# Arda Mavi
import os
import numpy
from get_dataset import get_dataset
from get_model import get_model, save_model
from keras.callbacks import ModelCheckpoint, TensorBoard
epochs = 15
batch_size = 6
def train_model(model, X, X_test, Y, Y_test):
checkpoints = []
if not os.path.exists('Data/Checkpoints/'):
os.makedirs('Data/Checkpoints/')
checkpoints.append(ModelCheckpoint('Data/Checkpoints/best_weights.h5', monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=True, mode='auto', period=1))
checkpoints.append(TensorBoard(log_dir='Data/Checkpoints/./logs', histogram_freq=1, write_graph=True, write_images=True, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None))
'''
# Creates live data:
# For better yield. The duration of the training is extended.
from keras.preprocessing.image import ImageDataGenerator
generated_data = ImageDataGenerator(rotation_range=30, width_shift_range=0.1, height_shift_range=0.1, zoom_range=0.2) # For include left hand data add: 'horizontal_flip = True'
generated_data.fit(X)
model.fit_generator(generated_data.flow(X, Y, batch_size=batch_size), steps_per_epoch=X.shape[0]/batch_size, epochs=epochs, validation_data=(X_test, Y_test), callbacks=checkpoints)
'''
model.fit(X, Y, batch_size=batch_size, epochs=epochs, validation_data=(X_test, Y_test), shuffle=True, callbacks=checkpoints)
return model
def main():
X, X_test, Y, Y_test = get_dataset()
model = get_model()
model = train_model(model, X, X_test, Y, Y_test)
save_model(model)
return model
if __name__ == '__main__':
main()
