Initial commit

Tensorflow/tutoriel35/model_cond.py

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