val_dataset = supervised_dataset.SupervisedDataset(X_val, y_val)
train_iterator = train_dataset.iterator(mode='random_uniform',
batch_size=64,
num_batches=31000)
val_iterator = val_dataset.iterator(mode='random_uniform',
batch_size=64,
num_batches=31000)
# Do data augmentation (crops, flips, rotations, scales, intensity)
data_augmenter = util.DataAugmenter2(crop_shape=(96, 96),
flip=True,
gray_on=True)
normer = util.Normer3(filter_size=5, num_channels=1)
module_list_train = [data_augmenter, normer]
module_list_val = [normer]
preprocessor_train = util.Preprocessor(module_list_train)
preprocessor_val = util.Preprocessor(module_list_val)
print('Training Model')
for x_batch, y_batch in train_iterator:
x_batch = preprocessor_train.run(x_batch)
monitor.start()
log_prob, accuracy = model.train(x_batch, y_batch)
monitor.stop(1 - accuracy)
if monitor.test:
monitor.start()
x_val_batch, y_val_batch = val_iterator.next()
x_val_batch = preprocessor_val.run(x_val_batch)
val_accuracy = model.eval(x_val_batch, y_val_batch)
monitor.stop_test(1 - val_accuracy)
print std
train_dataset = supervised_dataset.SupervisedDataset(X_train, y_train)
val_dataset = supervised_dataset.SupervisedDataset(X_val, y_val)
train_iterator = train_dataset.iterator(mode='random_uniform',
batch_size=64,
num_batches=31000)
val_iterator = val_dataset.iterator(mode='random_uniform',
batch_size=64,
num_batches=31000)
# Create object to local contrast normalize a batch.
# Note: Every batch must be normalized before use.
normer = util.Normer3(filter_size=5, num_channels=1)
module_list = [normer]
preprocessor = util.Preprocessor(module_list)
print('Training Model')
for x_batch, y_batch in train_iterator:
#x_batch = preprocessor.run(x_batch)
x_batch = (x_batch - mean) / std
# loop over batch
for i in range(len(x_batch)):
# hide patch for an image
x_batch[i] = hide_patch(x_batch[i])
monitor.start()
log_prob, accuracy = model.train(x_batch, y_batch)
monitor.stop(1 - accuracy) # monitor takes error instead of accuracy
test_dataset = supervised_dataset.SupervisedDataset(X_test, y_test)
train_iterator = train_dataset.iterator(mode='random_uniform',
batch_size=64,
num_batches=31000)
test_iterator = test_dataset.iterator(mode='random_uniform',
batch_size=64,
num_batches=31000)
# Do data augmentation (crops, flips, rotations, scales, intensity)
data_augmenter = util.DataAugmenter2(crop_shape=(96, 96),
flip=True,
gray_on=True)
normer = util.Normer3(filter_size=5, num_channels=1)
module_list_train = [data_augmenter, normer]
module_list_test = [normer]
preprocessor_train = util.Preprocessor(module_list_train)
preprocessor_test = util.Preprocessor(module_list_test)
print('Training Model')
for x_batch, y_batch in train_iterator:
x_batch = preprocessor_train.run(x_batch)
monitor.start()
log_prob, accuracy = model.train(x_batch, y_batch)
monitor.stop(1 - accuracy)
if monitor.test:
monitor.start()
x_test_batch, y_test_batch = test_iterator.next()
x_test_batch = preprocessor_test.run(x_test_batch)
test_accuracy = model.eval(x_test_batch, y_test_batch)
monitor.stop_test(1 - test_accuracy)
