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


		# 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)
		conv3c   =  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)(conv3b)
		pool3    =  MaxPool2D(pool_size=[2,2])(conv3c)

		# final convolutional layer
