from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten

		'''
		w_init   = tf.contrib.layers.variance_scaling_initializer()

		# glimpse1 = tf.image.extract_glimpse(inputs, [glimpse_size1,glimpse_size1], self.prev_loc, centered=True, normalized=True)

		# glimpse2 = tf.image.extract_glimpse(inputs, [glimpse_size2,glimpse_size2], self.prev_loc, centered=True, normalized=True)
		# glimpse2 = tf.image.resize(glimpse2,        [glimpse_size1,glimpse_size1])

		# glimpse3 = tf.image.extract_glimpse(inputs, [glimpse_size3,glimpse_size3], self.prev_loc, centered=True, normalized=True)
		# glimpse3 = tf.image.resize(glimpse3,        [glimpse_size1,glimpse_size1])

		# self.glimpses = tf.concat([glimpse1,glimpse2,glimpse3],axis=-1)

		# Block 1
		conv1a   =  Conv2D(padding="same", filters=RNN_SIZE//8, kernel_size=[8, 8],   strides=4, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(self.inputs)
		conv1b   =  Conv2D(padding="same", filters=RNN_SIZE//8, kernel_size=[3, 3],   strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv1a)
		conv1c   =  Conv2D(padding="same", filters=RNN_SIZE//8, kernel_size=[3, 3],   strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv1b)
		pool1    =  MaxPool2D(pool_size=[2,2])(conv1c)

		# Block 2
		conv2a   =  Conv2D(padding="same", filters=RNN_SIZE//4, kernel_size=[3, 3],   strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(pool1)
		conv2b   =  Conv2D(padding="same", filters=RNN_SIZE//4, kernel_size=[3, 3],   strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv2a)
		conv2c   =  Conv2D(padding="same", filters=RNN_SIZE//4, kernel_size=[3, 3],   strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv2b)
		pool2    =  MaxPool2D(pool_size=[2,2])(conv2c)

		# Block 3
		conv3a   =  Conv2D(padding="same", filters=RNN_SIZE//2, kernel_size=[3, 3],   strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(pool2)
		conv3b   =  Conv2D(padding="same", filters=RNN_SIZE//2, kernel_size=[3, 3],   strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv3a)
