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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
import numpy as np


    

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def get_proper_images(raw):
    raw_float = np.array(raw, dtype=float) 
    images = raw_float.reshape([-1, 3, 32, 32])
    images = images.transpose([0, 2, 3, 1])
    return images

def onehot_labels(labels):
    return np.eye(100)[labels]

def unpickle(file):
    import cPickle
    fo = open(file, 'rb')
    dict = cPickle.load(fo)
    fo.close()
    return dict


    

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X = get_proper_images(unpickle('cifar-100-python/train')['data'])
Y = onehot_labels(unpickle('cifar-100-python/train')['fine_labels'])
X_test = get_proper_images(unpickle('cifar-100-python/test')['data'])
Y_test = onehot_labels(unpickle('cifar-100-python/test')['fine_labels'])


    

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img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()


    

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# Real-time data augmentation
img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_rotation(max_angle=15.)
#img_aug.add_random_crop((28, 28))
#img_aug.add_random_blur(sigma_max=5.0)


    

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# Convolutional network building
network = input_data(shape=[None, 32, 32, 3])
network = conv_2d(network, 32, 3, strides=1, padding='same', activation='relu', bias=True, 
                  bias_init='zeros', weights_init='uniform_scaling')
network = max_pool_2d(network, 2 , strides=None, padding='same')
network = conv_2d(network, 64, 3, strides=1, padding='same', activation='relu', bias=True, 
                  bias_init='zeros', weights_init='uniform_scaling')
network = conv_2d(network, 64, 3 , strides=1, padding='same', activation='relu', bias=True, 
                  bias_init='zeros', weights_init='uniform_scaling')
network = max_pool_2d(network, 2 , strides=None, padding='same')
network = fully_connected(network, 600, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 100, activation='softmax')
network = regression(network, optimizer='adam',
                     loss='categorical_crossentropy',
                     learning_rate=0.001)


    

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# 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=100 , run_id="withoutaugpre")