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train.py
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train.py
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import os
import warnings
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
warnings.simplefilter('ignore')
import keras
import numpy as np
import pickle
from keras.layers import Conv3D, MaxPooling2D, Convolution2D, Dropout, Dense, Flatten, LSTM
from keras.models import Sequential, save_model
from keras.utils import np_utils
from scipy.io import loadmat
def load_data(mat_file_path, width=28, height=28):
def rotate(img):
flipped = np.fliplr(img)
return np.rot90(flipped)
if not os.path.exists('model/'):
os.makedirs('model/')
mat = loadmat(mat_file_path)
mapping = {kv[0]:kv[1:][0] for kv in mat['dataset'][0][0][2]}
pickle.dump(mapping, open('model/mapping.p', 'wb'))
size = len(mat['dataset'][0][0][0][0][0][0])
training_images = mat['dataset'][0][0][0][0][0][0].reshape(size, height, width, 1)
training_labels = mat['dataset'][0][0][0][0][0][1]
size = len(mat['dataset'][0][0][1][0][0][0])
testing_images = mat['dataset'][0][0][1][0][0][0].reshape(size, height, width, 1)
testing_labels = mat['dataset'][0][0][1][0][0][1]
length = len(testing_images)
for i in range(len(testing_images)):
print('%d/%d (%.2lf%%)' % (i + 1, length, ((i + 1) / length) * 100), end='\r')
testing_images[i] = rotate(testing_images[i])
print()
training_images = training_images.astype('float32') / 255
testing_images = testing_images.astype('float32') / 255
nb_classes = len(mapping)
return ((training_images, training_labels), (testing_images, testing_labels), mapping, nb_classes)
def build_net(training_data, width=28, height=28):
(x_train, y_train), (x_test, y_test), mapping, nb_classes = training_data
input_shape = (height, width, 1)
nb_filters = 32
pool_size = (2, 2)
kernel_size = (3, 3)
model = Sequential()
model.add(Convolution2D(nb_filters, kernel_size, padding='valid',
input_shape=input_shape, activation='relu'))
model.add(Convolution2D(nb_filters, kernel_size, activation='relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])
print(model.summary())
return model
def train(model, training_data, batch_size=256, epochs=10):
(x_train, y_train), (x_test, y_test), mapping, nb_classes = training_data
y_train = np_utils.to_categorical(y_train, nb_classes)
y_test = np_utils.to_categorical(y_test, nb_classes)
tb_callback = keras.callbacks.TensorBoard(log_dir='./model/Graph',
histogram_freq=0, write_graph=True, write_images=True)
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1,
validation_data=(x_test, y_test), callbacks=[tb_callback])
score = model.evaluate(x_test, y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
model_yaml = model.to_yaml()
with open('model/model.yaml', 'w') as yaml_file:
yaml_file.write(model_yaml)
save_model(model, 'model/model.h5')
if __name__ == '__main__':
np.random.seed(10)
training_data = load_data('data/emnist-byclass.mat')
# training_data = load_data('data/emnist-balanced.mat')
model = build_net(training_data)
train(model, training_data)