def test_cast():
stream = DataStream(
IterableDataset(OrderedDict([("features", numpy.array([1, 2, 3]).astype("float64")), ("targets", [0, 1, 0])]))
)
wrapper = Cast(stream, "float32", which_sources=("features",))
assert_equal(list(wrapper.get_epoch_iterator()), [(numpy.array(1), 0), (numpy.array(2), 1), (numpy.array(3), 0)])
assert all(f.dtype == "float32" for f, t in wrapper.get_epoch_iterator())
def test_cast():
stream = DataStream(
IterableDataset({'features': numpy.array([1, 2, 3]).astype('float64'),
'targets': [0, 1, 0]}))
wrapper = Cast(stream, 'float32', which_sources=('features',))
assert_equal(
list(wrapper.get_epoch_iterator()),
[(numpy.array(1), 0), (numpy.array(2), 1), (numpy.array(3), 0)])
assert all(f.dtype == 'float32' for f, t in wrapper.get_epoch_iterator())
def test_cast():
stream = DataStream(
IterableDataset(
OrderedDict([
('features', numpy.array([1, 2, 3]).astype('float64')),
('targets', [0, 1, 0])])))
wrapper = Cast(stream, 'float32', which_sources=('features',))
assert_equal(
list(wrapper.get_epoch_iterator()),
[(numpy.array(1), 0), (numpy.array(2), 1), (numpy.array(3), 0)])
assert all(f.dtype == 'float32' for f, t in wrapper.get_epoch_iterator())
def batch_iterator(dataset, batchsize, shuffle=False):
if shuffle:
train_scheme = ShuffledScheme(examples=dataset.num_examples, batch_size=batchsize)
else:
train_scheme = SequentialScheme(examples=dataset.num_examples, batch_size=batchsize)
stream = DataStream.default_stream(dataset=dataset, iteration_scheme=train_scheme)
stream_scale = ScaleAndShift(stream, 1./256.0, 0, which_sources=('features',))
stream_data = Cast(stream_scale, dtype=theano.config.floatX, which_sources=('features',))
return stream_data.get_epoch_iterator()
class TestCast(object):
def setUp(self):
dataset = IterableDataset(
OrderedDict([
('features', numpy.array([1, 2, 3]).astype('float64')),
('targets', [0, 1, 0])]),
axis_labels={'features': ('batch'), 'targets': ('batch')})
self.stream = DataStream(dataset)
self.wrapper = Cast(
self.stream, 'float32', which_sources=('features',))
def test_cast(self):
assert_equal(
list(self.wrapper.get_epoch_iterator()),
[(numpy.array(1), 0), (numpy.array(2), 1), (numpy.array(3), 0)])
assert all(
f.dtype == 'float32' for f, t in self.wrapper.get_epoch_iterator())
def test_axis_labels_are_passed_through(self):
assert_equal(self.wrapper.axis_labels, self.stream.axis_labels)
class TestCast(object):
def setUp(self):
dataset = IterableDataset(
OrderedDict([
('features', numpy.array([1, 2, 3]).astype('float64')),
('targets', [0, 1, 0])]),
axis_labels={'features': ('batch'), 'targets': ('batch')})
self.stream = DataStream(dataset)
self.wrapper = Cast(
self.stream, 'float32', which_sources=('features',))
def test_cast(self):
assert_equal(
list(self.wrapper.get_epoch_iterator()),
[(numpy.array(1), 0), (numpy.array(2), 1), (numpy.array(3), 0)])
assert all(
f.dtype == 'float32' for f, t in self.wrapper.get_epoch_iterator())
def test_axis_labels_are_passed_through(self):
assert_equal(self.wrapper.axis_labels, self.stream.axis_labels)
upscale_test_stream = MaximumImageDimensions(
data_stream = downscale_test_stream,
maximum_shape = image_size,
which_sources=('image_features',)
)
scaled_test_stream = ScaleAndShift(
data_stream = upscale_test_stream,
scale = 1./255,
shift = 0,
which_sources = ('image_features',)
)
data_test_stream = Cast(
data_stream = scaled_test_stream,
dtype = 'float32',
which_sources = ('image_features',)
)
test_x =tensor.tensor4('image_features')
predict_function = theano.function(inputs=[test_x], outputs=top_mlp.apply(Flattener().apply(conv_sequence.apply(test_x))))
import csv
csvfile = csv.writer(open("test_pred_overfeat.csv",'wb'))
for i,test_image in enumerate(data_test_stream.get_epoch_iterator()):
prediction = predict_function(test_image[0])[0]
isadog = numpy.argmax(prediction)
csvfile.writerow([str(i+1), str(isadog)])
#get the test stream
from fuel.datasets.dogs_vs_cats import DogsVsCats
from fuel.streams import DataStream, ServerDataStream
from fuel.schemes import ShuffledScheme, SequentialExampleScheme
from fuel.transformers.image import RandomFixedSizeCrop, MinimumImageDimensions, MaximumImageDimensions, Random2DRotation
from fuel.transformers import Flatten, Cast, ScaleAndShift
size = (128,128)
cats = DogsVsCats(('test',))
stream = DataStream.default_stream(cats, iteration_scheme=SequentialExampleScheme(cats.num_examples))
stream_upscale = MaximumImageDimensions(stream, size, which_sources=('image_features',))
stream_scale = ScaleAndShift(stream_upscale, 1./255, 0, which_sources=('image_features',))
stream_data = Cast(stream_scale, dtype='float32', which_sources=('image_features',))
#Load the parameters of the model
params = load_parameter_values('convnet_parameters.pkl')
mo = Model(predict)
mo.set_parameter_values(params)
#Create the forward propagation function
fprop = function(mo.inputs, mo.outputs[0], allow_input_downcast=True)
tab = []
i = 1
#Get the prediction for each example of the test set
for data in stream_data.get_epoch_iterator():
predict = np.argmax(fprop(data))
tab.append([i, predict])
print str(i) + "," + str(predict)
i = i + 1
#Save predictions in a csv file
np.savetxt("dump.csv", tab, delimiter=",", fmt='%d')
stream = DataStream.default_stream(cats,
iteration_scheme=SequentialExampleScheme(
cats.num_examples))
stream_upscale = MaximumImageDimensions(stream,
size,
which_sources=('image_features', ))
stream_scale = ScaleAndShift(stream_upscale,
1. / 255,
0,
which_sources=('image_features', ))
stream_data = Cast(stream_scale,
dtype='float32',
which_sources=('image_features', ))
#Load the parameters of the model
params = load_parameter_values('convnet_parameters.pkl')
mo = Model(predict)
mo.set_parameter_values(params)
#Create the forward propagation function
fprop = function(mo.inputs, mo.outputs[0], allow_input_downcast=True)
tab = []
i = 1
#Get the prediction for each example of the test set
for data in stream_data.get_epoch_iterator():
predict = np.argmax(fprop(data))
tab.append([i, predict])
print str(i) + "," + str(predict)
i = i + 1
#Save predictions in a csv file
np.savetxt("dump.csv", tab, delimiter=",", fmt='%d')