def get_shape(x, not_none=False, native=False):
"""Return the shape of a tensor, this function search for predefined shape
of `x` first, otherwise, return the theano shape
Warning: type returned will be different for
Theano backend (Theano tensor type) and TF backend (TF TensorShape).
Parameters
----------
x: theano or tensorflow variable, numpy.ndarray
variable for getting the shape
not_none : bool
if `not_none`=True, does not allow None in returned shape tuple.
Default value is False
native : bool
if True, return the native shape information returned by backend (i.e.
object shape not int shape)
"""
if native:
if get_backend() == 'theano':
return x.shape
elif get_backend() == 'tensorflow':
from tensorflow import shape
return shape(x)
else:
raise Exception("No support for native shape of backend: " +
get_backend())
# ====== get default shape ====== #
if hasattr(x, 'tag') and hasattr(x.tag, 'shape'):
shape = x.tag.shape
elif hasattr(x, 'get_shape'):
shape = tuple(x.get_shape().as_list())
elif hasattr(x, 'shape'):
shape = x.shape
else:
raise ValueError('Cannot get shape of variable: ' + str(x))
# ====== check tag shape ====== #
if not_none and isinstance(shape, (tuple, list)):
if get_backend() == 'theano':
x_shape = x.shape
else:
import tensorflow as tf
x_shape = tf.shape(x)
shape = tuple(
[x_shape[i] if s is None else s for i, s in enumerate(shape)])
return shape
def test_lstm(self):
W_in_to_ingate = random(28, 32) / 12
W_hid_to_ingate = random(32, 32) / 12
b_ingate = random(32) / 12
W_in_to_forgetgate = random(28, 32) / 12
W_hid_to_forgetgate = random(32, 32) / 12
b_forgetgate = random(32) / 12
W_in_to_cell = random(28, 32) / 12
W_hid_to_cell = random(32, 32) / 12
b_cell = random(32) / 12
W_in_to_outgate = random(28, 32) / 12
W_hid_to_outgate = random(32, 32) / 12
b_outgate = random(32) / 12
W_cell_to_ingate = random(32) / 12
W_cell_to_forgetgate = random(32) / 12
W_cell_to_outgate = random(32) / 12
cell_init = random(1, 32) / 12
hid_init = random(1, 32) / 12
# ====== pre-define parameters ====== #
x = random(12, 28, 28)
x_mask = np.random.randint(0, 2, size=(12, 28))
# x_mask = np.ones(shape=(12, 28))
# ====== odin ====== #
X = K.placeholder(shape=(None, 28, 28), name='X')
mask = K.placeholder(shape=(None, 28), name='mask', dtype='int32')
f = N.Sequence([
N.Merge([
N.Dense(32,
W_init=W_in_to_ingate,
b_init=b_ingate,
activation=K.linear),
N.Dense(32,
W_init=W_in_to_forgetgate,
b_init=b_forgetgate,
activation=K.linear),
N.Dense(32,
W_init=W_in_to_cell,
b_init=b_cell,
activation=K.linear),
N.Dense(32,
W_init=W_in_to_outgate,
b_init=b_outgate,
activation=K.linear)
],
merge_function=K.concatenate),
N.LSTM(32,
activation=K.tanh,
gate_activation=K.sigmoid,
W_hid_init=[
W_hid_to_ingate, W_hid_to_forgetgate, W_hid_to_cell,
W_hid_to_outgate
],
W_peepholes=[
W_cell_to_ingate, W_cell_to_forgetgate,
W_cell_to_outgate
],
input_mode='skip',
name='lstm')
])
y = f(X, h0=hid_init, c0=cell_init, mask=mask)
f = K.function([X, mask], y)
out1 = f(x, x_mask)
# ====== lasagne ====== #
if get_backend() == 'tensorflow':
self.assertTrue(repr(np.sum(out1))[:4] == repr(43.652363)[:4])
return
l = lasagne.layers.InputLayer(shape=(None, 28, 28))
l.input_var = X
l_mask = lasagne.layers.InputLayer(shape=(None, 28))
l_mask.input_var = mask
l = lasagne.layers.LSTMLayer(
l,
num_units=32,
ingate=lasagne.layers.Gate(
nonlinearity=lasagne.nonlinearities.sigmoid,
W_in=W_in_to_ingate,
W_hid=W_hid_to_ingate,
W_cell=W_cell_to_ingate,
b=b_ingate),
forgetgate=lasagne.layers.Gate(
nonlinearity=lasagne.nonlinearities.sigmoid,
W_in=W_in_to_forgetgate,
W_hid=W_hid_to_forgetgate,
W_cell=W_cell_to_forgetgate,
b=b_forgetgate),
cell=lasagne.layers.Gate(nonlinearity=lasagne.nonlinearities.tanh,
W_in=W_in_to_cell,
W_hid=W_hid_to_cell,
W_cell=None,
b=b_cell),
outgate=lasagne.layers.Gate(
nonlinearity=lasagne.nonlinearities.sigmoid,
W_in=W_in_to_outgate,
W_hid=W_hid_to_outgate,
W_cell=W_cell_to_outgate,
b=b_outgate),
nonlinearity=lasagne.nonlinearities.tanh,
cell_init=cell_init,
hid_init=hid_init,
mask_input=l_mask,
precompute_input=True,
backwards=False)
y = lasagne.layers.get_output(l)
f = K.function([X, mask], y)
out2 = f(x, x_mask)
# ====== test ====== #
self.assertAlmostEqual(np.sum(np.abs(out1 - out2)), 0.)
def test_gru(self):
# ====== pre-define parameters ====== #
W_in_to_updategate = random(28, 32)
W_hid_to_updategate = random(32, 32)
b_updategate = random(32)
#
W_in_to_resetgate = random(28, 32)
W_hid_to_resetgate = random(32, 32)
b_resetgate = random(32)
#
W_in_to_hidden_update = random(28, 32)
W_hid_to_hidden_update = random(32, 32)
b_hidden_update = random(32)
#
hid_init = random(1, 32)
x = random(12, 28, 28)
x_mask = np.random.randint(0, 2, size=(12, 28))
# ====== odin ====== #
X = K.placeholder(shape=(None, 28, 28), name='X')
mask = K.placeholder(shape=(None, 28), name='mask', dtype='int32')
f = N.Sequence([
N.Merge([
N.Dense(32,
W_init=W_in_to_updategate,
b_init=b_updategate,
activation=K.linear,
name='update'),
N.Dense(32,
W_init=W_in_to_resetgate,
b_init=b_resetgate,
activation=K.linear,
name='reset'),
N.Dense(32,
W_init=W_in_to_hidden_update,
b_init=b_hidden_update,
activation=K.linear,
name='hidden')
],
merge_function=K.concatenate),
N.GRU(32,
activation=K.tanh,
gate_activation=K.sigmoid,
W_hid_init=[
W_hid_to_updategate, W_hid_to_resetgate,
W_hid_to_hidden_update
],
input_mode='skip')
])
y = f(X, h0=hid_init, mask=mask)
f = K.function([X, mask], y)
out1 = f(x, x_mask)
# ====== lasagne ====== #
if get_backend() == 'tensorflow':
self.assertTrue(repr(np.sum(out1))[:8] == repr(2490.0596)[:8])
return
l = lasagne.layers.InputLayer(shape=(None, 28, 28))
l.input_var = X
l_mask = lasagne.layers.InputLayer(shape=(None, 28))
l_mask.input_var = mask
l = lasagne.layers.GRULayer(
l,
num_units=32,
updategate=lasagne.layers.Gate(
W_cell=None,
W_in=W_in_to_updategate,
W_hid=W_hid_to_updategate,
b=b_updategate,
nonlinearity=lasagne.nonlinearities.sigmoid),
resetgate=lasagne.layers.Gate(
W_cell=None,
W_in=W_in_to_resetgate,
W_hid=W_hid_to_resetgate,
b=b_resetgate,
nonlinearity=lasagne.nonlinearities.sigmoid),
hidden_update=lasagne.layers.Gate(
W_cell=None,
W_in=W_in_to_hidden_update,
W_hid=W_hid_to_hidden_update,
b=b_hidden_update,
nonlinearity=lasagne.nonlinearities.tanh),
hid_init=hid_init,
mask_input=l_mask,
precompute_input=True)
y = lasagne.layers.get_output(l)
f = K.function([X, mask], y)
out2 = f(x, x_mask)
# ====== test ====== #
self.assertAlmostEqual(np.sum(np.abs(out1 - out2)), 0.)