import argparse
import cv2
import numpy as np
from keras.callbacks import LambdaCallback, EarlyStopping
import feature_utility as fu
import myVGG
parser = argparse.ArgumentParser(description=("Model training process."))
# parser.add_argument('data_path', help=("The path of training data set"))
parser.add_argument('--test', help=("Input a single image to check if the model works well."))
args = parser.parse_args()
def main():
model = myVGG.VGG_16()
if args.test is not None:
print ("Test mode")
img = cv2.imread(args.test)
img = fu.preprocessing(img)
img = np.expand_dims(img, axis=0)
y = np.expand_dims(np.asarray([0]), axis=0)
batch_size = 1
model.fit(img, y, nb_epoch=400, \
batch_size=batch_size, \
validation_split=0.2, \
shuffle=True, verbose=0)
return
#input_path = args.data_path
#print("training data path : " + input_path)
#X_train, y_train = fu.extract_features(input_path)
X_fname = '../data/X_train_train.npy'
y_fname = '../data/y_train_train.npy'
X_train = np.load(X_fname)
y_train = np.load(y_fname)
print(X_train.shape)
print(y_train.shape)
print("Training started")
callbacks = []
earlystop_callback = EarlyStopping(monitor='val_loss', patience=5, verbose=0)
batch_print_callback = LambdaCallback(on_batch_begin=lambda batch, logs: print(batch))
epoch_print_callback = LambdaCallback(on_epoch_end=lambda epoch, logs: print("epoch:", epoch))
callbacks.append(earlystop_callback)
callbacks.append(batch_print_callback)
callbacks.append(epoch_print_callback)
batch_size = 512
model.fit(X_train, y_train, nb_epoch=400, \
batch_size=batch_size, \
validation_split=0.2, \
shuffle=True, verbose=0, \
callbacks=callbacks)
model.save_weights('my_model_weights.h5')
scores = model.evaluate(X_train, y_train, verbose=0)
print ("Train loss : %.3f" % scores[0])
print ("Train accuracy : %.3f" % scores[1])
print ("Training finished")
if __name__ == "__main__":
main()
