chartolz.blogg.se

Model ( inputs, outputs ) model = make_model ( input_shape = image_size + ( 3 ,), num_classes = 2 ) keras. Dense ( units, activation = activation )( x ) return keras. GlobalAveragePooling2D ()( x ) if num_classes = 2 : activation = "sigmoid" units = 1 else : activation = "softmax" units = num_classes x = layers. SeparableConv2D ( 1024, 3, padding = "same" )( x ) x = layers. add () # Add back residual previous_block_activation = x # Set aside next residual x = layers. Conv2D ( size, 1, strides = 2, padding = "same" )( previous_block_activation ) x = layers. MaxPooling2D ( 3, strides = 2, padding = "same" )( x ) # Project residual residual = layers. SeparableConv2D ( size, 3, padding = "same" )( x ) x = layers. Activation ( "relu" )( x ) previous_block_activation = x # Set aside residual for size in : x = layers. Conv2D ( 64, 3, padding = "same" )( x ) x = layers. Conv2D ( 32, 3, strides = 2, padding = "same" )( x ) x = layers.

Input ( shape = input_shape ) # Image augmentation block x = data_augmentation ( inputs ) # Entry block x = layers. Def make_model ( input_shape, num_classes ): inputs = keras.