def main(_):
# Please make sure that a root dir is specified before running this script!
root_dir = None
model_config = mc.ModelConfig(model_type='nin', dataset='cifar10')
load_and_run(model_config, root_dir)
print('Loaded a NIN_CIFAR10 model.')
# example for resnet cifar100
model_config = mc.ModelConfig(model_type='resnet', dataset='cifar100')
load_and_run(model_config, root_dir)
print('Loaded a RESNET_CIFAR100 model.')
def main(_):
# Please make sure that a root dir is specified before running this script!
root_dir = None
model_config = mc.ModelConfig(
model_type='nin', dataset='cifar10', root_dir=root_dir)
load_and_run(model_config, root_dir)
print('Loaded a NIN_CIFAR10 model.')
print('Evaluating the NIN_CIFAR10 model.')
eval_result = evaluate_model(model_config, root_dir)
print('Test Accuracy: {}'.format(eval_result))
print('Stored Test Accuracy: {}'.format(model_config.test_stats()))
print('Stored Train Accuracy: {}'.format(model_config.training_stats()))
print('==========================================')
# example for resnet cifar100
model_config = mc.ModelConfig(model_type='resnet', dataset='cifar100')
load_and_run(model_config, root_dir)
print('Loaded a RESNET_CIFAR100 model.')
count = 0
for wide_multiplier in d[key]['wide_multiplier']:
for batchnorm in d[key]['batchnorm']:
for dropout_prob in d[key]['dropout_prob']:
for augmentation in d[key]['augmentation']:
for decay_fac in d[key]['decay_fac']:
for copy in d[key]['copy']:
for normalization in d[key]['normalization']:
for learning_rate in d[key]['learning_rate']:
model_config = mc.ModelConfig(
model_type=model_type,
dataset=dataset,
wide_multiplier=wide_multiplier,
batchnorm=batchnorm,
dropout_prob=dropout_prob,
data_augmentation=augmentation,
l2_decay_factor=decay_fac,
normalization=normalization,
learning_rate=learning_rate,
copy=copy,
root_dir=root_dir)
count += 1
train_loss = np.NaN
train_cross_entropy = np.NaN
train_global_step = np.NaN
train_accuracy = np.NaN
eval_loss = np.NaN
eval_cross_entropy = np.NaN
eval_global_step = np.NaN
eval_accuracy = np.NaN
try:
