def create_dilated_cnn_receptive_field_25(input_shape, kernel_regularizer=None):
    return Sequential([Conv2D(filters=32, kernel_size=5, input_shape=input_shape, kernel_regularizer=kernel_regularizer), BatchNormalization(), Activation('relu'), Conv2D(filters=32, kernel_size=5, kernel_regularizer=kernel_regularizer), BatchNormalization(), Activation('relu'), Conv2D(filters=32, kernel_size=5, kernel_regularizer=kernel_regularizer, dilation_rate=2), BatchNormalization(), Activation('relu'), Conv2D(filters=32, kernel_size=3, kernel_regularizer=kernel_regularizer), BatchNormalization(), Activation('relu'), Conv2D(filters=32, kernel_size=3, kernel_regularizer=kernel_regularizer), BatchNormalization(), Activation('relu'), Conv2D(filters=32, kernel_size=3, kernel_regularizer=kernel_regularizer), BatchNormalization(), Activation('relu'), Conv2D(filters=32, kernel_size=3, kernel_regularizer=kernel_regularizer), BatchNormalization()])
def create_dilated_cnn_receptive_field_25(input_shape, kernel_regularizer=None):
    return Sequential([Conv2D(filters=32, kernel_size=5, input_shape=input_shape, kernel_regularizer=kernel_regularizer), BatchNormalization(), Activation('relu'), Conv2D(filters=32, kernel_size=5, kernel_regularizer=kernel_regularizer), BatchNormalization(), Activation('relu'), Conv2D(filters=32, kernel_size=5, kernel_regularizer=kernel_regularizer, dilation_rate=2), BatchNormalization(), Activation('relu'), Conv2D(filters=32, kernel_size=3, kernel_regularizer=kernel_regularizer), BatchNormalization(), Activation('relu'), Conv2D(filters=32, kernel_size=3, kernel_regularizer=kernel_regularizer), BatchNormalization(), Activation('relu'), Conv2D(filters=32, kernel_size=3, kernel_regularizer=kernel_regularizer), BatchNormalization(), Activation('relu'), Conv2D(filters=32, kernel_size=3, kernel_regularizer=kernel_regularizer), BatchNormalization()])