def _test_dataset():
import sflow.py as py
data = dataset_testA(16)
for d in tf.feeds(data):
py.imshow(d)
if not py.plot_pause():
break
def _test_dataset():
import sflow.py as py
data = dataset_valB(16)
for d in tf.feeds(data):
py.imshow([d.A, d.B])
if not py.plot_pause():
break
def _test_dataset():
import sflow.py as py
data = dataset_trainA(16)
with tf.feeding() as (sess, coord):
while not coord.should_stop():
py.plt.imshow(data.eval())
if not py.plot_pause():
break
x_train = _resize_mnist_image(x_train, size)
x_test = _resize_mnist_image(x_test, size)
np.savez(f,
x_train=x_train,
y_train=y_train,
x_test=x_test,
y_test=y_test)
return (x_train, y_train), (x_test, y_test)
else:
return _load_numpy(f)
def _load_data(size=(28, 28), folder=None):
folder = folder or _asset_folder()
f = os.path.join(folder, 'mnist.npz')
url = 'http://s3.amazonaws.com/img-datasets/mnist.npz'
py.download_if_not(url, f)
return _load_resized_local(folder, size)
if __name__ == '__main__':
data = dataset_train(16, (7, 7))
for d in tf.feeds(data):
py.imshow(d.image)
print(d.label)
if not py.plot_pause():
break