def __init__(self, class_names, image_size=100, learning_rate=0.001, test_split=0.1):

self.model = None

self.image_size = image_size

self.learning_rate = learning_rate

self.classes = [class_name.lower() for class_name in class_names]

self.image_data = []

self.labels = []

self.test_split = test_split

def _extract_label(self, image_name):

zeros = [0 for i in range(len(self.classes))]

label_name = image_name.split('.')[0]

index = self.classes.index(label_name.lower())

zeros[index] = 1

return zeros

def _process_image(self, image):

label = self._extract_label(image)

img = io.imread(image)

img = imresize(img, (self.image_size, self.image_size, 3))

self.image_data.append(np.array(img))

self.labels.append(np.array(label))

def prepare_data(self, images):

for image in images:

self._process_image(image)

def _image_to_array(self, image):

img = io.imread(image)

img = imresize(img, (self.image_size, self.image_size, 3))

return img

def build_model(self):

network = input_data(shape=[None, self.image_size, self.image_size, 3])

conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2')

pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)

pool1_3_3 = local_response_normalization(pool1_3_3)

conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce')

conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3')

conv2_3_3 = local_response_normalization(conv2_3_3)

pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')

# 3a

inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')

inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce')

inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu',

name='inception_3a_3_3')

inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu',

name='inception_3a_5_5_reduce')

inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu',

name='inception_3a_5_5')

inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool')

inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu',

name='inception_3a_pool_1_1')

inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1],

mode='concat', axis=3)

# 3b

inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1')

inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu',

name='inception_3b_3_3_reduce')

inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu',

name='inception_3b_3_3')

inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu',

name='inception_3b_5_5_reduce')

inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name='inception_3b_5_5')

inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')

inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu',

name='inception_3b_pool_1_1')

inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1],

mode='concat', axis=3, name='inception_3b_output')

pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')

# 4a

inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')

inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu',

name='inception_4a_3_3_reduce')

inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu',

name='inception_4a_3_3')

inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu',

name='inception_4a_5_5_reduce')

inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu',

name='inception_4a_5_5')

inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')

inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu',

name='inception_4a_pool_1_1')

inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1],

mode='concat', axis=3, name='inception_4a_output')

# 4b

inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')

inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu',

name='inception_4b_3_3_reduce')

inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu',

name='inception_4b_3_3')

inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu',

name='inception_4b_5_5_reduce')

inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu',

name='inception_4b_5_5')

inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')

inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu',

name='inception_4b_pool_1_1')

inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1],

mode='concat', axis=3, name='inception_4b_output')

# 4c

inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_1_1')

inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu',

name='inception_4c_3_3_reduce')

inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu',

name='inception_4c_3_3')

inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu',

name='inception_4c_5_5_reduce')

inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu',

name='inception_4c_5_5')

inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)

inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu',

name='inception_4c_pool_1_1')

inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1],

mode='concat', axis=3, name='inception_4c_output')

# 4d

inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')

inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu',

name='inception_4d_3_3_reduce')

inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu',

name='inception_4d_3_3')

inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu',

name='inception_4d_5_5_reduce')

inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu',

name='inception_4d_5_5')

inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')

inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu',

name='inception_4d_pool_1_1')

inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1],

mode='concat', axis=3, name='inception_4d_output')

# 4e

inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')

inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu',

name='inception_4e_3_3_reduce')

inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu',

name='inception_4e_3_3')

inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu',

name='inception_4e_5_5_reduce')

inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu',

name='inception_4e_5_5')

inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')

inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu',

name='inception_4e_pool_1_1')

inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1],

axis=3, mode='concat')

pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')

# 5a

inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')

inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu',

name='inception_5a_3_3_reduce')

inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu',

name='inception_5a_3_3')

inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu',

name='inception_5a_5_5_reduce')

inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu',

name='inception_5a_5_5')

inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')

inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu',

name='inception_5a_pool_1_1')

inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1],

axis=3, mode='concat')

# 5b

inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1')

inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu',

name='inception_5b_3_3_reduce')

inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu',

name='inception_5b_3_3')

inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu',

name='inception_5b_5_5_reduce')

inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu',

name='inception_5b_5_5')

inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')

inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu',

name='inception_5b_pool_1_1')

inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1],

axis=3, mode='concat')

pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)

pool5_7_7 = dropout(pool5_7_7, 0.4)

loss = fully_connected(pool5_7_7, len(self.classes), activation='softmax')

network = regression(loss, optimizer='momentum',

loss='categorical_crossentropy',

learning_rate=self.learning_rate)

model = tflearn.DNN(network, tensorboard_dir='log')

return model

def train_model(self, model_name, epochs=5, batch_size=64):

X = self.image_data

y = self.labels

split = int(len(X) * self.test_split)

X_train, X_test = X[split:], X[:split]

y_train, y_test = y[split:], y[:split]

model = self.build_model()

model.fit(X_train, y_train, n_epoch=epochs, shuffle=True, validation_set=(X_test, y_test), show_metric=True,

batch_size=batch_size)

model.save(model_name)

self.model = model

def load_model(self, model_file):

model = self.build_model()

model.load(model_file)

self.model = model

def predict_image(self, image):

img = self._image_to_array(image)

results = self.model.predict([img])[0]

most_probable = max(results)

results = list(results)

most_probable_index = results.index(most_probable)

class_name = self.classes[most_probable_index]