location = T.fmatrix()
scale = T.fmatrix()
alpha = T.fmatrix()
x = T.fvector()
batch_size = 1
num_channel = 1
patch_shape = (28, 28)
image_shape = (100, 100)
hyperparameters = {}
hyperparameters["cutoff"] = 3000
hyperparameters["batched_window"] = True
# tds, _ = get_cooking_streams(batch_size)
tds, _ = get_bmnist_streams(1)
res = tds.get_epoch_iterator(as_dict=True).next()['features']
# shape: 3 x 125 x 200
img = res[5, 0]
draw(img)
plt.savefig('img.png')
cropper = LocallySoftRectangularCropper(patch_shape=patch_shape,
hyperparameters=hyperparameters,
kernel=Gaussian())
patch1, matrix, dx2 = cropper.apply(
x.reshape((
batch_size,
num_channel,
) + image_shape), np.array([list(image_shape)]), location, scale, alpha)
cropper = LocallySoftRectangularCropper(
patch_shape=(28, 28),
hyperparameters={'cutoff': 3000, 'batched_window': True},
kernel=Gaussian())
down, W, dx2 = cropper.apply(
input_,
np.array([list((100, 100))]),
T.constant(
99 *
[[80, 70]]).astype('float32'),
T.constant(
99 *
[[0.28, 0.28]]).astype('float32'),
T.constant(
99 *
[[0.001, ] * 2]).astype('float32'))
f = theano.function([input_], [down, W, dx2])
data = get_bmnist_streams(99)[0].get_epoch_iterator().next()
res = f(data[0][0])
print np.min(res[2][0], axis=0)
print np.sum(res[1][0], axis=0)
plt.imshow(res[1][0], interpolation='nearest')
plt.savefig('w.png')
import ipdb; ipdb.set_trace()
# shape: B x C x X x Y
input_ = tensor5('features')
cropper = LocallySoftRectangularCropper(patch_shape=(28, 28),
hyperparameters={
'cutoff': 3000,
'batched_window': True
},
kernel=Gaussian())
down, W, dx2 = cropper.apply(
input_, np.array([list((100, 100))]),
T.constant(99 * [[80, 70]]).astype('float32'),
T.constant(99 * [[0.28, 0.28]]).astype('float32'),
T.constant(99 * [[
0.001,
] * 2]).astype('float32'))
f = theano.function([input_], [down, W, dx2])
data = get_bmnist_streams(99)[0].get_epoch_iterator().next()
res = f(data[0][0])
print np.min(res[2][0], axis=0)
print np.sum(res[1][0], axis=0)
plt.imshow(res[1][0], interpolation='nearest')
plt.savefig('w.png')
import ipdb
ipdb.set_trace()
interpolation='nearest')
location = T.fmatrix()
scale = T.fmatrix()
alpha = T.fmatrix()
x = T.fvector()
batch_size = 1
num_channel = 1
patch_shape = (28, 28)
image_shape = (100, 100)
hyperparameters = {}
hyperparameters["cutoff"] = 3000
hyperparameters["batched_window"] = True
# tds, _ = get_cooking_streams(batch_size)
tds, _ = get_bmnist_streams(1)
res = tds.get_epoch_iterator(as_dict=True).next()['features']
# shape: 3 x 125 x 200
img = res[5, 0]
draw(img)
plt.savefig('img.png')
cropper = LocallySoftRectangularCropper(
patch_shape=patch_shape,
hyperparameters=hyperparameters,
kernel=Gaussian())
patch1, matrix, dx2 = cropper.apply(
x.reshape((batch_size, num_channel,) + image_shape),
np.array([list(image_shape)]),
location,
