def main(model='lenet', num_epochs=500, min_epochs=100, improve_epochs=50,
subset_sizes=None, validation_intervals=1,
batchsize=500,
sample_chooser=None, refine=False, out_path=None,
csv_path=None, indices_out_path=None):
if subset_sizes is None:
subset_sizes = [500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000]
subset_sizes = cmdline_helpers.coerce_num_list_parameter(subset_sizes,
num_type=int, name='subset_sizes')
num_epochs = cmdline_helpers.coerce_num_list_parameter(num_epochs, N=len(subset_sizes),
num_type=int, name='num_epochs')
min_epochs = cmdline_helpers.coerce_num_list_parameter(min_epochs, N=len(subset_sizes),
num_type=int, name='min_epochs')
improve_epochs = cmdline_helpers.coerce_num_list_parameter(improve_epochs, N=len(subset_sizes),
num_type=int, name='improve_epochs')
validation_intervals = cmdline_helpers.coerce_num_list_parameter(validation_intervals, N=len(subset_sizes),
num_type=int, name='validation_intervals')
N_train, N_val, N_test = mnist_dataset.train_val_test_size()
builder = mnist_architecture.network_builder(model)
mnist = mnist_dataset.MNISTTrainValTest()
trainer, indices_labelled_history, validation_error_history, test_error_history = \
active_learning.active_learning_image_classifier(sample_chooser=sample_chooser, model_builder=builder, N_train=N_train,
batchsize=batchsize,
refine=refine, datasets_fn=mnist.datasets, subset_sizes=subset_sizes,
num_epochs=num_epochs,
min_epochs=min_epochs, improve_epochs=improve_epochs,
validation_intervals=validation_intervals,
batch_xform_fn=mnist_dataset.xform_mnist_batch,
n_train_repetitions_in_case_of_failure=3)
print('Results:')
print('N-train\t\tErr')
for labelled_indices, err in zip(indices_labelled_history, test_error_history):
print('{0}\t\t{1:.2f}%'.format(labelled_indices.shape[0], err * 100.0))
if csv_path is not None:
writer = csv.writer(open(csv_path, 'wb'))
writer.writerow(['# samples', 'Error %'])
for labelled_indices, err in zip(indices_labelled_history, test_error_history):
writer.writerow([labelled_indices.shape[0], err * 100.0])
if out_path is not None:
print('Saving model to {0} ...'.format(out_path))
utils.save_model(out_path, trainer.network)
if indices_out_path is not None:
np.save(indices_out_path, indices_labelled_history[-1])
def main(model='lenet',
num_epochs=500,
min_epochs=200,
improve_epochs=250,
batchsize=500,
training_subset=None,
aug_factor=1,
out_path=None):
# Load the dataset
print("Loading data...")
mnist = mnist_dataset.MNISTTrainValTest()
# Generate the indices of the subset of the training set
training_subset_indices = None
if training_subset is not None:
training_subset_indices = mnist.balanced_train_subset_indices(
training_subset)
# Get the train, validation and test sets
train_ds, val_ds, test_ds = mnist.datasets(training_subset_indices)
# Get network builder function for named model
builder = mnist_architecture.network_builder(model)
# Build the image classifier for the given model builder
clf = image_classifier.ImageClassifier.for_model(builder)
# Set verbosity
clf.trainer.report(verbosity=trainer.VERBOSITY_EPOCH)
# Set data transformation function
clf.trainer.data_xform_fn(batch_xform_fn=mnist_dataset.xform_mnist_batch)
# Set training length
clf.trainer.train_for(num_epochs=num_epochs,
min_epochs=min_epochs,
val_improve_num_epochs=improve_epochs,
val_improve_epochs_factor=0)
# Train
clf.trainer.train(train_ds, val_ds, test_ds, batchsize=batchsize)
if out_path is not None:
print('Saving model to {0} ...'.format(out_path))
utils.save_model(out_path, clf.network)
def main(model='lenet', num_epochs=500, min_epochs=200, improve_epochs=250, batchsize=500,
training_subset=None, aug_factor=1, out_path=None):
# Load the dataset
print("Loading data...")
mnist = mnist_dataset.MNISTTrainValTest()
# Generate the indices of the subset of the training set
training_subset_indices = None
if training_subset is not None:
training_subset_indices = mnist.balanced_train_subset_indices(training_subset)
# Get the train, validation and test sets
train_ds, val_ds, test_ds = mnist.datasets(training_subset_indices)
# Get network builder function for named model
builder = mnist_architecture.network_builder(model)
# Build the image classifier for the given model builder
clf = image_classifier.ImageClassifier.for_model(builder)
# Set verbosity
clf.trainer.report(verbosity=trainer.VERBOSITY_EPOCH)
# Set data transformation function
clf.trainer.data_xform_fn(batch_xform_fn=mnist_dataset.xform_mnist_batch)
# Set training length
clf.trainer.train_for(num_epochs=num_epochs, min_epochs=min_epochs, val_improve_num_epochs=improve_epochs,
val_improve_epochs_factor=0)
# Train
clf.trainer.train(train_ds, val_ds, test_ds, batchsize=batchsize)
if out_path is not None:
print('Saving model to {0} ...'.format(out_path))
utils.save_model(out_path, clf.network)