from __future__ import division, print_function, absolute_import
import matplotlib.pyplot as plt
import numpy as np
from scipy.misc import toimage
import tflearn
from tflearn.data_utils import shuffle, to_categorical
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.estimator import regression
from tflearn.data_preprocessing import ImagePreprocessing
from tflearn.data_augmentation import ImageAugmentation

# Data loading and preprocessing
from tflearn.datasets import cifar10
(X, Y), (X_test, Y_test) = cifar10.load_data()
X, Y = shuffle(X, Y)
Y = to_categorical(Y,10)
Y_test = to_categorical(Y_test,10)

# Real-time data preprocessing
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()

# Real-time data augmentation
img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_rotation(max_angle=25.)

# Convolutional network building
network = input_data(shape=[None, 32, 32, 3],
                     data_preprocessing=img_prep,
                     data_augmentation=img_aug)
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 10, activation='softmax')
network = regression(network, optimizer='adam',
                     loss='categorical_crossentropy',
                     learning_rate=0.001)

# Train using classifier
model = tflearn.DNN(network, tensorboard_verbose=0)

'''
model.fit(X, Y, n_epoch=50, shuffle=True, validation_set=(X_test, Y_test),
          show_metric=True, batch_size=96, run_id='cifar10_cnn')
		  
#saves weights of the model	
model.save('CIFAR10_tflearncnn.model')
'''

#just load the last trained model and ready for test
model.load('CIFAR10_tflearncnn.model')
#test on individual

test_num = 5
predicted_result = model.predict([X_test[test_num]])
predicted_result = np.array(predicted_result, dtype='float')
print('Predicted Label = ',np.argmax(predicted_result))
original_label = np.array(Y_test[test_num])
print('Original Label = ',np.argmax(original_label))

IMG = X_test[test_num]
plt.imshow(toimage(IMG))
plt.show()
