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Keras 2 : examples : 生成深層学習 – 変分オートエンコーダ

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Keras 2 : examples : 生成深層学習 – 変分オートエンコーダ (翻訳/解説)

翻訳 : (株)クラスキャット セールスインフォメーション
作成日時 : 06/28/2022 (keras 2.9.0)

* 本ページは、Keras の以下のドキュメントを翻訳した上で適宜、補足説明したものです：

* サンプルコードの動作確認はしておりますが、必要な場合には適宜、追加改変しています。
* ご自由にリンクを張って頂いてかまいませんが、sales-info@classcat.com までご一報いただけると嬉しいです。

 

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Keras 2 : examples : 生成深層学習 – 変分オートエンコーダ

Description : MNIST 数字で訓練された畳み込み変分オートエンコーダ (VAE)

 

セットアップ

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

 

サンプリング層の作成

class Sampling(layers.Layer):
    """Uses (z_mean, z_log_var) to sample z, the vector encoding a digit."""

    def call(self, inputs):
        z_mean, z_log_var = inputs
        batch = tf.shape(z_mean)[0]
        dim = tf.shape(z_mean)[1]
        epsilon = tf.keras.backend.random_normal(shape=(batch, dim))
        return z_mean + tf.exp(0.5 * z_log_var) * epsilon

 

エンコーダの構築

latent_dim = 2

encoder_inputs = keras.Input(shape=(28, 28, 1))
x = layers.Conv2D(32, 3, activation="relu", strides=2, padding="same")(encoder_inputs)
x = layers.Conv2D(64, 3, activation="relu", strides=2, padding="same")(x)
x = layers.Flatten()(x)
x = layers.Dense(16, activation="relu")(x)
z_mean = layers.Dense(latent_dim, name="z_mean")(x)
z_log_var = layers.Dense(latent_dim, name="z_log_var")(x)
z = Sampling()([z_mean, z_log_var])
encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name="encoder")
encoder.summary()

Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 28, 28, 1)]  0                                            
__________________________________________________________________________________________________
conv2d (Conv2D)                 (None, 14, 14, 32)   320         input_1[0][0]                    
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 7, 7, 64)     18496       conv2d[0][0]                     
__________________________________________________________________________________________________
flatten (Flatten)               (None, 3136)         0           conv2d_1[0][0]                   
__________________________________________________________________________________________________
dense (Dense)                   (None, 16)           50192       flatten[0][0]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 2)            34          dense[0][0]                      
__________________________________________________________________________________________________
z_log_var (Dense)               (None, 2)            34          dense[0][0]                      
__________________________________________________________________________________________________
sampling (Sampling)             (None, 2)            0           z_mean[0][0]                     
                                                                 z_log_var[0][0]                  
==================================================================================================
Total params: 69,076
Trainable params: 69,076
Non-trainable params: 0
________________________________________________________________________________________

 

デコーダの構築

latent_inputs = keras.Input(shape=(latent_dim,))
x = layers.Dense(7 * 7 * 64, activation="relu")(latent_inputs)
x = layers.Reshape((7, 7, 64))(x)
x = layers.Conv2DTranspose(64, 3, activation="relu", strides=2, padding="same")(x)
x = layers.Conv2DTranspose(32, 3, activation="relu", strides=2, padding="same")(x)
decoder_outputs = layers.Conv2DTranspose(1, 3, activation="sigmoid", padding="same")(x)
decoder = keras.Model(latent_inputs, decoder_outputs, name="decoder")
decoder.summary()

Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         [(None, 2)]               0         
_________________________________________________________________
dense_1 (Dense)              (None, 3136)              9408      
_________________________________________________________________
reshape (Reshape)            (None, 7, 7, 64)          0         
_________________________________________________________________
conv2d_transpose (Conv2DTran (None, 14, 14, 64)        36928     
_________________________________________________________________
conv2d_transpose_1 (Conv2DTr (None, 28, 28, 32)        18464     
_________________________________________________________________
conv2d_transpose_2 (Conv2DTr (None, 28, 28, 1)         289       
=================================================================
Total params: 65,089
Trainable params: 65,089
Non-trainable params: 0
_________________________________________________________________

 

VAE をカスタム train_step を持つモデルとして定義する

class VAE(keras.Model):
    def __init__(self, encoder, decoder, **kwargs):
        super(VAE, self).__init__(**kwargs)
        self.encoder = encoder
        self.decoder = decoder
        self.total_loss_tracker = keras.metrics.Mean(name="total_loss")
        self.reconstruction_loss_tracker = keras.metrics.Mean(
            name="reconstruction_loss"
        )
        self.kl_loss_tracker = keras.metrics.Mean(name="kl_loss")

    @property
    def metrics(self):
        return [
            self.total_loss_tracker,
            self.reconstruction_loss_tracker,
            self.kl_loss_tracker,
        ]

    def train_step(self, data):
        with tf.GradientTape() as tape:
            z_mean, z_log_var, z = self.encoder(data)
            reconstruction = self.decoder(z)
            reconstruction_loss = tf.reduce_mean(
                tf.reduce_sum(
                    keras.losses.binary_crossentropy(data, reconstruction), axis=(1, 2)
                )
            )
            kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))
            kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))
            total_loss = reconstruction_loss + kl_loss
        grads = tape.gradient(total_loss, self.trainable_weights)
        self.optimizer.apply_gradients(zip(grads, self.trainable_weights))
        self.total_loss_tracker.update_state(total_loss)
        self.reconstruction_loss_tracker.update_state(reconstruction_loss)
        self.kl_loss_tracker.update_state(kl_loss)
        return {
            "loss": self.total_loss_tracker.result(),
            "reconstruction_loss": self.reconstruction_loss_tracker.result(),
            "kl_loss": self.kl_loss_tracker.result(),
        }

 

VAE の訓練

(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
mnist_digits = np.concatenate([x_train, x_test], axis=0)
mnist_digits = np.expand_dims(mnist_digits, -1).astype("float32") / 255

vae = VAE(encoder, decoder)
vae.compile(optimizer=keras.optimizers.Adam())
vae.fit(mnist_digits, epochs=30, batch_size=128)

Epoch 1/30
547/547 [==============================] - 35s 62ms/step - loss: 255.8020 - reconstruction_loss: 208.5391 - kl_loss: 2.9673
Epoch 2/30
547/547 [==============================] - 38s 69ms/step - loss: 178.8786 - reconstruction_loss: 168.4294 - kl_loss: 5.4217
Epoch 3/30
547/547 [==============================] - 39s 72ms/step - loss: 166.0320 - reconstruction_loss: 158.7979 - kl_loss: 5.8015
Epoch 4/30
547/547 [==============================] - 38s 69ms/step - loss: 161.1647 - reconstruction_loss: 154.5963 - kl_loss: 5.9926
Epoch 5/30
547/547 [==============================] - 40s 72ms/step - loss: 152.0941 - reconstruction_loss: 145.7407 - kl_loss: 6.4654
Epoch 14/30
547/547 [==============================] - 38s 70ms/step - loss: 148.8709 - reconstruction_loss: 142.5713 - kl_loss: 6.6179
Epoch 27/30
191/547 [=========>....................] - ETA: 25s - loss: 149.0829 - reconstruction_loss: 142.2507 - kl_loss: 6.6429

 

サンプリングされた数字のグリッドを表示する

import matplotlib.pyplot as plt


def plot_latent_space(vae, n=30, figsize=15):
    # display a n*n 2D manifold of digits
    digit_size = 28
    scale = 1.0
    figure = np.zeros((digit_size * n, digit_size * n))
    # linearly spaced coordinates corresponding to the 2D plot
    # of digit classes in the latent space
    grid_x = np.linspace(-scale, scale, n)
    grid_y = np.linspace(-scale, scale, n)[::-1]

    for i, yi in enumerate(grid_y):
        for j, xi in enumerate(grid_x):
            z_sample = np.array([[xi, yi]])
            x_decoded = vae.decoder.predict(z_sample)
            digit = x_decoded[0].reshape(digit_size, digit_size)
            figure[
                i * digit_size : (i + 1) * digit_size,
                j * digit_size : (j + 1) * digit_size,
            ] = digit

    plt.figure(figsize=(figsize, figsize))
    start_range = digit_size // 2
    end_range = n * digit_size + start_range
    pixel_range = np.arange(start_range, end_range, digit_size)
    sample_range_x = np.round(grid_x, 1)
    sample_range_y = np.round(grid_y, 1)
    plt.xticks(pixel_range, sample_range_x)
    plt.yticks(pixel_range, sample_range_y)
    plt.xlabel("z[0]")
    plt.ylabel("z[1]")
    plt.imshow(figure, cmap="Greys_r")
    plt.show()


plot_latent_space(vae)

 

潜在空間が異なる数字クラスをどのようにクラスタリングするか表示する

def plot_label_clusters(vae, data, labels):
    # display a 2D plot of the digit classes in the latent space
    z_mean, _, _ = vae.encoder.predict(data)
    plt.figure(figsize=(12, 10))
    plt.scatter(z_mean[:, 0], z_mean[:, 1], c=labels)
    plt.colorbar()
    plt.xlabel("z[0]")
    plt.ylabel("z[1]")
    plt.show()


(x_train, y_train), _ = keras.datasets.mnist.load_data()
x_train = np.expand_dims(x_train, -1).astype("float32") / 255

plot_label_clusters(vae, x_train, y_train)

 

以上