41 lines
2.0 KiB
Python
41 lines
2.0 KiB
Python
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from tensorflow.keras import layers, models
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# Fonction d'activation à tester: sigmoid, tanh, relu,
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def model(size, nbr_cc):
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entree=layers.Input(shape=(size, size, 3), dtype='float32')
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result=layers.Conv2D(nbr_cc, 3, activation='relu', padding='same')(entree)
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result=layers.Conv2D(nbr_cc, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.MaxPool2D()(result)
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result=layers.Conv2D(2*nbr_cc, 3, activation='relu', padding='same')(result)
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result=layers.Conv2D(2*nbr_cc, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(2*nbr_cc, 3, activation='relu', padding='same')(result)
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result=layers.Conv2D(2*nbr_cc, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.MaxPool2D()(result)
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result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
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result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
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result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
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result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
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result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.MaxPool2D()(result)
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result=layers.Flatten()(result)
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result=layers.Dense(1024, activation='relu')(result)
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sortie=layers.Dense(1, activation='sigmoid')(result)
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model=models.Model(inputs=entree, outputs=sortie)
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return model
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