93 lines
4.0 KiB
Python
93 lines
4.0 KiB
Python
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import tensorflow as tf
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from tensorflow.keras import layers, models
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import config
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def block_resnet(input, filters, kernel_size, reduce, dropout=0.):
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result=layers.Conv2D(filters, kernel_size, strides=1, padding='SAME', activation='relu')(input)
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if dropout is not 0.:
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result=layers.Dropout(dropout)(result)
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if reduce is True:
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result=layers.Conv2D(filters, kernel_size, strides=2, padding='SAME')(result)
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else:
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result=layers.Conv2D(filters, kernel_size, strides=1, padding='SAME')(result)
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if input.shape[-1]==filters:
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if reduce is True:
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shortcut=layers.Conv2D(filters, 1, strides=2, padding='SAME')(input)
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else:
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shortcut=input
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else:
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if reduce is True:
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shortcut=layers.Conv2D(filters, 1, strides=2, padding='SAME')(input)
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else:
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shortcut=layers.Conv2D(filters, 1, strides=1, padding='SAME')(input)
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result=layers.add([result, shortcut])
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if dropout is not 0.:
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result=layers.Dropout(dropout)(result)
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result=layers.Activation('relu')(result)
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result=layers.BatchNormalization()(result)
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return result
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def model(nbr):
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entree=layers.Input(shape=(config.largeur, config.hauteur, 1), dtype='float32')
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result=block_resnet(entree, 2*nbr, 3, False, 0.3)
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result=block_resnet(result, 2*nbr, 3, False, 0.3)
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result=block_resnet(result, 2*nbr, 3, False, 0.3)
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result=block_resnet(result, 2*nbr, 3, True, 0.3)
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result=block_resnet(result, 4*nbr, 3, False, 0.4)
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result=block_resnet(result, 4*nbr, 3, False, 0.4)
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result=block_resnet(result, 4*nbr, 3, False, 0.4)
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result=block_resnet(result, 4*nbr, 3, False, 0.4)
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result=block_resnet(result, 4*nbr, 3, False, 0.4)
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result=block_resnet(result, 4*nbr, 3, False, 0.4)
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result=block_resnet(result, 4*nbr, 3, True, 0.4)
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result=block_resnet(result, 8*nbr, 3, False, 0.4)
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result=block_resnet(result, 8*nbr, 3, False, 0.4)
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result=block_resnet(result, 8*nbr, 3, False, 0.4)
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result=block_resnet(result, 8*nbr, 3, False, 0.4)
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result=block_resnet(result, 8*nbr, 3, False, 0.4)
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result=block_resnet(result, 8*nbr, 3, False, 0.4)
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result=block_resnet(result, 8*nbr, 3, False, 0.4)
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result=block_resnet(result, 8*nbr, 3, False, 0.4)
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result=block_resnet(result, 8*nbr, 3, False, 0.4)
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result=block_resnet(result, 8*nbr, 3, False, 0.4)
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result=block_resnet(result, 8*nbr, 3, False, 0.4)
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result=block_resnet(result, 8*nbr, 3, False, 0.4)
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result=block_resnet(result, 8*nbr, 3, True, 0.4)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=block_resnet(result, 16*nbr, 3, False, 0.5)
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result=layers.AveragePooling2D()(result)
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result=layers.Flatten()(result)
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sortie=layers.Dense(5, activation='sigmoid')(result)
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model=models.Model(inputs=entree, outputs=sortie)
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return model
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