# os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # turn off gpu training
""" Script controlling the evaluation of softmax model
ensure the label_scheme, data_dir, and model_path are set correctly below
"""
label_scheme = 1
data_dir = 'C:/out2'
model_path = 'C:\models\softmax-d2-l1-RMSProp-07-256-0.4499-9997.60.l.h5'
env = Environment()
train_list, dev_list, test_list = env.generate_train_dev_test_lists(
data_dir, .95, .025, .025, label_scheme=label_scheme)
model = load_model(model_path)
batch_size = 256
train_steps = 630
val_steps = 58
test_steps = 58
nb_epoch = 10
score = model.evaluate_generator(
env.single_distortion_data_generator(test_list,
data_dir,
batch_size=batch_size,
flatten=True,
batch_name="test",
steps=test_steps,
label_scheme=label_scheme),
steps=test_steps #len(test_list)/batch_size
)
print('Test score:', score[0])
print('Test accuracy:', score[1])
save_weights_only=False,
mode='max',
period=5)
checkpoint2 = ModelCheckpoint(filepath2,
verbose=1,
save_best_only=False,
save_weights_only=True,
period=1)
callbacks_list = [checkpoint, checkpoint2]
# custom_model.save_weights("C:/models/vgg16weights-"+optimizer+"-00-"+str(batch_size)+".h5")
history = custom_model.fit_generator(
env.single_distortion_data_generator(train_list,
data_dir,
batch_size=batch_size,
flatten=False,
batch_name="train",
steps=train_steps,
label_scheme=label_scheme),
steps_per_epoch=train_steps, #len(train_list)/batch_size,
epochs=nb_epoch,
verbose=1,
validation_data=env.single_distortion_data_generator(
dev_list,
data_dir,
batch_size=batch_size,
flatten=False,
batch_name="dev",
steps=val_steps,
label_scheme=label_scheme),
validation_steps=val_steps, #len(dev_list)/batch_size,
