用变分自编码器生成图像
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# -*- coding = utf-8 -*- # @Time : 2021/7/22 # @Author : pistachio # @File : p25.py # @Software : PyCharm import keras from keras import layers from keras import backend as K from keras.models import Model import numpy as np from keras.datasets import mnist import matplotlib.pyplot as plt from scipy.stats import norm import tensorflow as tf tf.compat.v1.disable_eager_execution() # build VAE encoder network img_shape = (28, 28, 1) batch_size = 16 latent_dim = 2 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_flatting = 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) # latent spatial sampling function 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]) # VAE decoder network, mapping potential spatial points to images decoder_input = layers.Input(K.int_shape(z)[1:]) x = layers.Dense(np.prod(shape_before_flatting[1:]), activation='relu')(decoder_input) x = layers.Reshape(shape_before_flatting[1:])(x) x = layers.Conv2DTranspose(32, 3, padding='same', activation='relu', strides=(2, 2))(x) x = layers.Conv2D(1, 3, padding='same', activation='sigmoid')(x) decoder = Model(decoder_input, x) z_decoded = decoder(z) # Custom layer for calculating VAE loss 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) return x y = CustomVariationalLayer()([input_img, z_decoded]) # train VAE vae = Model(input_img, y) training = True if training: vae.compile(optimizer=tf.optimizers.RMSprop(lr=0.001, epsilon=1e-7), loss=None, experimental_run_tf_function = False) vae.summary() else: vae.compile(optimizer=tf.optimizers.RMSprop(lr=0.001, epsilon=1e-7), loss=None) vae.summary() (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)) # The grid of a group of points is sampled from the two-dimensional potential space and decoded into an image n = 15 digit_size = 28 figure = np.zeros((digit_size * n, digit_size * n)) 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()
运行结果:
D:\Anaconda\envs\tensorflow\python.exe D:/PYCHARMprojects/Dailypractise/p25.py WARNING:tensorflow:AutoGraph could not transform <bound method CustomVariationalLayer.call of <__main__.CustomVariationalLayer object at 0x000001FBA8B6C4C0>> and will run it as-is. Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Index' To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert 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 2021-07-23 10:14:25.162966: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 60000/60000 [==============================] - ETA: 0s - loss: 0.2277WARNING:tensorflow:From D:\Anaconda\envs\tensorflow\lib\site-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 [==============================] - 155s 3ms/sample - loss: 0.2277 - val_loss: 0.2022 Epoch 2/10 60000/60000 [==============================] - 152s 3ms/sample - loss: 0.1977 - val_loss: 0.1946 Epoch 3/10 60000/60000 [==============================] - 152s 3ms/sample - loss: 0.1921 - val_loss: 0.1895 Epoch 4/10 60000/60000 [==============================] - 151s 3ms/sample - loss: 0.1888 - val_loss: 0.1868 Epoch 5/10 60000/60000 [==============================] - 152s 3ms/sample - loss: 0.1866 - val_loss: 0.1869 Epoch 6/10 60000/60000 [==============================] - 157s 3ms/sample - loss: 0.1849 - val_loss: 0.1852 Epoch 7/10 60000/60000 [==============================] - 162s 3ms/sample - loss: 0.1837 - val_loss: 0.1830 Epoch 8/10 60000/60000 [==============================] - 161s 3ms/sample - loss: 0.1827 - val_loss: 0.1821 Epoch 9/10 60000/60000 [==============================] - 156s 3ms/sample - loss: 0.1818 - val_loss: 0.1840 Epoch 10/10 60000/60000 [==============================] - 152s 3ms/sample - loss: 0.1811 - val_loss: 0.1812
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