def predict(model):
input_files = fnmatch.filter(
listdir(FLAGS.filePath), FLAGS.filePattern +
FLAGS.predictionSetExtension + '.*.' + FLAGS.extension)
if len(input_files) == 0:
sys.exit("File not found: " + filename_pattern)
for file in input_files:
print('Number of predictions: ', end='')
predict_data_generator = DataGenerator(
data_shape,
num_class,
FLAGS.filePath,
file,
FLAGS.testBatchSize,
useOddSample=True,
)
prediction = model.predict_generator(
predict_data_generator,
predict_data_generator.num_steps(),
max_queue_size=FLAGS.queueSize,
)
np.save(
path.join(
FLAGS.filePath,
file.replace(FLAGS.predictionSetExtension,
FLAGS.predictionExtension).replace(
'.' + FLAGS.extension, '')), prediction)
def test(model):
print('Number of test samples: ', end='')
test_data_generator = DataGenerator(
data_shape,
num_class,
FLAGS.filePath,
FLAGS.filePattern + FLAGS.testSetExtension + '.*.' + FLAGS.extension,
FLAGS.testBatchSize,
useOddSample=True,
percent=FLAGS.testPercent,
shuffle=True,
)
score = model.evaluate_generator(
test_data_generator,
test_data_generator.num_steps(),
max_queue_size=FLAGS.queueSize,
)
print('Test loss: ', score[0])
print('Test accuracy:', score[1])
def main():
model_serial = define_model()
# Save model
model_yaml = model_serial.to_yaml()
model_yaml_file = open(
path.join(FLAGS.modelPath, FLAGS.sessionName + '.model.yaml'), "w")
model_yaml_file.write(model_yaml)
model_yaml_file.close()
#keras.utils.plot_model(model_serial, to_file=path.join(FLAGS.modelPath, FLAGS.sessionName + '.model.png'), show_shapes= True)
# load weights
if FLAGS.startEpoch > 0:
model_serial.load_weights(
path.join(
FLAGS.modelPath, FLAGS.sessionName +
'.weights.{:02d}.hdf5'.format(FLAGS.startEpoch)))
print('Loaded epoch', FLAGS.startEpoch)
if (FLAGS.testOnly == True or FLAGS.predictOnly) and FLAGS.startEpoch == 0:
model_serial.load_weights(
path.join(FLAGS.modelPath, FLAGS.sessionName + '.weights.hdf5'))
model = model_serial
model.summary()
model.compile(
loss='categorical_crossentropy',
optimizer=keras.optimizers.nadam(lr=FLAGS.learningRate),
metrics=['accuracy'],
)
if FLAGS.randomSeed > 0:
random.seed(FLAGS.randomSeed)
if FLAGS.testOnly == True:
test(model)
sys.exit()
if FLAGS.predictOnly == True:
predict(model)
sys.exit()
trainingParameterText = 'BatchSize : {:02d}; learningRate : {:.8f}; L2 : {:.8f}; Dropout : {:.4f}'.format(
FLAGS.batchSize, FLAGS.learningRate, FLAGS.L2, FLAGS.dropout)
print(trainingParameterText)
print('_________________________________________________________________')
print('Number of training samples: ', end='')
training_data_generator = DataGenerator(
data_shape,
num_class,
FLAGS.filePath,
FLAGS.filePattern + FLAGS.trainingSetExtension + '.*.' +
FLAGS.extension,
FLAGS.batchSize,
percent=FLAGS.trainingPercent,
queue_size=FLAGS.queueSize,
shuffle=True,
#class_weight=class_weight,
)
print('Number of validation samples: ', end='')
validation_data_generator = DataGenerator(
data_shape,
num_class,
FLAGS.filePath,
FLAGS.filePattern + FLAGS.validationSetExtension + '.*.' +
FLAGS.extension,
FLAGS.testBatchSize,
percent=FLAGS.validationPercent,
shuffle=True,
#class_weight=class_weight,
)
print('Number of test samples: ', end='')
test_data_generator = DataGenerator(
data_shape,
num_class,
FLAGS.filePath,
FLAGS.filePattern + FLAGS.testSetExtension + '.*.' + FLAGS.extension,
FLAGS.testBatchSize,
percent=FLAGS.testPercent,
shuffle=True,
)
print('_________________________________________________________________')
callback_list = []
callback_list.append(
RecordEpoch(
best_epoch_dict,
weigh_filepath=path.join(
FLAGS.modelPath,
FLAGS.sessionName + '.weights.{epoch:02d}.hdf5'),
csv_log_filepath=path.join(
FLAGS.modelPath,
FLAGS.sessionName + '.' + FLAGS.logExtension,
),
csv_log_header=trainingParameterText,
patience=FLAGS.earlyStoppingPatience,
sleep_after_epoch=sleep_after_epoch,
))
history = model.fit_generator(
training_data_generator,
training_data_generator.num_steps(),
epochs=FLAGS.maxEpoch,
initial_epoch=FLAGS.startEpoch,
verbose=1,
callbacks=callback_list,
#class_weight=class_weight,
validation_data=validation_data_generator,
validation_steps=validation_data_generator.num_steps(),
max_queue_size=FLAGS.queueSize,
)
training_data_generator.terminate()
print('Best epoch:', best_epoch_dict['epoch_index'])
print('Validation accuracy:', best_epoch_dict['val_acc'])
model.load_weights(
path.join(
FLAGS.modelPath, FLAGS.sessionName +
'.weights.{:02d}.hdf5'.format(best_epoch_dict['epoch_index'])))
print('Loaded weights of this epoch')
test(model)
