses.append(np.mean(total_loss))\n",
" step_reconstruction_losses.append(np.mean(reconstruction_loss))\n",
" step_kl_losses.append(np.mean(kl_loss))\n",
" \n",
" epoch_total_loss = np.mean(step_total_losses)\n",
" epoch_reconstruction_loss = np.mean(step_reconstruction_losses)\n",
" epoch_kl_loss = np.mean(step_kl_losses)\n",
"\n",
" total_losses.append(epoch_total_loss)\n",
" reconstruction_losses.append(epoch_reconstruction_loss)\n",
" kl_losses.append(epoch_kl_loss)\n",
"\n",
" val_str = ''\n",
" if not validation_data is None:\n",
" x_val = validation_data\n",
" tl, rl, kl = self.model.loss_fn(x_val)\n",
" val_tl = np.mean(tl)\n",
" val_rl = np.mean(rl)\n",
" val_kl = np.mean(kl)\n",
" val_total_losses.append(val_tl)\n",
" val_reconstruction_losses.append(val_rl)\n",
" val_kl_losses.append(val_kl)\n",
" val_str = f'val loss total {val_tl:.3f} reconstruction {val_rl:.3f} kl {val_kl:.3f} '\n",
"\n",
" if (epoch+1) % save_epoch_interval == 0 and run_folder != None:\n",
" self.save(run_folder)\n",
" self.save_weights(run_folder, self.epoch)\n",
"\n",
" elapsed_time = datetime.datetime.now() - start_time\n",
" print(f'{epoch+1}/{epochs} {steps} loss: total {epoch_total_loss:.3f} reconstruction {epoch_reconstruction_loss:.3f} kl {epoch_kl_loss:.3f} {val_str}{elapsed_time}')\n",
"\n",
" self.epoch += 1\n",
"\n",
" if run_folder != None:\n",
" self.save(run_folder)\n",
" self.save_weights(run_folder, self.epoch-1)\n",
"\n",
" dic = { 'loss' : total_losses, 'reconstruction_loss' : reconstruction_losses, 'kl_loss' : kl_losses }\n",
" if not validation_data is None:\n",
" dic['val_loss'] = val_total_losses\n",
" dic['val_reconstruction_loss'] = val_reconstruction_losses\n",
" dic['val_kl_loss'] = val_kl_losses\n",
"\n",
" return dic\n",
" \n",
"\n",
" def train_tf_generator(\n",
" self,\n",
" data_flow,\n",
" epochs = 10,\n",
" run_folder = 'run/',\n",
" optimizer = None,\n",
" save_epoch_interval = 100,\n",
" validation_data_flow = None\n",
" ):\n",
" start_time = datetime.datetime.now()\n",
" steps = len(data_flow)\n",
"\n",
" total_losses = []\n",
" reconstruction_losses = []\n",
" kl_losses = []\n",
"\n",
" val_total_losses = []\n",
" val_reconstruction_losses = []\n",
" val_kl_losses = []\n",
"\n",
" for epoch in range(self.epoch, epochs):\n",
" epoch_loss = 0\n",
"\n",
" step_total_losses = []\n",
" step_reconstruction_losses = []\n",
" step_kl_losses = []\n",
"\n",
" for step in range(steps):\n",
" x, _ = next(data_flow)\n",
"\n",
" total_loss, reconstruction_loss, kl_loss, grads = self.model.compute_loss_and_grads(x)\n",
" optimizer.apply_gradients(zip(grads, self.model.trainable_weights))\n",
" \n",
" step_total_losses.append(np.mean(total_loss))\n",
" step_reconstruction_losses.append(np.mean(reconstruction_loss))\n",
" step_kl_losses.append(np.mean(kl_loss))\n",
" \n",
" epoch_total_loss = np.mean(step_total_losses)\n",
" epoch_reconstruction_loss = np.mean(step_reconstruction_losses)\n",
" epoch_kl_loss = np.mean(step_kl_losses)\n",
"\n",
" total_losses.append(epoch_total_loss)\n",
" reconstruction_losses.append(epoch_reconstruction_loss)\n",
" kl_losses.append(epoch_kl_loss)\n",
"\n",
" val_str = ''\n",
" if not validation_data_flow is None:\n",
" step_val_tl = []\n",
" step_val_rl = []\n",
" step_val_kl = []\n",
" for i in range(len(validation_data_flow)):\n",
" x, _ = next(validation_data_flow)\n",
" tl, rl, kl = self.model.loss_fn(x)\n",
" step_val_tl.append(np.mean(tl))\n",
" step_val_rl.append(np.mean(rl))\n",
" step_val_kl.append(np.mean(kl))\n",
" val_tl = np.mean(step_val_tl)\n",
" val_rl = np.mean(step_val_rl)\n",
" val_kl = np.mean(step_val_kl)\n",
" val_total_losses.append(val_tl)\n",
" val_reconstruction_losses.append(val_rl)\n",
" val_kl_losses.append(val_kl)\n",
" val_str = f'val loss total {val_tl:.3f} reconstruction {val_rl:.3f} kl {val_kl:.3f} '\n",
"\n",
" if (epoch+1) % save_epoch_interval == 0 and run_folder != None:\n",
" self.save(run_folder)\n",
" self.save_weights(run_folder, self.epoch)\n",
"\n",
" elapsed_time = datetime.datetime.now() - start_time\n",
" print(f'{epoch+1}/{epochs} {steps} loss: total {epoch_total_loss:.3f} reconstruction {epoch_reconstruction_loss:.3f} kl {epoch_kl_loss:.3f} {val_str}{elapsed_time}')\n",
"\n",
" self.epoch += 1\n",
"\n",
" if run_folder != None:\n",
" self.save(run_folder)\n",
" self.save_weights(run_folder, self.epoch-1)\n",
"\n",
" dic = { 'loss' : total_losses, 'reconstruction_loss' : reconstruction_losses, 'kl_loss' : kl_losses }\n",
" if not validation_data_flow is None:\n",
" dic['val_loss'] = val_total_losses\n",
" dic['val_reconstruction_loss'] = val_reconstruction_losses\n",
" dic['val_kl_loss'] = val_kl_losses\n",
"\n",
" return dic\n",
"\n",
"\n",
" @staticmethod\n",
" def showImages(imgs1, imgs2, txts, w, h, vskip=0.5, filepath=None):\n",
" n = len(imgs1)\n",
" fig, ax = plt.subplots(2, n, figsize=(w * n, (2+vskip) * h))\n",
" for i in range(n):\n",
" if n == 1:\n",
" axis = ax[0]\n",
" else:\n",
" axis = ax[0][i]\n",
" img = imgs1[i].squeeze()\n",
" axis.imshow(img, cmap='gray_r')\n",
" axis.axis('off')\n",
"\n",
" axis.text(0.5, -0.35, txts[i], fontsize=10, ha='center', transform=axis.transAxes)\n",
"\n",
" if n == 1:\n",
" axis = ax[1]\n",
" else:\n",
" axis = ax[1][i]\n",
" img2 = imgs2[i].squeeze()\n",
" axis.imshow(img2, cmap='gray_r')\n",
" axis.axis('off')\n",
"\n",
" if not filepath is None:\n",
" dpath, fname = os.path.split(filepath)\n",
" if dpath != '' and not os.path.exists(dpath):\n",
" os.makedirs(dpath)\n",
" fig.savefig(filepath, dpi=600)\n",
" plt.close()\n",
" else:\n",
" plt.show()\n",
"\n",
" @staticmethod\n",
" def plot_history(vals, labels):\n",
" colors = ['red', 'blue', 'green', 'orange', 'black', 'pink']\n",
" n = len(vals)\n",
" fig, ax = plt.subplots(1, 1, figsize=(9,4))\n",
" for i in range(n):\n",
" ax.plot(vals[i], c=colors[i], label=labels[i])\n",
" ax.legend(loc='upper right')\n",
" ax.set_xlabel('epochs')\n",
" # ax[0].set_ylabel('loss')\n",
" \n",
" plt.show()\n"
]
}
],
"source": [
"! cat {nw_path}/VariationalAutoEncoder.py"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K29zyLNo-JG-"
},
"source": [
"# Preparing CelebA dataset\n",
"\n",
"Official WWW of CelebA dataset:\n",
"\n",
"https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html\n",
"\n",
"\n",
"Google Drive of CelebA dataset:\n",
"\n",
"https://drive.google.com/drive/folders/0B7EVK8r0v71pWEZsZE9oNnFzTm8?resourcekey=0-5BR16BdXnb8hVj6CNHKzLg\n",
"\n",
"\n",
"img_align_celeba.zip mirrored on my Google Drive: \n",
"\n",
"https://drive.google.com/uc?id=1LFKeoI-hb96jlV0K10dO1o04iQPBoFdx\n",
"\n",
"\n",
"## CelebA データセットを用意する\n",
"\n",
"CelebA データセットの公式ページ:\n",
"\n",
"https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html\n",
"\n",
"\n",
"CelebA データセットのGoogle Drive:\n",
"\n",
"https://drive.google.com/drive/folders/0B7EVK8r0v71pWEZsZE9oNnFzTm8?resourcekey=0-5BR16BdXnb8hVj6CNHKzLg\n",
"\n",
"\n",
"自分の Google Drive 上にミラーした img_align_celeba.zip: \n",
"\n",
"https://drive.google.com/uc?id=1LFKeoI-hb96jlV0K10dO1o04iQPBoFdx\n",
""
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 29746,
"status": "ok",
"timestamp": 1637560460582,
"user": {
"displayName": "Yoshihisa Nitta",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
"userId": "15888006800030996813"
},
"user_tz": -540
},
"id": "g99ZWERz-DP8",
"outputId": "9e61035d-c4b3-4edb-984c-e23f87c5acf5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=1LFKeoI-hb96jlV0K10dO1o04iQPBoFdx\n",
"To: /content/img_align_celeba.zip\n",
"100% 1.44G/1.44G [00:22<00:00, 64.1MB/s]\n"
]
}
],
"source": [
"# Download img_align_celeba.zip from GoogleDrive\n",
"\n",
"MIRRORED_URL = 'https://drive.google.com/uc?id=1LFKeoI-hb96jlV0K10dO1o04iQPBoFdx'\n",
"\n",
"! gdown {MIRRORED_URL}"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 13,
"status": "ok",
"timestamp": 1637560460582,
"user": {
"displayName": "Yoshihisa Nitta",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
"userId": "15888006800030996813"
},
"user_tz": -540
},
"id": "xXFiRu9y-QSj",
"outputId": "79e36be8-5e5e-4c29-e51f-4e9a5129cb15"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total 1409676\n",
"drwx------ 6 root root 4096 Nov 22 05:53 drive\n",
"-rw-r--r-- 1 root root 1443490838 Nov 22 05:54 img_align_celeba.zip\n",
"drwxr-xr-x 2 root root 4096 Nov 22 05:53 nw\n",
"drwxr-xr-x 1 root root 4096 Nov 18 14:36 sample_data\n"
]
}
],
"source": [
"! ls -l"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"executionInfo": {
"elapsed": 10,
"status": "ok",
"timestamp": 1637560460582,
"user": {
"displayName": "Yoshihisa Nitta",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
"userId": "15888006800030996813"
},
"user_tz": -540
},
"id": "0SnX7ijr-UBg"
},
"outputs": [],
"source": [
"DATA_DIR = 'data'\n",
"DATA_SUBDIR = 'img_align_celeba'"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"executionInfo": {
"elapsed": 17992,
"status": "ok",
"timestamp": 1637560478564,
"user": {
"displayName": "Yoshihisa Nitta",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
"userId": "15888006800030996813"
},
"user_tz": -540
},
"id": "CeMWTJWeAXVq"
},
"outputs": [],
"source": [
"! rm -rf {DATA_DIR}\n",
"! unzip -d {DATA_DIR} -q {DATA_SUBDIR}.zip"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 2181,
"status": "ok",
"timestamp": 1637560480735,
"user": {
"displayName": "Yoshihisa Nitta",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
"userId": "15888006800030996813"
},
"user_tz": -540
},
"id": "fDN_8kaFAZPV",
"outputId": "565fdabb-fbc2-401a-8505-8385cbdf9f7f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total 1737936\n",
"-rw-r--r-- 1 root root 11440 Sep 28 2015 000001.jpg\n",
"-rw-r--r-- 1 root root 7448 Sep 28 2015 000002.jpg\n",
"-rw-r--r-- 1 root root 4253 Sep 28 2015 000003.jpg\n",
"-rw-r--r-- 1 root root 10747 Sep 28 2015 000004.jpg\n",
"-rw-r--r-- 1 root root 6351 Sep 28 2015 000005.jpg\n",
"-rw-r--r-- 1 root root 8073 Sep 28 2015 000006.jpg\n",
"-rw-r--r-- 1 root root 8203 Sep 28 2015 000007.jpg\n",
"-rw-r--r-- 1 root root 7725 Sep 28 2015 000008.jpg\n",
"-rw-r--r-- 1 root root 8641 Sep 28 2015 000009.jpg\n",
" 202599 202599 2228589\n"
]
}
],
"source": [
"! ls -l {DATA_DIR}/{DATA_SUBDIR} | head\n",
"! ls {DATA_DIR}/{DATA_SUBDIR} | wc"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JTblqnRqAvLW"
},
"source": [
"# Check the CelebA dataset\n",
"\n",
"## CelebA データセットを確認する"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 456,
"status": "ok",
"timestamp": 1637560481182,
"user": {
"displayName": "Yoshihisa Nitta",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
"userId": "15888006800030996813"
},
"user_tz": -540
},
"id": "DR3yVPDZAuRw",
"outputId": "5c89e461-8223-4b3a-83a7-150e68e006be"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"202599\n"
]
}
],
"source": [
"# paths to all the image files.\n",
"\n",
"import os\n",
"import glob\n",
"import numpy as np\n",
"\n",
"all_file_paths = np.array(glob.glob(os.path.join(DATA_DIR, DATA_SUBDIR, '*.jpg')))\n",
"n_all_images = len(all_file_paths)\n",
"\n",
"print(n_all_images)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"executionInfo": {
"elapsed": 3,
"status": "ok",
"timestamp": 1637560481182,
"user": {
"displayName": "Yoshihisa Nitta",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
"userId": "15888006800030996813"
},
"user_tz": -540
},
"id": "OmuBX5z_A1qG"
},
"outputs": [],
"source": [
"# slect some image files.\n",
"\n",
"n_to_show = 10\n",
"selected_indices = np.random.choice(range(n_all_images), n_to_show)\n",
"selected_paths = all_file_paths[selected_indices]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 107
},
"executionInfo": {
"elapsed": 992,
"status": "ok",
"timestamp": 1637560482171,
"user": {
"displayName": "Yoshihisa Nitta",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
"userId": "15888006800030996813"
},
"user_tz": -540
},
"id": "RABvNE7zA3nl",
"outputId": "e3694fcf-fe86-4c75-d7d2-8f348164747b"
},
"outputs": [
{
"data": {
"image/png": 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\n",
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