How to use the anna.datasets.unsupervised_dataset.UnsupervisedDataset function in anna

util.set_parameters_from_unsupervised_model(model, checkpoint)
monitor = util.Monitor(model, save_steps=200)

model.conv1.trainable = False
model._compile()

# Loading STL-10 dataset
print('Loading Data')
data = numpy.load('/data/stl10_matlab/unsupervised.npy')
data = numpy.float32(data)
data /= 255.0
data *= 2.0
train_data = data[0:90000, :, :, :]
test_data = data[90000::, :, :, :]

train_dataset = unsupervised_dataset.UnsupervisedDataset(train_data)
test_dataset = unsupervised_dataset.UnsupervisedDataset(test_data)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=128, num_batches=100000)
test_iterator = test_dataset.iterator(mode='sequential', batch_size=128)

# 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)

# Orthogonalize second layer weights.
W2 = model.conv2.W.get_value()
W2 = conv_orthogonalize(W2)
# Scale second layer weights.
s = 2.5
model.conv2.W.set_value(W2*s)

f.close()

model = CAELayer1Model('experiment', './', learning_rate=1e-5)
monitor = util.Monitor(model, save_steps=200)


# Loading STL-10 dataset
print('Loading Data')
data = numpy.load('/data/stl10_matlab/unsupervised.npy')
data = numpy.float32(data)
data /= 255.0
data *= 2.0
train_data = data[0:90000, :, :, :]
test_data = data[90000::, :, :, :]

train_dataset = unsupervised_dataset.UnsupervisedDataset(train_data)
test_dataset = unsupervised_dataset.UnsupervisedDataset(test_data)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=128, num_batches=100000)
test_iterator = test_dataset.iterator(mode='sequential', batch_size=128)

# 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)

# Grab batch for patch extraction.
x_batch = train_iterator.next()
x_batch = x_batch.transpose(1, 2, 3, 0)
x_batch = normer.run(x_batch)
# Grab some patches to initialize weights.
patch_grabber = util.PatchGrabber(64, 5)
patches = patch_grabber.run(x_batch)*0.05