import tensorflow as tf

                                  filter_depths=[256, 256, 1024],
                                  kernel_size=3)

        x = tf.keras.layers.AveragePooling2D(pool_size=7, strides=1,
                                        padding="valid", name="pool")(x)

        x = tf.reshape(x, shape=(-1, 1024))

        self.logits = self.__fully_connected(name="fc_nsfw",
                                             inputs=x, num_outputs=2)
        self.predictions = tf.nn.softmax(self.logits, name="predictions")

    """Get weights for layer with given name
    """
    def __get_weights(self, layer_name, field_name):
        if not layer_name in self.weights:
            raise ValueError("No weights for layer named '{}' found"
                             .format(layer_name))

        w = self.weights[layer_name]
