76 lines
3.0 KiB
Python
76 lines
3.0 KiB
Python
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import tensorflow as tf
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from tensorflow.keras import layers, models
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def model(nbr):
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entree=layers.Input(shape=(576, 560, 3), dtype='float32')
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result=layers.Conv2D(nbr, 3, activation='relu', padding='same')(entree)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(nbr, 3, activation='relu', padding='same')(result)
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result1=layers.BatchNormalization()(result)
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result=layers.MaxPool2D()(result1)
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result=layers.Conv2D(2*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(2*nbr, 3, activation='relu', padding='same')(result)
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result2=layers.BatchNormalization()(result)
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result=layers.MaxPool2D()(result2)
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result=layers.Conv2D(4*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(4*nbr, 3, activation='relu', padding='same')(result)
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result3=layers.BatchNormalization()(result)
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result=layers.MaxPool2D()(result3)
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result=layers.Conv2D(4*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(4*nbr, 3, activation='relu', padding='same')(result)
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result4=layers.BatchNormalization()(result)
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result=layers.MaxPool2D()(result4)
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result=layers.Conv2D(8*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(4*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.UpSampling2D()(result)
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result=tf.concat([result, result4], axis=3)
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result=layers.Conv2D(8*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(4*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.UpSampling2D()(result)
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result=tf.concat([result, result3], axis=3)
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result=layers.Conv2D(4*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(2*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.UpSampling2D()(result)
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result=tf.concat([result, result2], axis=3)
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result=layers.Conv2D(2*nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.UpSampling2D()(result)
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result=tf.concat([result, result1], axis=3)
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result=layers.Conv2D(nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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result=layers.Conv2D(nbr, 3, activation='relu', padding='same')(result)
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result=layers.BatchNormalization()(result)
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sortie=layers.Conv2D(1, 1, activation='sigmoid', padding='same')(result)
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model=models.Model(inputs=entree, outputs=sortie)
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return model
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