{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lEMI1oxe8KNy"
   },
   "source": [
    "<i>Updated 21/Nov/2021</i> by Yoshihisa Nitta &nbsp; <img src=\"https://nw.tsuda.ac.jp/icons/nitta-email.gif\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X4_Pjeum8NUa"
   },
   "source": [
    "\n",
    "# Further Training 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_Train.ipynb.\n",
    "\n",
    "## CelebA データセットに対して Variational Auto Encoder をGoogle Colab 上の Tensorflow 2 で追加学習する\n",
    "\n",
    "CelebA データセットに対して変分オートエンコーダをさらに学習させる。\n",
    "VAE_CelebA_Train.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": 270,
     "status": "ok",
     "timestamp": 1637505886000,
     "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": 2260,
     "status": "ok",
     "timestamp": 1637505895914,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "woOXJdh57sIx",
    "outputId": "7d22b67d-2269-4366-b102-b2e16ed4a396"
   },
   "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": 659,
     "status": "ok",
     "timestamp": 1637505915895,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "4xRE6QCs9QO1",
    "outputId": "5ece69df-eb07-4802-eca0-0de3b00b986a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sun Nov 21 14:45:15 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   33C    P0    26W / 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: 79\n",
      "model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n",
      "stepping\t: 0\n",
      "microcode\t: 0x1\n",
      "cpu MHz\t\t: 2199.998\n",
      "cache size\t: 56320 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 rdseed adx smap xsaveopt 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: 4399.99\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: 79\n",
      "model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n",
      "stepping\t: 0\n",
      "microcode\t: 0x1\n",
      "cpu MHz\t\t: 2199.998\n",
      "cache size\t: 56320 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 rdseed adx smap xsaveopt 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: 4399.99\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        733M          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": 24864,
     "status": "ok",
     "timestamp": 1637505975236,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "9B4MX6GC9Vf9",
    "outputId": "bcf572f3-bb59-4a1f-f51c-bb315fd77731"
   },
   "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": 6,
     "status": "ok",
     "timestamp": 1637505975237,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "P4voAIIh9aiO",
    "outputId": "a7621009-b46a-4e31-990b-daf067d60055"
   },
   "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",
    "Basically, <code>gdown</code> from Google Drive.\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",
    "基本的に Google Drive から <code>gdown</code> してください。\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、nw.tsuda.ac.jp からダウンロードしてください。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2707,
     "status": "ok",
     "timestamp": 1637506093686,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "K13qk7Td9mH_",
    "outputId": "c24b8044-e953-48a7-8133-630c28eb58d7"
   },
   "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, 16.3MB/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": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 244,
     "status": "ok",
     "timestamp": 1637506102894,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "WmOyk35j-AZ7",
    "outputId": "c3dd811f-3888-4b94-da39-98454e895051"
   },
   "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",
    "<a href=\"https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html\">\n",
    "https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html\n",
    "</a>\n",
    "\n",
    "Google Drive of CelebA dataset:\n",
    "<a href=\"https://drive.google.com/drive/folders/0B7EVK8r0v71pWEZsZE9oNnFzTm8?resourcekey=0-5BR16BdXnb8hVj6CNHKzLg\">\n",
    "https://drive.google.com/drive/folders/0B7EVK8r0v71pWEZsZE9oNnFzTm8?resourcekey=0-5BR16BdXnb8hVj6CNHKzLg\n",
    "</a>\n",
    "\n",
    "img_align_celeba.zip mirrored on my Google Drive:    \n",
    "<a href=\"https://drive.google.com/uc?id=1LFKeoI-hb96jlV0K10dO1o04iQPBoFdx\">\n",
    "https://drive.google.com/uc?id=1LFKeoI-hb96jlV0K10dO1o04iQPBoFdx\n",
    "</a>\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",
    "<a href=\"https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html\">\n",
    "https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html\n",
    "</a>\n",
    "\n",
    "CelebA データセットのGoogle Drive:\n",
    "<a href=\"https://drive.google.com/drive/folders/0B7EVK8r0v71pWEZsZE9oNnFzTm8?resourcekey=0-5BR16BdXnb8hVj6CNHKzLg\">\n",
    "https://drive.google.com/drive/folders/0B7EVK8r0v71pWEZsZE9oNnFzTm8?resourcekey=0-5BR16BdXnb8hVj6CNHKzLg\n",
    "</a>\n",
    "\n",
    "自分の Google Drive 上にミラーした img_align_celeba.zip:    \n",
    "<a href=\"https://drive.google.com/uc?id=1LFKeoI-hb96jlV0K10dO1o04iQPBoFdx\">\n",
    "https://drive.google.com/uc?id=1LFKeoI-hb96jlV0K10dO1o04iQPBoFdx\n",
    "</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20130,
     "status": "ok",
     "timestamp": 1637506176166,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "g99ZWERz-DP8",
    "outputId": "62a1e72f-154f-4c4b-f00b-275f79503df9"
   },
   "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 [00:06<00:00, 238MB/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": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1637506176167,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "xXFiRu9y-QSj",
    "outputId": "65bdaf54-4f9e-40a0-9124-56c6bf8c25f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 1409676\n",
      "drwx------ 6 root root       4096 Nov 21 14:46 drive\n",
      "-rw-r--r-- 1 root root 1443490838 Nov 21 14:49 img_align_celeba.zip\n",
      "drwxr-xr-x 2 root root       4096 Nov 21 14:48 nw\n",
      "drwxr-xr-x 1 root root       4096 Nov 18 14:36 sample_data\n"
     ]
    }
   ],
   "source": [
    "! ls -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "executionInfo": {
     "elapsed": 260,
     "status": "ok",
     "timestamp": 1637506709488,
     "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": 14,
   "metadata": {
    "executionInfo": {
     "elapsed": 18168,
     "status": "ok",
     "timestamp": 1637506735151,
     "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": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1999,
     "status": "ok",
     "timestamp": 1637506737138,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "fDN_8kaFAZPV",
    "outputId": "09c85014-f7cf-4a4b-80a9-5da2d03f1e31"
   },
   "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": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 809,
     "status": "ok",
     "timestamp": 1637506845329,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "DR3yVPDZAuRw",
    "outputId": "4680c5f5-0eb3-4e08-b512-490a9bcf2663"
   },
   "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": 19,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1637506845329,
     "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": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 107
    },
    "executionInfo": {
     "elapsed": 721,
     "status": "ok",
     "timestamp": 1637506846299,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "RABvNE7zA3nl",
    "outputId": "63e14d31-9936-4e6c-827a-1297abfc97a7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x100.8 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display some images.\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(1, n_to_show, figsize=(1.4 * n_to_show, 1.4))\n",
    "for i, path in enumerate(selected_paths):\n",
    "    img = tf.keras.preprocessing.image.load_img(path)\n",
    "    ax[i].imshow(img)\n",
    "    ax[i].axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Guuvk7_jAgVX"
   },
   "source": [
    "# Separate image files for train and test\n",
    "\n",
    "## 画像ファイルを学習用とテスト用に分割する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "executionInfo": {
     "elapsed": 237,
     "status": "ok",
     "timestamp": 1637506755563,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "xrBIRblGAa2N"
   },
   "outputs": [],
   "source": [
    "TRAIN_DATA_DIR = 'train_data'\n",
    "TEST_DATA_DIR = 'test_data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "executionInfo": {
     "elapsed": 4537,
     "status": "ok",
     "timestamp": 1637506856342,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "lK0VJZHpAimc"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "split = 0.05\n",
    "\n",
    "indices = np.arange(n_all_images)\n",
    "np.random.shuffle(indices)\n",
    "train_indices = indices[: -int(n_all_images * split)]\n",
    "test_indices = indices[-int(n_all_images * split):]\n",
    "\n",
    "! rm -rf {TRAIN_DATA_DIR} {TEST_DATA_DIR}\n",
    "\n",
    "dst=f'{TRAIN_DATA_DIR}/celeba'\n",
    "if not os.path.exists(dst):\n",
    "    os.makedirs(dst)\n",
    "for idx in train_indices:\n",
    "    path = all_file_paths[idx]\n",
    "    dpath, fname = os.path.split(path)\n",
    "    os.symlink(f'../../{path}', f'{dst}/{fname}')\n",
    "\n",
    "dst=f'{TEST_DATA_DIR}/celeba'\n",
    "if not os.path.exists(dst):\n",
    "    os.makedirs(dst)\n",
    "for idx in test_indices:\n",
    "    path = all_file_paths[idx]\n",
    "    dpath, fname = os.path.split(path)\n",
    "    os.symlink(f'../../{path}', f'{dst}/{fname}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D__ZFCMiBAy-"
   },
   "source": [
    "# Prepare ImageDataGenerator\n",
    "\n",
    "<code>flow_from_directory()</code> requires to specify the parent directory of the directory where the image files are located.\n",
    "\n",
    "## ImageDataGenerator を用意する\n",
    "\n",
    "<code>flow_from_directory()</code> では image files があるディレクトリの親ディレクトリを指定する必要がある。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "executionInfo": {
     "elapsed": 225,
     "status": "ok",
     "timestamp": 1637506889613,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "fGbSF4wqAkPm"
   },
   "outputs": [],
   "source": [
    "INPUT_DIM = (128, 128, 3)\n",
    "BATCH_SIZE = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6072,
     "status": "ok",
     "timestamp": 1637506902401,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "ShjR9wXyBDTJ",
    "outputId": "f26d4691-7552-41e9-c40c-37bbc96c120c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 192470 images belonging to 1 classes.\n",
      "Found 10129 images belonging to 1 classes.\n"
     ]
    }
   ],
   "source": [
    "data_gen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
    "    rescale = 1.0/255\n",
    "    )\n",
    "\n",
    "data_flow = data_gen.flow_from_directory(\n",
    "    TRAIN_DATA_DIR,\n",
    "    target_size = INPUT_DIM[:2],\n",
    "    batch_size = BATCH_SIZE,\n",
    "    shuffle=True,\n",
    "    class_mode = 'input'\n",
    "    )\n",
    "\n",
    "val_data_flow = data_gen.flow_from_directory(\n",
    "    TEST_DATA_DIR,\n",
    "    target_size = INPUT_DIM[:2],\n",
    "    batch_size = BATCH_SIZE,\n",
    "    shuffle=True,\n",
    "    class_mode = 'input'\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 240,
     "status": "ok",
     "timestamp": 1637506903980,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "QteUZEgwBFBc",
    "outputId": "571ac8ae-1bee-46ef-eca1-50211be76942"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6015\n",
      "317\n"
     ]
    }
   ],
   "source": [
    "print(len(data_flow))\n",
    "print(len(val_data_flow))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tpXkMEYgBLVO"
   },
   "source": [
    "# Load the Neural Network Model trained before\n",
    "\n",
    "Load the model trained by the '(3) Training' method of VAE_CelebA_Train.ipynb.\n",
    "\n",
    "## 学習済みのニューラルネットワーク・モデルをロードする\n",
    "\n",
    "VAE_CelebA_Train.ipynb の 「(3) 学習」方法で学習したモデルをロードする。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "executionInfo": {
     "elapsed": 249,
     "status": "ok",
     "timestamp": 1637507088812,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "71S4E4RVBG1g"
   },
   "outputs": [],
   "source": [
    "save_path3 = '/content/drive/MyDrive/ColabRun/VAE_CelebA03/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 610,
     "status": "ok",
     "timestamp": 1637508174186,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "ol7yi-iQBfAL",
    "outputId": "d696a566-26a1-42b6-f3c2-040784fe2d18"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "# Load the parameters and model weights saved before\n",
    "# 保存したパラメータと重みを読み込む\n",
    "\n",
    "from nw.VariationalAutoEncoder import VariationalAutoEncoder\n",
    "\n",
    "vae3 = VariationalAutoEncoder.load(save_path3)\n",
    "print(vae3.epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eSMGbodOC33H"
   },
   "source": [
    "# Load the saved loss transition of training before\n",
    "\n",
    "## 以前の学習で保存した loss の遷移をロードする"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "executionInfo": {
     "elapsed": 290,
     "status": "ok",
     "timestamp": 1637508179976,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "mUO_yPxJB7Li"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "\n",
    "var_path = f'{save_path3}/loss_{vae3.epoch-1}.pkl'\n",
    "\n",
    "with open(var_path, 'rb') as f:\n",
    "    loss3_1, rloss3_1, kloss3_1, val_loss3_1, val_rloss3_1, val_kloss3_1 = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 252,
     "status": "ok",
     "timestamp": 1637508181735,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "dvNcktJrCi5Q",
    "outputId": "1bcbe933-3204-412d-d68e-f5d138f24eb7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "print(len(loss3_1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ijoIIFGwDSxd"
   },
   "source": [
    "# Train in addition\n",
    "## 追加で学習する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "executionInfo": {
     "elapsed": 239,
     "status": "ok",
     "timestamp": 1637508184714,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "0xP_xn2UDePo"
   },
   "outputs": [],
   "source": [
    "LEARNING_RATE = 0.0005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
     "timestamp": 1637508185486,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "bQf7O4WWCkGp"
   },
   "outputs": [],
   "source": [
    "# initial_learning_rate * decay_rate ^ (step // decay_steps)\n",
    "\n",
    "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
    "    initial_learning_rate = LEARNING_RATE,\n",
    "    decay_steps = len(data_flow),\n",
    "    decay_rate=0.96\n",
    ")\n",
    "\n",
    "optimizer3 = tf.keras.optimizers.Adam(learning_rate=lr_schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 51990616,
     "status": "ok",
     "timestamp": 1637560184395,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "9KvsxDuHDix1",
    "outputId": "f82c2e05-75c0-4aec-f677-bfb3bca7c5b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/200 6015 loss: total 200.568 reconstruction 139.167 kl 61.402 val loss total 199.716 reconstruction 138.757 kl 60.959 0:04:32.413829\n",
      "6/200 6015 loss: total 199.201 reconstruction 137.740 kl 61.461 val loss total 198.803 reconstruction 135.566 kl 63.236 0:09:00.840572\n",
      "7/200 6015 loss: total 198.084 reconstruction 136.619 kl 61.464 val loss total 197.773 reconstruction 138.099 kl 59.674 0:13:28.770028\n",
      "8/200 6015 loss: total 197.448 reconstruction 135.976 kl 61.471 val loss total 197.090 reconstruction 135.068 kl 62.023 0:17:56.283880\n",
      "9/200 6015 loss: total 196.674 reconstruction 135.245 kl 61.429 val loss total 196.911 reconstruction 136.293 kl 60.618 0:22:23.191827\n",
      "10/200 6015 loss: total 196.214 reconstruction 134.815 kl 61.398 val loss total 195.840 reconstruction 134.457 kl 61.383 0:26:52.411447\n",
      "11/200 6015 loss: total 195.389 reconstruction 134.051 kl 61.338 val loss total 195.385 reconstruction 134.659 kl 60.726 0:31:21.585673\n",
      "12/200 6015 loss: total 195.514 reconstruction 134.116 kl 61.398 val loss total 195.432 reconstruction 133.572 kl 61.859 0:35:50.644285\n",
      "13/200 6015 loss: total 194.981 reconstruction 133.609 kl 61.371 val loss total 194.876 reconstruction 134.686 kl 60.190 0:40:18.560140\n",
      "14/200 6015 loss: total 194.501 reconstruction 133.142 kl 61.360 val loss total 194.673 reconstruction 133.745 kl 60.929 0:44:45.301768\n",
      "15/200 6015 loss: total 194.389 reconstruction 132.984 kl 61.405 val loss total 194.404 reconstruction 132.935 kl 61.469 0:49:11.734210\n",
      "16/200 6015 loss: total 194.060 reconstruction 132.721 kl 61.340 val loss total 194.477 reconstruction 132.656 kl 61.821 0:53:38.312727\n",
      "17/200 6015 loss: total 193.802 reconstruction 132.420 kl 61.381 val loss total 194.061 reconstruction 132.766 kl 61.296 0:58:04.671105\n",
      "18/200 6015 loss: total 193.661 reconstruction 132.288 kl 61.373 val loss total 193.420 reconstruction 132.419 kl 61.000 1:02:28.818799\n",
      "19/200 6015 loss: total 193.363 reconstruction 132.005 kl 61.358 val loss total 193.682 reconstruction 131.354 kl 62.328 1:06:54.455887\n",
      "20/200 6015 loss: total 193.203 reconstruction 131.853 kl 61.350 val loss total 193.382 reconstruction 132.363 kl 61.019 1:11:20.673511\n",
      "21/200 6015 loss: total 193.032 reconstruction 131.673 kl 61.360 val loss total 193.215 reconstruction 132.211 kl 61.004 1:15:47.300723\n",
      "22/200 6015 loss: total 192.995 reconstruction 131.634 kl 61.361 val loss total 193.328 reconstruction 131.712 kl 61.617 1:20:13.540286\n",
      "23/200 6015 loss: total 192.744 reconstruction 131.357 kl 61.387 val loss total 192.852 reconstruction 131.338 kl 61.514 1:24:39.321436\n",
      "24/200 6015 loss: total 192.598 reconstruction 131.199 kl 61.399 val loss total 192.727 reconstruction 131.283 kl 61.444 1:29:06.260637\n",
      "25/200 6015 loss: total 192.436 reconstruction 131.113 kl 61.323 val loss total 192.670 reconstruction 131.508 kl 61.162 1:33:34.268558\n",
      "26/200 6015 loss: total 192.299 reconstruction 130.964 kl 61.335 val loss total 192.654 reconstruction 131.610 kl 61.044 1:38:02.271351\n",
      "27/200 6015 loss: total 192.279 reconstruction 130.894 kl 61.385 val loss total 192.524 reconstruction 131.013 kl 61.511 1:42:29.732575\n",
      "28/200 6015 loss: total 192.090 reconstruction 130.729 kl 61.361 val loss total 192.298 reconstruction 131.387 kl 60.911 1:46:56.482835\n",
      "29/200 6015 loss: total 191.990 reconstruction 130.627 kl 61.363 val loss total 192.038 reconstruction 130.568 kl 61.470 1:51:22.718647\n",
      "30/200 6015 loss: total 191.945 reconstruction 130.586 kl 61.359 val loss total 191.994 reconstruction 130.083 kl 61.911 1:55:49.025294\n",
      "31/200 6015 loss: total 191.772 reconstruction 130.404 kl 61.368 val loss total 192.161 reconstruction 131.505 kl 60.656 2:00:16.566865\n",
      "32/200 6015 loss: total 191.608 reconstruction 130.271 kl 61.337 val loss total 191.926 reconstruction 130.664 kl 61.262 2:04:44.523716\n",
      "33/200 6015 loss: total 191.654 reconstruction 130.267 kl 61.387 val loss total 191.847 reconstruction 129.996 kl 61.851 2:09:12.634162\n",
      "34/200 6015 loss: total 191.527 reconstruction 130.181 kl 61.346 val loss total 191.904 reconstruction 131.380 kl 60.524 2:13:38.871422\n",
      "35/200 6015 loss: total 191.435 reconstruction 130.085 kl 61.350 val loss total 191.677 reconstruction 130.091 kl 61.586 2:18:05.688359\n",
      "36/200 6015 loss: total 191.301 reconstruction 129.911 kl 61.390 val loss total 191.597 reconstruction 129.989 kl 61.608 2:22:30.800486\n",
      "37/200 6015 loss: total 191.411 reconstruction 130.028 kl 61.384 val loss total 191.686 reconstruction 130.779 kl 60.907 2:26:55.185918\n",
      "38/200 6015 loss: total 191.238 reconstruction 129.885 kl 61.353 val loss total 191.571 reconstruction 130.240 kl 61.330 2:31:23.018640\n",
      "39/200 6015 loss: total 191.177 reconstruction 129.811 kl 61.366 val loss total 191.513 reconstruction 130.113 kl 61.400 2:35:51.144367\n",
      "40/200 6015 loss: total 191.087 reconstruction 129.738 kl 61.349 val loss total 191.582 reconstruction 129.551 kl 62.031 2:40:18.046345\n",
      "41/200 6015 loss: total 191.020 reconstruction 129.638 kl 61.382 val loss total 191.349 reconstruction 130.358 kl 60.991 2:44:45.438834\n",
      "42/200 6015 loss: total 191.090 reconstruction 129.730 kl 61.360 val loss total 191.228 reconstruction 130.266 kl 60.962 2:49:13.116803\n",
      "43/200 6015 loss: total 190.814 reconstruction 129.448 kl 61.366 val loss total 191.296 reconstruction 129.657 kl 61.639 2:53:40.482109\n",
      "44/200 6015 loss: total 190.916 reconstruction 129.537 kl 61.378 val loss total 191.235 reconstruction 130.130 kl 61.105 2:58:07.187151\n",
      "45/200 6015 loss: total 190.848 reconstruction 129.470 kl 61.378 val loss total 191.184 reconstruction 130.094 kl 61.089 3:02:33.587573\n",
      "46/200 6015 loss: total 190.755 reconstruction 129.387 kl 61.369 val loss total 191.292 reconstruction 129.986 kl 61.306 3:07:00.933575\n",
      "47/200 6015 loss: total 190.729 reconstruction 129.384 kl 61.345 val loss total 191.018 reconstruction 129.780 kl 61.238 3:11:28.767333\n",
      "48/200 6015 loss: total 190.746 reconstruction 129.366 kl 61.379 val loss total 191.128 reconstruction 129.486 kl 61.642 3:15:56.930912\n",
      "49/200 6015 loss: total 190.665 reconstruction 129.285 kl 61.380 val loss total 191.122 reconstruction 129.899 kl 61.223 3:20:25.891575\n",
      "50/200 6015 loss: total 190.568 reconstruction 129.216 kl 61.353 val loss total 191.062 reconstruction 129.415 kl 61.647 3:24:54.822287\n",
      "51/200 6015 loss: total 190.507 reconstruction 129.187 kl 61.320 val loss total 191.045 reconstruction 129.658 kl 61.387 3:29:20.952387\n",
      "52/200 6015 loss: total 190.579 reconstruction 129.189 kl 61.390 val loss total 190.926 reconstruction 129.437 kl 61.489 3:33:49.332335\n",
      "53/200 6015 loss: total 190.384 reconstruction 129.077 kl 61.307 val loss total 191.009 reconstruction 130.127 kl 60.882 3:38:17.467712\n",
      "54/200 6015 loss: total 190.604 reconstruction 129.210 kl 61.394 val loss total 190.829 reconstruction 129.964 kl 60.864 3:42:44.749036\n",
      "55/200 6015 loss: total 190.344 reconstruction 128.994 kl 61.350 val loss total 190.877 reconstruction 129.566 kl 61.311 3:47:11.161280\n",
      "56/200 6015 loss: total 190.405 reconstruction 129.000 kl 61.405 val loss total 190.767 reconstruction 129.130 kl 61.637 3:51:35.811515\n",
      "57/200 6015 loss: total 190.465 reconstruction 129.067 kl 61.398 val loss total 190.881 reconstruction 129.565 kl 61.316 3:55:59.106461\n",
      "58/200 6015 loss: total 190.271 reconstruction 128.893 kl 61.377 val loss total 190.640 reconstruction 129.392 kl 61.248 4:00:24.005876\n",
      "59/200 6015 loss: total 190.287 reconstruction 128.895 kl 61.391 val loss total 190.646 reconstruction 129.648 kl 60.998 4:04:53.825522\n",
      "60/200 6015 loss: total 190.259 reconstruction 128.923 kl 61.336 val loss total 190.699 reconstruction 129.426 kl 61.273 4:09:19.352547\n",
      "61/200 6015 loss: total 190.391 reconstruction 128.953 kl 61.437 val loss total 190.661 reconstruction 129.518 kl 61.143 4:13:42.718273\n",
      "62/200 6015 loss: total 190.085 reconstruction 128.754 kl 61.332 val loss total 190.731 reconstruction 129.224 kl 61.508 4:18:06.786141\n",
      "63/200 6015 loss: total 190.284 reconstruction 128.896 kl 61.388 val loss total 190.652 reconstruction 129.701 kl 60.951 4:22:31.262709\n",
      "64/200 6015 loss: total 190.181 reconstruction 128.807 kl 61.374 val loss total 190.714 reconstruction 129.725 kl 60.989 4:26:55.542714\n",
      "65/200 6015 loss: total 190.247 reconstruction 128.828 kl 61.419 val loss total 190.565 reconstruction 129.282 kl 61.282 4:31:19.501985\n",
      "66/200 6015 loss: total 189.989 reconstruction 128.650 kl 61.339 val loss total 190.573 reconstruction 129.545 kl 61.028 4:35:43.661270\n",
      "67/200 6015 loss: total 190.212 reconstruction 128.829 kl 61.383 val loss total 190.437 reconstruction 129.229 kl 61.208 4:40:07.482412\n",
      "68/200 6015 loss: total 190.242 reconstruction 128.828 kl 61.414 val loss total 190.537 reconstruction 129.258 kl 61.280 4:44:31.648131\n",
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     ]
    }
   ],
   "source": [
    "log3_2 = vae3.train_tf_generator(\n",
    "    data_flow,\n",
    "    epochs = 200,\n",
    "    run_folder = save_path3,\n",
    "    optimizer = optimizer3,\n",
    "    save_epoch_interval = 50,\n",
    "    validation_data_flow = val_data_flow\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1637560188599,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "4Gl6WsYpD8oP"
   },
   "outputs": [],
   "source": [
    "loss3_2 = log3_2['loss']\n",
    "rloss3_2 = log3_2['reconstruction_loss']\n",
    "kloss3_2 = log3_2['kl_loss']\n",
    "val_loss3_2 = log3_2['val_loss']\n",
    "val_rloss3_2 = log3_2['val_reconstruction_loss']\n",
    "val_kloss3_2 = log3_2['val_kl_loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1637560188600,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "m7N2smplELQe"
   },
   "outputs": [],
   "source": [
    "loss3 = np.concatenate([loss3_1, loss3_2], axis=0)\n",
    "rloss3 = np.concatenate([rloss3_1, rloss3_2], axis=0)\n",
    "kloss3 = np.concatenate([kloss3_1, kloss3_2], axis=0)\n",
    "\n",
    "val_loss3 = np.concatenate([val_loss3_1, val_loss3_2], axis=0)\n",
    "val_rloss3 = np.concatenate([val_rloss3_1, val_rloss3_2], axis=0)\n",
    "val_kloss3 = np.concatenate([val_kloss3_1, val_kloss3_2], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1637560188600,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "9FpcIe3NEthY",
    "outputId": "dbaca7b7-919b-4055-fed1-5bce9a9876c0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "VariationalAutoEncoder.plot_history(\n",
    "    [loss3, val_loss3], \n",
    "    ['total_loss', 'val_total_loss']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1637560188601,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "Hdx3ECTAEvji",
    "outputId": "46df245e-83e3-41ea-ca77-ab86f38f3ffa"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "VariationalAutoEncoder.plot_history(\n",
    "    [rloss3, val_rloss3], \n",
    "    ['reconstruction_loss', 'val_reconstruction_loss']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "executionInfo": {
     "elapsed": 632,
     "status": "ok",
     "timestamp": 1637560189225,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "xbQwAdw5ExbR",
    "outputId": "54baa158-b456-4105-d262-60621cd252ec"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "VariationalAutoEncoder.plot_history(\n",
    "    [kloss3, val_kloss3], \n",
    "    ['kl_loss', 'val_kl_loss']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1637560189226,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "1Eka4YJ-FFL_"
   },
   "outputs": [],
   "source": [
    "x_, _ = next(val_data_flow)\n",
    "selected_images = x_[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "executionInfo": {
     "elapsed": 237,
     "status": "ok",
     "timestamp": 1637560215788,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "oAwU9lVhEz3B"
   },
   "outputs": [],
   "source": [
    "z_mean3, z_log_var3, z3 = vae3.encoder(selected_images)\n",
    "reconst_images3 = vae3.decoder(z3).numpy()  # decoder() returns Tensor for @tf.function declaration. Convert the Tensor to numpy array.\n",
    "txts3 = [f'{p[0]:.3f}, {p[1]:.3f}' for p in z3 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 201
    },
    "executionInfo": {
     "elapsed": 1137,
     "status": "ok",
     "timestamp": 1637560382935,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "Ev_KyOSuFG2R",
    "outputId": "61c6e59a-5f25-45b3-9503-7ba5ae421710"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x252 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "VariationalAutoEncoder.showImages(selected_images, reconst_images3, txts3, 1.4, 1.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "opkOsRvNFTPX"
   },
   "source": [
    "# Save the loss transition for future training.\n",
    "\n",
    "Save the loss transition to the file 'loss_<i>N</i>.pkl'.\n",
    "\n",
    "# 将来の学習のために、loss の変遷をセーブしておく\n",
    "\n",
    "「(3) 学習」のlossの変遷を 'loss_<i>N</i>.pkl' ファイルにセーブしておく。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "executionInfo": {
     "elapsed": 236,
     "status": "ok",
     "timestamp": 1637560391088,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "1T48DGlmFJLZ"
   },
   "outputs": [],
   "source": [
    "# Save loss variables for future training\n",
    "# 将来の学習のために loss 変数をセーブしておく\n",
    "import os\n",
    "import pickle\n",
    "\n",
    "var_path = f'{save_path3}/loss_{vae3.epoch-1}.pkl'\n",
    "\n",
    "dpath, fname = os.path.split(var_path)\n",
    "if dpath != '' and not os.path.exists(dpath):\n",
    "    os.makedirs(dpath)\n",
    "\n",
    "with open(var_path, 'wb') as f:\n",
    "    pickle.dump([\n",
    "        loss3, \n",
    "        rloss3, \n",
    "        kloss3, \n",
    "        val_loss3, \n",
    "        val_rloss3, \n",
    "        val_kloss3         \n",
    "    ], f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "r1XH-mtmGRyn"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "name": "VAE_CelebA_Train2.ipynb",
   "provenance": []
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  "kernelspec": {
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