50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
from tensorflow.keras import layers, models
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def generator_model():
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entree_bruit =layers.Input(shape=(100), dtype='float32')
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entree_classe=layers.Input(shape=(10), dtype='float32')
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result=layers.concatenate([entree_bruit, entree_classe])
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result=layers.Dense(7*7*256, use_bias=False)(result)
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result=layers.BatchNormalization()(result)
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result=layers.LeakyReLU()(result)
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result=layers.Reshape((7, 7, 256))(result)
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result=layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False)(result)
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result=layers.BatchNormalization()(result)
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result=layers.LeakyReLU()(result)
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result=layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False)(result)
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result=layers.BatchNormalization()(result)
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result=layers.LeakyReLU()(result)
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sortie=layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh')(result)
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model=models.Model(inputs=[entree_bruit, entree_classe], outputs=sortie)
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return model
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def discriminator_model():
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entree_image =layers.Input(shape=(28, 28, 1), dtype='float32')
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entree_classe=layers.Input(shape=(10), dtype='float32')
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result1=layers.Dense(28*28, use_bias=False)(entree_classe)
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result1=layers.Reshape((28, 28, 1))(result1)
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result=layers.concatenate([entree_image, result1])
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result=layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same')(result)
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result=layers.LeakyReLU()(result)
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result=layers.Dropout(0.3)(result)
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result=layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same')(result)
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result=layers.LeakyReLU()(result)
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result=layers.Dropout(0.3)(result)
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result=layers.Flatten()(result)
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sortie=layers.Dense(1)(result)
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model=models.Model(inputs=[entree_image, entree_classe], outputs=sortie)
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return model
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