iteration_scheme=SequentialScheme(
train.num_examples, batch_size))
# upscaled_stream = MinimumImageDimensions(stream, (100, 100), which_sources=('image_features',))
downscaled_stream = DownscaleMinDimension(stream,
100,
which_sources=('image_features', ))
# Our images are of different sizes, so we'll use a Fuel transformer
# to take random crops of size (32 x 32) from each image
cropped_stream = RandomFixedSizeCrop(downscaled_stream, (100, 100),
which_sources=('image_features', ))
rotated_stream = Random2DRotation(cropped_stream,
math.pi / 6,
which_sources=('image_features', ))
flipped_stream = RandomHorizontalFlip(rotated_stream,
which_sources=('image_features', ))
# We'll use a simple MLP, so we need to flatten the images
# from (channel, width, height) to simply (features,)
float_stream = ScaleAndShift(flipped_stream,
1. / 255,
0,
which_sources=('image_features', ))
float32_stream = Cast(float_stream,
numpy.float32,
which_sources=('image_features', ))
start_server(float32_stream, port=port)
def sample_transformations(thestream):
cast_stream = Cast(data_stream=thestream,
dtype='float32',
which_sources=('features', ))
return cast_stream
