def test_cifar_resnet_error(device_id):
target_device = DeviceDescriptor.gpu_device(0)
DeviceDescriptor.set_default_device(target_device)
try:
base_path = os.path.join(
os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
*"Image/CIFAR/v0/cifar-10-batches-py".split("/"))
except KeyError:
base_path = os.path.join(
*
"../../../../Examples/Image/Miscellaneous/CIFAR-10/cifar-10-batches-py"
.split("/"))
base_path = os.path.normpath(base_path)
os.chdir(os.path.join(base_path, '..'))
test_error = cifar_resnet(base_path)
expected_test_error = 0.7
assert np.allclose(test_error,
expected_test_error,
atol=TOLERANCE_ABSOLUTE)
pe = classification_error(classifier_output, label_var)
# Instantiate the trainer object to drive the model training
lr = learning_rates_per_sample(0.0078125)
trainer = Trainer(classifier_output, ce, pe,
[sgd_learner(classifier_output.owner.parameters(), lr)])
# Get minibatches of images to train with and perform model training
mb_size = 32
training_progress_output_freq = 20
num_mbs = 1000
for i in range(0, num_mbs):
mb = minibatch_source.get_next_minibatch(mb_size)
# Specify the mapping of input variables in the model to actual minibatch data to be trained with
arguments = {
image_input: mb[features_si].m_data,
label_var: mb[labels_si].m_data
}
trainer.train_minibatch(arguments)
print_training_progress(trainer, i, training_progress_output_freq)
if __name__ == '__main__':
# Specify the target device to be used for computing
target_device = DeviceDescriptor.gpu_device(0)
DeviceDescriptor.set_default_device(target_device)
cifar_resnet()
# Instantiate the resnet classification model
classifier_output = resnet_classifer(image_input, num_classes)
ce = cross_entropy_with_softmax(classifier_output, label_var)
pe = classification_error(classifier_output, label_var)
# Instantiate the trainer object to drive the model training
lr = learning_rates_per_sample(0.0078125)
trainer = Trainer(classifier_output, ce, pe, [sgd_learner(classifier_output.owner.parameters(), lr)])
# Get minibatches of images to train with and perform model training
mb_size = 32
training_progress_output_freq = 20
num_mbs = 1000
for i in range(0, num_mbs):
mb=minibatch_source.get_next_minibatch(mb_size)
# Specify the mapping of input variables in the model to actual minibatch data to be trained with
arguments = {image_input : mb[features_si].m_data, label_var : mb[labels_si].m_data}
trainer.train_minibatch(arguments)
print_training_progress(trainer, i, training_progress_output_freq)
if __name__=='__main__':
# Specify the target device to be used for computing
target_device = DeviceDescriptor.gpu_device(0)
DeviceDescriptor.set_default_device(target_device)
cifar_resnet()
