
from tensorflow import keras
keras.__version__

'2.4.0'


import tensorflow as tf

tf.compat.v1.disable_v2_behavior()

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term


from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import backend as K
from tensorflow.keras.models import Model
import numpy as np

img_shape = (28, 28, 1)
batch_size = 16
latent_dim = 2  # 잠재 공간의 차원: 2D 평면

input_img = keras.Input(shape=img_shape)

x = layers.Conv2D(32, 3,
                  padding='same', activation='relu')(input_img)
x = layers.Conv2D(64, 3,
                  padding='same', activation='relu',
                  strides=(2, 2))(x)
x = layers.Conv2D(64, 3,
                  padding='same', activation='relu')(x)
x = layers.Conv2D(64, 3,
                  padding='same', activation='relu')(x)
shape_before_flattening = K.int_shape(x)

x = layers.Flatten()(x)
x = layers.Dense(32, activation='relu')(x)

z_mean = layers.Dense(latent_dim)(x)
z_log_var = layers.Dense(latent_dim)(x)


def sampling(args):
    z_mean, z_log_var = args
    epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim),
                              mean=0., stddev=1.)
    return z_mean + K.exp(z_log_var) * epsilon

z = layers.Lambda(sampling)([z_mean, z_log_var])


# Input에 z를 주입합니다
decoder_input = layers.Input(K.int_shape(z)[1:])

# 입력을 업샘플링합니다
x = layers.Dense(np.prod(shape_before_flattening[1:]),
                 activation='relu')(decoder_input)

# 인코더 모델의 마지막 Flatten 층 직전의 특성 맵과 같은 크기를 가진 특성 맵으로 z의 크기를 바꿉니다
x = layers.Reshape(shape_before_flattening[1:])(x)

# Conv2DTranspose 층과 Conv2D 층을 사용해 z를 원본 입력 이미지와 같은 크기의 특성 맵으로 디코딩합니다
x = layers.Conv2DTranspose(32, 3,
                           padding='same', activation='relu',
                           strides=(2, 2))(x)
x = layers.Conv2D(1, 3,
                  padding='same', activation='sigmoid')(x)
# 특성 맵의 크기가 원본 입력과 같아집니다

# 디코더 모델 객체를 만듭니다
decoder = Model(decoder_input, x)

# 모델에 z를 주입하면 디코딩된 z를 출력합니다
z_decoded = decoder(z)


class CustomVariationalLayer(keras.layers.Layer):

    def vae_loss(self, x, z_decoded):
        x = K.flatten(x)
        z_decoded = K.flatten(z_decoded)
        xent_loss = keras.metrics.binary_crossentropy(x, z_decoded)
        kl_loss = -5e-4 * K.mean(
            1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
        return K.mean(xent_loss + kl_loss)

    def call(self, inputs):
        x = inputs[0]
        z_decoded = inputs[1]
        loss = self.vae_loss(x, z_decoded)
        self.add_loss(loss, inputs=inputs)
        # 출력 값을 사용하지 않습니다
        return x

# 입력과 디코딩된 출력으로 이 층을 호출하여 모델의 최종 출력을 얻습니다
y = CustomVariationalLayer()([input_img, z_decoded])


from tensorflow.keras.datasets import mnist

vae = Model(input_img, y)
vae.compile(optimizer='rmsprop', loss=None)
vae.summary()

# MNIST 숫자 이미지에서 VAE를 훈련합니다
(x_train, _), (x_test, y_test) = mnist.load_data()

x_train = x_train.astype('float32') / 255.
x_train = x_train.reshape(x_train.shape + (1,))
x_test = x_test.astype('float32') / 255.
x_test = x_test.reshape(x_test.shape + (1,))

vae.fit(x=x_train, y=None,
        shuffle=True,
        epochs=10,
        batch_size=batch_size,
        validation_data=(x_test, None))

WARNING:tensorflow:Output custom_variational_layer missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to custom_variational_layer.
Model: "functional_3"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 28, 28, 1)]  0                                            
__________________________________________________________________________________________________
conv2d (Conv2D)                 (None, 28, 28, 32)   320         input_1[0][0]                    
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 14, 14, 64)   18496       conv2d[0][0]                     
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 14, 14, 64)   36928       conv2d_1[0][0]                   
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 14, 14, 64)   36928       conv2d_2[0][0]                   
__________________________________________________________________________________________________
flatten (Flatten)               (None, 12544)        0           conv2d_3[0][0]                   
__________________________________________________________________________________________________
dense (Dense)                   (None, 32)           401440      flatten[0][0]                    
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 2)            66          dense[0][0]                      
__________________________________________________________________________________________________
dense_2 (Dense)                 (None, 2)            66          dense[0][0]                      
__________________________________________________________________________________________________
lambda (Lambda)                 (None, 2)            0           dense_1[0][0]                    
                                                                 dense_2[0][0]                    
__________________________________________________________________________________________________
functional_1 (Functional)       (None, 28, 28, 1)    56385       lambda[0][0]                     
__________________________________________________________________________________________________
custom_variational_layer (Custo (None, 28, 28, 1)    0           input_1[0][0]                    
                                                                 functional_1[0][0]               
==================================================================================================
Total params: 550,629
Trainable params: 550,629
Non-trainable params: 0
__________________________________________________________________________________________________
Train on 60000 samples, validate on 10000 samples
Epoch 1/10
WARNING:tensorflow:OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs.
59984/60000 [============================>.] - ETA: 0s - loss: 0.6673WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_v1.py:2048: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0, as updates are applied automatically.
60000/60000 [==============================] - 43s 713us/sample - loss: 0.6672 - val_loss: 0.1994
Epoch 2/10
60000/60000 [==============================] - 43s 709us/sample - loss: 0.1950 - val_loss: 0.1913
Epoch 3/10
60000/60000 [==============================] - 43s 714us/sample - loss: 0.1899 - val_loss: 0.1911
Epoch 4/10
60000/60000 [==============================] - 43s 716us/sample - loss: 0.1872 - val_loss: 0.1880
Epoch 5/10
60000/60000 [==============================] - 42s 708us/sample - loss: 0.1854 - val_loss: 0.1846
Epoch 6/10
60000/60000 [==============================] - 43s 714us/sample - loss: 0.1840 - val_loss: 0.1840
Epoch 7/10
60000/60000 [==============================] - 42s 705us/sample - loss: 0.1829 - val_loss: 0.1835
Epoch 8/10
60000/60000 [==============================] - 43s 713us/sample - loss: 0.1821 - val_loss: 0.1820
Epoch 9/10
60000/60000 [==============================] - 43s 715us/sample - loss: 0.1813 - val_loss: 0.1825
Epoch 10/10
60000/60000 [==============================] - 43s 710us/sample - loss: 0.1807 - val_loss: 0.1809

<tensorflow.python.keras.callbacks.History at 0x7ff2e2d3df28>


import matplotlib.pyplot as plt


from scipy.stats import norm

# Display a 2D manifold of the digits
n = 15  # 15 × 15 숫자의 그리드를 출력합니다
digit_size = 28
figure = np.zeros((digit_size * n, digit_size * n))
# 싸이파이 ppf 함수를 사용하여 일정하게 떨어진 간격마다 잠재 변수 z의 값을 만듭니다
# 잠재 공간의 사전 확률은 가우시안입니다
grid_x = norm.ppf(np.linspace(0.05, 0.95, n))
grid_y = norm.ppf(np.linspace(0.05, 0.95, n))

for i, yi in enumerate(grid_x):
    for j, xi in enumerate(grid_y):
        z_sample = np.array([[xi, yi]])
        z_sample = np.tile(z_sample, batch_size).reshape(batch_size, 2)
        x_decoded = decoder.predict(z_sample, batch_size=batch_size)
        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=(10, 10))
plt.imshow(figure, cmap='Greys_r')
plt.show()

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