def main():
learning_rate = 10e-4
batch_size = 32
epochs = 15
height = 256
width = 256
# Learning Rate multipliers for each layer
learning_rate_multipliers = {}
learning_rate_multipliers['Conv1'] = 1
learning_rate_multipliers['Conv2'] = 1
learning_rate_multipliers['Conv3'] = 1
learning_rate_multipliers['Conv4'] = 1
learning_rate_multipliers['Dense1'] = 1
# l2-regularization penalization for each layer
l2_penalization = {}
l2_penalization['Conv1'] = 1e-2
l2_penalization['Conv2'] = 1e-2
l2_penalization['Conv3'] = 1e-2
l2_penalization['Conv4'] = 1e-2
l2_penalization['Dense1'] = 1e-4
# Path where the logs will be saved
tensorboard_log_path = './logs/siamese_net_lr10e-4'
siamese_network = SiameseNetwork(
learning_rate=learning_rate,
batch_size=batch_size,
epochs=epochs,
learning_rate_multipliers=learning_rate_multipliers,
l2_regularization_penalization=l2_penalization,
tensorboard_log_path=tensorboard_log_path)
# Data
os.chdir("/data8t/ljq/whale_data/whale_data/siamese_networks/")
train_data_temp = np.load('training_data.npy')
train_data = [train_data_temp[0]]
train_data.append(train_data_temp[1])
train_label = np.load('training_label.npy')
val_data_temp = np.load('validation_data.npy')
val_data = [val_data_temp[0]]
val_data.append(val_data_temp[1])
val_label = np.load('validation_label.npy')
# train_base_num = 1000
# train_data = [np.zeros((train_base_num * 6, height, width, 3)) for j in range(2)]
# train_label = np.zeros((train_base_num * 6, 1))
# val_data = [np.ones((train_base_num * 2, height, width, 3)) for j in range(2)]
# val_label = np.ones((train_base_num * 2, 1))
siamese_network.train_siamese_network(model_name='siamese_net_whale',
train_data=train_data,
train_label=train_label,
val_data=val_data,
val_label=val_label)
def main():
dataset_path = 'gen_simulated_data'
use_augmentation = False
learning_rate = 10e-4
batch_size = 32
# Learning Rate multipliers for each layer
learning_rate_multipliers = {}
learning_rate_multipliers['Conv1'] = 1
learning_rate_multipliers['Conv2'] = 1
learning_rate_multipliers['Conv3'] = 1
learning_rate_multipliers['Conv4'] = 1
#learning_rate_multipliers['Conv5'] = 1
learning_rate_multipliers['Dense1'] = 1
# l2-regularization penalization for each layer
l2_penalization = {}
l2_penalization['Conv1'] = 1e-2
l2_penalization['Conv2'] = 1e-2
l2_penalization['Conv3'] = 1e-2
l2_penalization['Conv4'] = 1e-2
#l2_penalization['Conv5'] = 1e-2
l2_penalization['Dense1'] = 1e-4
# Path where the logs will be saved
tensorboard_log_path = './logs/siamese_net_lr10e-4'
siamese_network = SiameseNetwork(
dataset_path=dataset_path,
learning_rate=learning_rate,
batch_size=batch_size,
use_augmentation=use_augmentation,
learning_rate_multipliers=learning_rate_multipliers,
l2_regularization_penalization=l2_penalization,
tensorboard_log_path=tensorboard_log_path)
# Final layer-wise momentum (mu_j in the paper)
momentum = 0.9
# linear epoch slope evolution
momentum_slope = 0.01
# support_set指支持一次"图像验证"的图像集合，即1：N image evaluation的N
support_set_size = 20
# evaluation时需要进行验证的图像数目
number_of_tasks_per_bucket = 20
evaluate_each = 500
number_of_train_iterations = 1000000
validation_accuracy = siamese_network.train_siamese_network(
number_of_iterations=number_of_train_iterations,
support_set_size=support_set_size,
final_momentum=momentum,
momentum_slope=momentum_slope,
evaluate_each=evaluate_each,
model_name='siamese_net_lr10e-4')
if validation_accuracy == 0:
evaluation_accuracy = 0
else:
# Load the weights with best validation accuracy
siamese_network.model.load_weights('.models/siamese_net_lr10e-4.h5')
evaluation_accuracy = siamese_network.chars_loader.one_shot_test(
siamese_network.model, support_set_size,
number_of_tasks_per_bucket, False)
print('Final Evaluation Accuracy = ' + str(evaluation_accuracy))
def main():
dataset_path = 'Omniglot Dataset'
use_augmentation = True
learning_rate = 10e-4
#batch_size = 128
batch_size = 256
# Learning Rate multipliers for each layer
learning_rate_multipliers = {}
learning_rate_multipliers['Conv1'] = 1
learning_rate_multipliers['Conv2'] = 1
learning_rate_multipliers['Conv3'] = 1
learning_rate_multipliers['Conv4'] = 1
learning_rate_multipliers['Dense1'] = 1
# l2-regularization penalization for each layer
l2_penalization = {}
l2_penalization['Conv1'] = 1e-2
l2_penalization['Conv2'] = 1e-2
l2_penalization['Conv3'] = 1e-2
l2_penalization['Conv4'] = 1e-2
l2_penalization['Dense1'] = 1e-4
# Path where the logs will be saved
tensorboard_log_path = './logs/siamese_net_lr10e-4'
siamese_network = SiameseNetwork(
dataset_path=dataset_path,
learning_rate=learning_rate,
batch_size=batch_size,
use_augmentation=use_augmentation,
learning_rate_multipliers=learning_rate_multipliers,
l2_regularization_penalization=l2_penalization,
tensorboard_log_path=tensorboard_log_path)
# Final layer-wise momentum (mu_j in the paper)
momentum = 0.9
# linear epoch slope evolution
momentum_slope = 0.01
support_set_size = 20
evaluate_each = 1000
#number_of_train_iterations = 250000
number_of_train_iterations = 125000
validation_accuracy = siamese_network.train_siamese_network(
number_of_iterations=number_of_train_iterations,
support_set_size=support_set_size,
final_momentum=momentum,
momentum_slope=momentum_slope,
evaluate_each=evaluate_each,
model_name='siamese_net_lr10e-4')
if validation_accuracy == 0:
evaluation_accuracy = 0
else:
# Load the weights with best validation accuracy
siamese_network.model.load_weights('./models/siamese_net_lr10e-4.h5')
evaluation_accuracy = siamese_network.omniglot_loader.one_shot_test(
siamese_network.model, 20, 40, False)
print('Final Evaluation Accuracy = ' + str(evaluation_accuracy))
def main():
dataset_path = 'data/split_dataset_NOMASK/train'
model_name = 'model_train_NOMASK'
use_augmentation = True
learning_rate = 1e-2
batch_size = 16
# Learning Rate multipliers for each layer
learning_rate_multipliers = {'Conv1': 1, 'Conv2': 1, 'Conv3': 1, 'Conv4': 1, 'Dense1': 1}
# l2-regularization penalization for each layer
l2_penalization = {'Conv1': 1e-2, 'Conv2': 1e-2, 'Conv3': 1e-2, 'Conv4': 1e-2, 'Dense1': 1e-4}
# Path where the logs will be saved
tensorboard_log_path = f'./logs/{model_name}'
siamese_network = SiameseNetwork(
dataset_path=dataset_path,
learning_rate=learning_rate,
batch_size=batch_size, use_augmentation=use_augmentation,
learning_rate_multipliers=learning_rate_multipliers,
l2_regularization_penalization=l2_penalization,
tensorboard_log_path=tensorboard_log_path
)
# Final layer-wise momentum (mu_j in the paper)
momentum = 0.9
# linear epoch slope evolution
momentum_slope = 0.01
support_set_size = 20
evaluate_each = 500
number_of_train_iterations = 30000
validation_accuracy = siamese_network.train_siamese_network(number_of_iterations=number_of_train_iterations,
support_set_size=support_set_size,
final_momentum=momentum,
momentum_slope=momentum_slope,
evaluate_each=evaluate_each,
model_name=model_name)
if validation_accuracy == 0:
evaluation_accuracy = 0
else:
# Load the weights with best validation accuracy
siamese_network.model.load_weights(f'./models/{model_name}.h5')
evaluation_accuracy = siamese_network.omniglot_loader.one_shot_test(siamese_network.model, 20, 40, False)
print('Final Evaluation Accuracy = ' + str(evaluation_accuracy))
def bayesian_optimization_function(x):
dataset_path = 'Omniglot Dataset'
current_learning_rate = float(x[:, 0])
current_momentum = float(x[:, 1])
current_momentum_slope = float(x[:, 2])
current_conv1_multiplier = float(x[:, 3])
current_conv2_multiplier = float(x[:, 4])
current_conv3_multiplier = float(x[:, 5])
current_conv4_multiplier = float(x[:, 6])
current_dense1_multiplier = float(x[:, 7])
current_conv1_penalization = float(x[:, 8])
current_conv2_penalization = float(x[:, 9])
current_conv3_penalization = float(x[:, 10])
current_conv4_penalization = float(x[:, 11])
current_dense1_penalization = float(x[:, 12])
model_name = 'siamese_net_lr_' + str(current_learning_rate) + \
'momentum_' + str(current_momentum) + '_slope_' + \
str(current_momentum_slope)
global current_model_number
current_model_number += 1
tensorboard_log_path = './logs/' + str(current_model_number)
# Learning Rate multipliers for each layer
learning_rate_multipliers = {}
learning_rate_multipliers['Conv1'] = current_conv1_multiplier
learning_rate_multipliers['Conv2'] = current_conv2_multiplier
learning_rate_multipliers['Conv3'] = current_conv3_multiplier
learning_rate_multipliers['Conv4'] = current_conv4_multiplier
learning_rate_multipliers['Dense1'] = current_dense1_multiplier
# l2-regularization penalization for each layer
l2_penalization = {}
l2_penalization['Conv1'] = current_conv1_penalization
l2_penalization['Conv2'] = current_conv2_penalization
l2_penalization['Conv3'] = current_conv3_penalization
l2_penalization['Conv4'] = current_conv4_penalization
l2_penalization['Dense1'] = current_dense1_penalization
K.clear_session()
siamese_network = SiameseNetwork(
dataset_path=dataset_path,
learning_rate=current_learning_rate,
batch_size=32,
use_augmentation=True,
learning_rate_multipliers=learning_rate_multipliers,
l2_regularization_penalization=l2_penalization,
tensorboard_log_path=tensorboard_log_path)
current_model_number += 1
support_set_size = 20
evaluate_each = 500
number_of_train_iterations = 100000
validation_accuracy = siamese_network.train_siamese_network(
number_of_iterations=number_of_train_iterations,
support_set_size=support_set_size,
final_momentum=current_momentum,
momentum_slope=current_momentum_slope,
evaluate_each=evaluate_each,
model_name=model_name)
if validation_accuracy == 0:
evaluation_accuracy = 0
else:
# Load the weights with best validation accuracy
siamese_network.model.load_weights('models/' + model_name + '.h5')
evaluation_accuracy = siamese_network.data_loader.one_shot_test(
siamese_network.model, 20, 40, False)
print("Model: " + model_name + ' | Accuracy: ' +
str(evaluation_accuracy))
K.clear_session()
return 1 - evaluation_accuracy