How to use the anna.util.DataAugmenter function in anna

To help you get started, we’ve selected a few anna examples, based on popular ways it is used in public projects.

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ifp-uiuc / an-analysis-of-unsupervised-pre-training-iclr-2015 / cifar10 / 50_to_1 / cnn_au / train.py View on Github

train_data = numpy.load(os.path.join(reduced_data_path, 'train_X_split_0.npy'))
train_labels = numpy.load(os.path.join(reduced_data_path,
                          'train_y_split_0.npy'))
test_data = numpy.load('/data/cifar10/test_X.npy')
test_labels = numpy.load('/data/cifar10/test_y.npy')

train_dataset = supervised_dataset.SupervisedDataset(train_data, train_labels)
test_dataset = supervised_dataset.SupervisedDataset(test_data, test_labels)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=128, num_batches=100000)
test_iterator = test_dataset.iterator(mode='random_uniform', batch_size=128,
                                      num_batches=100000)

normer = util.Normer2(filter_size=5, num_channels=3)
augmenter = util.DataAugmenter(2, (32, 32), flip=False)

print('Training Model')
for x_batch, y_batch in train_iterator:
    x_batch = x_batch.transpose(1, 2, 3, 0)
    x_batch = augmenter.run(x_batch)
    x_batch = normer.run(x_batch)
    #y_batch = numpy.int64(numpy.argmax(y_batch, axis=1))
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch)
    monitor.stop(1-accuracy)  # monitor takes error instead of accuracy

    if monitor.test:
        monitor.start()
        x_test_batch, y_test_batch = test_iterator.next()
        x_test_batch = x_test_batch.transpose(1, 2, 3, 0)
        x_test_batch = normer.run(x_test_batch)

ifp-uiuc / an-analysis-of-unsupervised-pre-training-iclr-2015 / cifar10 / 1_to_1 / cnn_adu / train.py View on Github

# Loading CIFAR-10 dataset
print('Loading Data')
train_data = numpy.load('/data/cifar10/train_X.npy')
train_labels = numpy.load('/data/cifar10/train_y.npy')
test_data = numpy.load('/data/cifar10/test_X.npy')
test_labels = numpy.load('/data/cifar10/test_y.npy')

train_dataset = supervised_dataset.SupervisedDataset(train_data, train_labels)
test_dataset = supervised_dataset.SupervisedDataset(test_data, test_labels)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=128, num_batches=100000)
test_iterator = test_dataset.iterator(mode='random_uniform', batch_size=128,
                                      num_batches=100000)

normer = util.Normer2(filter_size=5, num_channels=3)
augmenter = util.DataAugmenter(2, (32, 32), flip=True)

print('Training Model')
for x_batch, y_batch in train_iterator:
    x_batch = x_batch.transpose(1, 2, 3, 0)
    x_batch = augmenter.run(x_batch)
    x_batch = normer.run(x_batch)
    #y_batch = numpy.int64(numpy.argmax(y_batch, axis=1))
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch)
    monitor.stop(1-accuracy)  # monitor takes error instead of accuracy

    if monitor.test:
        monitor.start()
        x_test_batch, y_test_batch = test_iterator.next()
        x_test_batch = x_test_batch.transpose(1, 2, 3, 0)
        x_test_batch = normer.run(x_test_batch)

ifp-uiuc / an-analysis-of-unsupervised-pre-training-iclr-2015 / cifar10 / 50_to_1 / cnn_adu / train.py View on Github

train_data = numpy.load(os.path.join(reduced_data_path, 'train_X_split_0.npy'))
train_labels = numpy.load(os.path.join(reduced_data_path,
                          'train_y_split_0.npy'))
test_data = numpy.load('/data/cifar10/test_X.npy')
test_labels = numpy.load('/data/cifar10/test_y.npy')

train_dataset = supervised_dataset.SupervisedDataset(train_data, train_labels)
test_dataset = supervised_dataset.SupervisedDataset(test_data, test_labels)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=128, num_batches=100000)
test_iterator = test_dataset.iterator(mode='random_uniform', batch_size=128,
                                      num_batches=100000)

normer = util.Normer2(filter_size=5, num_channels=3)
augmenter = util.DataAugmenter(2, (32, 32))

print('Training Model')
for x_batch, y_batch in train_iterator:
    x_batch = x_batch.transpose(1, 2, 3, 0)
    x_batch = augmenter.run(x_batch)
    x_batch = normer.run(x_batch)
    #y_batch = numpy.int64(numpy.argmax(y_batch, axis=1))
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch)
    monitor.stop(1-accuracy)  # monitor takes error instead of accuracy

    if monitor.test:
        monitor.start()
        x_test_batch, y_test_batch = test_iterator.next()
        x_test_batch = x_test_batch.transpose(1, 2, 3, 0)
        x_test_batch = normer.run(x_test_batch)

ifp-uiuc / an-analysis-of-unsupervised-pre-training-iclr-2015 / cifar10 / 10_to_1 / cnn_a / train.py View on Github

train_data = numpy.load(os.path.join(reduced_data_path, 'train_X_split_0.npy'))
train_labels = numpy.load(os.path.join(reduced_data_path,
                          'train_y_split_0.npy'))
test_data = numpy.load('/data/cifar10/test_X.npy')
test_labels = numpy.load('/data/cifar10/test_y.npy')

train_dataset = supervised_dataset.SupervisedDataset(train_data, train_labels)
test_dataset = supervised_dataset.SupervisedDataset(test_data, test_labels)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=128, num_batches=100000)
test_iterator = test_dataset.iterator(mode='random_uniform', batch_size=128,
                                      num_batches=100000)

normer = util.Normer2(filter_size=5, num_channels=3)
augmenter = util.DataAugmenter(2, (32, 32), flip=False)

print('Training Model')
for x_batch, y_batch in train_iterator:
    x_batch = x_batch.transpose(1, 2, 3, 0)
    x_batch = augmenter.run(x_batch)
    x_batch = normer.run(x_batch)
    #y_batch = numpy.int64(numpy.argmax(y_batch, axis=1))
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch)
    monitor.stop(1-accuracy)  # monitor takes error instead of accuracy

    if monitor.test:
        monitor.start()
        x_test_batch, y_test_batch = test_iterator.next()
        x_test_batch = x_test_batch.transpose(1, 2, 3, 0)
        x_test_batch = normer.run(x_test_batch)

ifp-uiuc / an-analysis-of-unsupervised-pre-training-iclr-2015 / cifar10 / 10_to_1 / cnn_ad / train.py View on Github

train_data = numpy.load(os.path.join(reduced_data_path, 'train_X_split_0.npy'))
train_labels = numpy.load(os.path.join(reduced_data_path,
                          'train_y_split_0.npy'))
test_data = numpy.load('/data/cifar10/test_X.npy')
test_labels = numpy.load('/data/cifar10/test_y.npy')

train_dataset = supervised_dataset.SupervisedDataset(train_data, train_labels)
test_dataset = supervised_dataset.SupervisedDataset(test_data, test_labels)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=128, num_batches=100000)
test_iterator = test_dataset.iterator(mode='random_uniform', batch_size=128,
                                      num_batches=100000)

normer = util.Normer2(filter_size=5, num_channels=3)
augmenter = util.DataAugmenter(2, (32, 32))

print('Training Model')
for x_batch, y_batch in train_iterator:
    x_batch = x_batch.transpose(1, 2, 3, 0)
    x_batch = augmenter.run(x_batch)
    x_batch = normer.run(x_batch)
    #y_batch = numpy.int64(numpy.argmax(y_batch, axis=1))
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch)
    monitor.stop(1-accuracy)  # monitor takes error instead of accuracy

    if monitor.test:
        monitor.start()
        x_test_batch, y_test_batch = test_iterator.next()
        x_test_batch = x_test_batch.transpose(1, 2, 3, 0)
        x_test_batch = normer.run(x_test_batch)

ifp-uiuc / an-analysis-of-unsupervised-pre-training-iclr-2015 / stl10 / cnn_adcu / train.py View on Github

X_test = numpy.float32(X_test)
X_test /= 255.0
X_test *= 1.0

train_dataset = supervised_dataset.SupervisedDataset(X_train, y_train)
test_dataset = supervised_dataset.SupervisedDataset(X_test, y_test)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=128, num_batches=45000)
test_iterator = test_dataset.iterator(
    mode='random_uniform', batch_size=128, num_batches=45000)

# Create object to local contrast normalize a batch.
# Note: Every batch must be normalized before use.
normer = util.Normer2(filter_size=5, num_channels=3)
augmenter = util.DataAugmenter(16, (96, 96), color_on=True)

print('Training Model')
for x_batch, y_batch in train_iterator:
    x_batch = x_batch.transpose(1, 2, 3, 0)
    x_batch = augmenter.run(x_batch)
    x_batch = normer.run(x_batch)
    # y_batch = numpy.int64(numpy.argmax(y_batch, axis=1))
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch-1)
    monitor.stop(1-accuracy)  # monitor takes error instead of accuracy

    if monitor.test:
        monitor.start()
        x_test_batch, y_test_batch = test_iterator.next()
        x_test_batch = x_test_batch.transpose(1, 2, 3, 0)
        x_test_batch = normer.run(x_test_batch)

ifp-uiuc / an-analysis-of-unsupervised-pre-training-iclr-2015 / cifar10 / 1_to_1 / cnn_a / train.py View on Github

# Loading CIFAR-10 dataset
print('Loading Data')
train_data = numpy.load('/data/cifar10/train_X.npy')
train_labels = numpy.load('/data/cifar10/train_y.npy')
test_data = numpy.load('/data/cifar10/test_X.npy')
test_labels = numpy.load('/data/cifar10/test_y.npy')

train_dataset = supervised_dataset.SupervisedDataset(train_data, train_labels)
test_dataset = supervised_dataset.SupervisedDataset(test_data, test_labels)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=128, num_batches=100000)
test_iterator = test_dataset.iterator(mode='random_uniform', batch_size=128,
                                      num_batches=100000)

normer = util.Normer2(filter_size=5, num_channels=3)
augmenter = util.DataAugmenter(2, (32, 32), flip=False)

print('Training Model')
for x_batch, y_batch in train_iterator:
    x_batch = x_batch.transpose(1, 2, 3, 0)
    x_batch = augmenter.run(x_batch)
    x_batch = normer.run(x_batch)
    #y_batch = numpy.int64(numpy.argmax(y_batch, axis=1))
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch)
    monitor.stop(1-accuracy)  # monitor takes error instead of accuracy

    if monitor.test:
        monitor.start()
        x_test_batch, y_test_batch = test_iterator.next()
        x_test_batch = x_test_batch.transpose(1, 2, 3, 0)
        x_test_batch = normer.run(x_test_batch)

ifp-uiuc / an-analysis-of-unsupervised-pre-training-iclr-2015 / stl10 / cnn_ad / train.py View on Github

X_test = numpy.float32(X_test)
X_test /= 255.0
X_test *= 2.0

train_dataset = supervised_dataset.SupervisedDataset(X_train, y_train)
test_dataset = supervised_dataset.SupervisedDataset(X_test, y_test)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=128, num_batches=45000)
test_iterator = test_dataset.iterator(
    mode='random_uniform', batch_size=128, num_batches=45000)

# Create object to local contrast normalize a batch.
# Note: Every batch must be normalized before use.
normer = util.Normer2(filter_size=5, num_channels=3)
augmenter = util.DataAugmenter(16, (96, 96))

print('Training Model')
for x_batch, y_batch in train_iterator:        
    x_batch = x_batch.transpose(1, 2, 3, 0) 
    x_batch = augmenter.run(x_batch)  
    x_batch = normer.run(x_batch)   
    # y_batch = numpy.int64(numpy.argmax(y_batch, axis=1))
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch-1)
    monitor.stop(1-accuracy) # monitor takes error instead of accuracy    
    
    if monitor.test:
        monitor.start()
        x_test_batch, y_test_batch = test_iterator.next()
        x_test_batch = x_test_batch.transpose(1, 2, 3, 0)
        x_test_batch = normer.run(x_test_batch)

How to use the anna.util.DataAugmenter function in anna

To help you get started, we’ve selected a few anna examples, based on popular ways it is used in public projects.

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