def main():
# FOR CIFAR-10
# dataset_generators = {
# 'train': read_data.cifar10_dataset_generator('train', 512),
# 'test': read_data.cifar10_dataset_generator('test', -1)
# }
# FOR SVHN
dataset_generators = {
'train': read_data.svhn_dataset_generator('train', 512),
'test': read_data.svhn_dataset_generator('test', 512)
}
print("map")
model_dict = model.apply_classification_loss(model.cnn_map)
train_model(model_dict, dataset_generators, epoch_n=20, print_every=100)
print("stride 44")
model_dict = model.apply_classification_loss(model.cnn_modification_s44)
train_model(model_dict, dataset_generators, epoch_n=20, print_every=100)
print("stride 24")
model_dict = model.apply_classification_loss(model.cnn_modification_s24)
train_model(model_dict, dataset_generators, epoch_n=20, print_every=100)
print("stride11")
model_dict = model.apply_classification_loss(model.cnn_modification_s11)
train_model(model_dict, dataset_generators, epoch_n=20, print_every=100)
print("filternum=12")
model_dict = model.apply_classification_loss(model.cnn_modification_f12)
train_model(model_dict, dataset_generators, epoch_n=20, print_every=100)
print("filternum=24")
model_dict = model.apply_classification_loss(model.cnn_modification_f24)
train_model(model_dict, dataset_generators, epoch_n=20, print_every=100)
print("filternum=48")
model_dict = model.apply_classification_loss(model.cnn_modification_f48)
train_model(model_dict, dataset_generators, epoch_n=20, print_every=100)
def svhn_tuner():
cifar10_dataset_generators = {
'train': read_data.cifar10_dataset_generator('train', 1000),
'test': read_data.cifar10_dataset_generator('test', -1)
}
dataset_generators = {
'train': read_data.svhn_dataset_generator('train', 512),
'test': read_data.svhn_dataset_generator('test', 512)
}
cnn_expanded_dict = model2.apply_classification_loss(model2.cnn_expanded)
print("####----- Running CNN_Expanded on SVHN and saving ------####")
new_train_model(cnn_expanded_dict,
dataset_generators,
epoch_n=50,
print_every=10,
variable_list=cnn_expanded_dict['var_list'],
save_model=True)
print("#### --------- Loading saved weights into new input ------ ####")
new_train_model(cnn_expanded_dict,
cifar10_dataset_generators,
epoch_n=100,
print_every=10,
variable_list=cnn_expanded_dict['var_list'],
load_model=True)
def main():
# FOR SVHN
dataset_generators = {
'train': read_data.svhn_dataset_generator('train', 256),
'test': read_data.svhn_dataset_generator('test', 256)
}
model_dict = model.apply_classification_loss(model.cnn_map)
visualize(model_dict,
dataset_generators,
epoch_n=20,
print_every=100,
batch_size=256)
def test_saving():
dataset_generators = {
'train': read_data.svhn_dataset_generator('train', 512),
'test': read_data.svhn_dataset_generator('test', 512)
}
model_dict = model.apply_classification_loss(model.cnn_modified)
new_train_model(model_dict,
dataset_generators,
epoch_n=100,
print_every=10,
variable_list=model_dict['var_list'],
save_model=True,
load_model=True)
def main():
# FOR CIFAR-10
# dataset_generators = {
# 'train': read_data.cifar10_dataset_generator('train', 512),
# 'test': read_data.cifar10_dataset_generator('test', -1)
# }
# FOR SVHN
dataset_generators = {
"train": read_data.svhn_dataset_generator("train", 512),
"test": read_data.svhn_dataset_generator("test", 512),
}
model_dict = model.apply_classification_loss(model.cnn_map)
train_model(model_dict, dataset_generators, epoch_n=50, print_every=10)
def main():
# FOR SVHN
dataset_generators = {
'train': read_data.svhn_dataset_generator('train', 512),
'test': read_data.svhn_dataset_generator('test', 512)
}
model_dict = model.apply_classification_loss(model.cnn_map)
new_train_model(model_dict,
dataset_generators,
epoch_n=50,
print_every=30,
save_model=True)
cnn_expanded_dict = model.apply_classification_loss(model.cnn_expanded)
new_train_model(cnn_expanded_dict,
dataset_generators,
epoch_n=50,
print_every=30,
load_model=True)