
""" AlexNet.
References:
    - Alex Krizhevsky, Ilya Sutskever & Geoffrey E. Hinton. ImageNet
    Classification with Deep Convolutional Neural Networks. NIPS, 2012.
Links:
    - [AlexNet Paper](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
"""

import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression

def alexnet(width, height, LR):
	# Building 'AlexNet'
	# print("alexnet")
	network = input_data(shape=[None, width, height, 1])
	network = conv_2d(network, 96, 11, strides=4, activation='relu')
	network = max_pool_2d(network, 3, strides=2)
	network = local_response_normalization(network)
	network = conv_2d(network, 256, 5, activation='relu')
	network = max_pool_2d(network, 3, strides=2)
	network = local_response_normalization(network)
	network = conv_2d(network, 384, 3, activation='relu')
	network = conv_2d(network, 384, 3, activation='relu')
	network = conv_2d(network, 256, 3, activation='relu')
	network = max_pool_2d(network, 3, strides=2)
	network = local_response_normalization(network)
	network = fully_connected(network, 4096, activation='tanh')
	network = dropout(network, 0.5)
	network = fully_connected(network, 4096, activation='tanh')
	network = dropout(network, 0.5)
	network = fully_connected(network, 2, activation='softmax')
	
	network = regression(network, optimizer='momentum',
	                     loss='categorical_crossentropy',
	                     learning_rate=LR)

	# Training
	model = tflearn.DNN(network, checkpoint_path='model_alexnet',
	                    max_checkpoints=1, tensorboard_verbose=2)
	return model

'''
model.fit(X, Y, n_epoch=1000, validation_set=0.1, shuffle=True,
          show_metric=True, batch_size=64, snapshot_step=200,
          snapshot_epoch=False, run_id='alexnet_oxflowers17')
'''