def build(self, input_shape):
    self._conv_layer_1 = tf.keras.layers.Conv2D(filters=self._filters[0], kernel_size=self._kernel_sizes[0], strides=1, padding='same')
    self._conv_layer_2 = tf.keras.layers.Conv2D(filters=self._filters[1], kernel_size=self._kernel_sizes[1], strides=1, padding='same')
    self._conv_layer_3 = tf.keras.layers.Conv2D(filters=self._filters[2], kernel_size=self._kernel_sizes[2], strides=2, padding='same')
    self._batch_norm_layer_1 = tf.keras.layers.BatchNormalization()
    self._batch_norm_layer_2 = tf.keras.layers.BatchNormalization()
    self._batch_norm_layer_3 = tf.keras.layers.BatchNormalization()
def build(self, input_shape):
    self._conv_layer_1 = tf.keras.layers.Conv2D(filters=self._filters[0], kernel_size=self._kernel_sizes[0], strides=1, padding='same')
    self._conv_layer_2 = tf.keras.layers.Conv2D(filters=self._filters[1], kernel_size=self._kernel_sizes[1], strides=1, padding='same')
    self._conv_layer_3 = tf.keras.layers.Conv2D(filters=self._filters[2], kernel_size=self._kernel_sizes[2], strides=2, padding='same')
    self._batch_norm_layer_1 = tf.keras.layers.BatchNormalization()
    self._batch_norm_layer_2 = tf.keras.layers.BatchNormalization()
    self._batch_norm_layer_3 = tf.keras.layers.BatchNormalization()