layers.py 文件源码

def build(self, input_shape):
        if input_shape[2] % self.shared_pool[0] != 0 or input_shape[3] % self.shared_pool[1] != 0:
            raise Exception('Layer only works if input dimensions can be divided by shared pool dimensions')
        nb_x_pools = int(input_shape[2]/self.shared_pool[0])
        nb_y_pools = int(input_shape[3]/self.shared_pool[1])
        output_shape = self.get_output_shape_for(input_shape)
        if self.dim_ordering == 'th':
            _, nb_filter, output_row, output_col = output_shape
            input_filter = input_shape[1]
        elif self.dim_ordering == 'tf':
            _, output_row, output_col, nb_filter = output_shape
            input_filter = input_shape[3]
        else:
            raise Exception('Invalid dim_ordering: ' + self.dim_ordering)

        self.output_row = output_row
        self.output_col = output_col
        self.W_shape = (nb_filter,input_filter,nb_x_pools,nb_y_pools)#(output_row * output_col, self.nb_row * self.nb_col * input_filter, nb_filter)
        self.W = self.init(self.W_shape, name='{}_W'.format(self.name))

        if self.bias:
            self.b = K.zeros((nb_filter,nb_x_pools, nb_y_pools), name='{}_b'.format(self.name))
            self.trainable_weights = [self.W, self.b]
        else:
            self.trainable_weights = [self.W]

        self.regularizers = []
        if self.W_regularizer:
            self.W_regularizer.set_param(self.W)
            self.regularizers.append(self.W_regularizer)
        if self.bias and self.b_regularizer:
            self.b_regularizer.set_param(self.b)
            self.regularizers.append(self.b_regularizer)
        if self.activity_regularizer:
            self.activity_regularizer.set_layer(self)
            self.regularizers.append(self.activity_regularizer)

        self.constraints = {}
        if self.W_constraint:
            self.constraints[self.W] = self.W_constraint
        if self.bias and self.b_constraint:
            self.constraints[self.b] = self.b_constraint

        if self.initial_weights is not None:
            self.set_weights(self.initial_weights)
            del self.initial_weights