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提交 1f5087d2 authored 作者: amrithasuresh's avatar amrithasuresh
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Updated numpy as np

上级 f2eca59f
隐藏空白字符变更
内嵌 并排
正在显示 包含 353 行增加 和 353 行删除
theano/scan_module/tests/test_scan.py
浏览文件 @ 1f5087d2
......@@ -11,11 +11,11 @@ from collections import OrderedDict
import six.moves.cPickle as pickle
from six.moves import xrange
import numpy
import numpy as np
from nose.plugins.skip import SkipTest
from nose.tools import assert_raises
from nose.tools import raises
from numpy.testing import dec
from np.testing import dec
import theano
import theano.sandbox.rng_mrg
......@@ -89,7 +89,7 @@ class multiple_outputs_numeric_grad:
for i, p in enumerate(pt):
if ndarray_mask[i]:
pt[i] = numpy.array(p)
pt[i] = np.array(p)
_eps = type_eps[str(pt[i].dtype)]
if _eps > dtype_eps:
dtype_eps = _eps
......@@ -116,12 +116,12 @@ class multiple_outputs_numeric_grad:
t[pos] += _eps
t = t.reshape(pt[i].shape)
f_eps = f(*(pt[:i] + [t] + pt[i + 1:]))
_g.append(numpy.asarray((f_eps - f_x) / _eps))
gx.append(numpy.asarray(_g).reshape(pt[i].shape))
_g.append(np.asarray((f_eps - f_x) / _eps))
gx.append(np.asarray(_g).reshape(pt[i].shape))
else:
t = numpy.array(pt[i] + _eps)
t = np.array(pt[i] + _eps)
f_eps = f(*(pt[:i] + [t] + pt[i + 1:]))
gx.append(numpy.asarray((f_eps - f_x) / _eps))
gx.append(np.asarray((f_eps - f_x) / _eps))
self.gx = gx
@staticmethod
......@@ -137,8 +137,8 @@ class multiple_outputs_numeric_grad:
for i in xrange(len(_g_pt)):
if self.ndarray_mask[i]:
g_pt.append(_g_pt[i])
elif isinstance(_g_pt[i], numpy.ndarray):
assert numpy.all(_g_pt[i] == 0)
elif isinstance(_g_pt[i], np.ndarray):
assert np.all(_g_pt[i] == 0)
if len(g_pt) != len(self.gx):
raise ValueError('argument has wrong number of elements',
len(g_pt))
......@@ -149,12 +149,12 @@ class multiple_outputs_numeric_grad:
raise ValueError('argument element %i has wrong shape %s' %
(i, str((a.shape, b.shape))))
vv = multiple_outputs_numeric_grad.abs_rel_err(a, b)
errs.append(numpy.max(
errs.append(np.max(
multiple_outputs_numeric_grad.abs_rel_err(a, b)))
if numpy.all(numpy.isfinite(errs)):
return numpy.max(errs), numpy.argmax(errs)
if np.all(np.isfinite(errs)):
return np.max(errs), np.argmax(errs)
else:
return numpy.inf, 0
return np.inf, 0
# TODO: Test this function, and if it works,
......@@ -262,11 +262,11 @@ class T_Scan(unittest.TestCase):
if tmpdir is not None:
shutil.rmtree(tmpdir)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
state = rng.uniform()
steps = 5
numpy_values = numpy.array([state * (2 ** (k + 1)) for k
numpy_values = np.array([state * (2 ** (k + 1)) for k
in xrange(steps)])
theano_values = my_f(state, steps)
utt.assert_allclose(numpy_values, theano_values)
......@@ -300,7 +300,7 @@ class T_Scan(unittest.TestCase):
assert all(i.value is None for i in scan_node.op.fn.input_storage)
assert all(o.value is None for o in scan_node.op.fn.output_storage)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
state = rng.uniform()
steps = 5
......@@ -332,11 +332,11 @@ class T_Scan(unittest.TestCase):
updates=updates,
allow_input_downcast=True)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
state = rng.uniform()
steps = 5
numpy_values = numpy.array([state * (2 ** (k + 1)) for k
numpy_values = np.array([state * (2 ** (k + 1)) for k
in xrange(steps)])
theano_values = my_f(state, steps)
utt.assert_allclose(numpy_values, theano_values[0])
......@@ -370,10 +370,10 @@ class T_Scan(unittest.TestCase):
# This assertation fails if savemem optimization failed on scan
if theano.config.mode != "FAST_COMPILE":
assert nodes[0].op._scan_savemem_visited
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
my_f(rng.uniform(size=(3,)),
4,
numpy.int64([2, 2, 3]))
np.int64([2, 2, 3]))
@attr('slow')
def test_only_nonseq_inputs(self):
......@@ -388,9 +388,9 @@ class T_Scan(unittest.TestCase):
fun = theano.function([inp], [broadcasted_inp, gr])
# Execute the Theano function and compare outputs to the expected outputs
inputs = numpy.array([[1, 2], [3, 4]], dtype=theano.config.floatX)
expected_out1 = numpy.repeat(inputs[None], n_steps, axis=0)
expected_out2 = numpy.ones(inputs.shape, dtype="int8") * n_steps
inputs = np.array([[1, 2], [3, 4]], dtype=theano.config.floatX)
expected_out1 = np.repeat(inputs[None], n_steps, axis=0)
expected_out2 = np.ones(inputs.shape, dtype="int8") * n_steps
out1, out2 = fun(inputs)
utt.assert_allclose(out1, expected_out1)
......@@ -420,14 +420,14 @@ class T_Scan(unittest.TestCase):
updates=updates,
allow_input_downcast=True)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(4,), low=-5., high=5.)
v_x0 = rng.uniform()
W = rng.uniform()
W_in = rng.uniform()
# compute the output in numpy
v_out = numpy.zeros((4,))
v_out = np.zeros((4,))
v_out[0] = v_u[0] * W_in + v_x0 * W
for step in xrange(1, 4):
v_out[step] = v_u[step] * W_in + v_out[step - 1] * W
......@@ -437,7 +437,7 @@ class T_Scan(unittest.TestCase):
# simple rnn, one input, one state, weights for each; input/state
# are vectors, weights are scalars; using shared variables
def test_one_sequence_one_output_weights_shared(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
u = theano.tensor.vector('u')
x0 = theano.tensor.scalar('x0')
W_in = theano.shared(asarrayX(rng.uniform()), name='w_in')
......@@ -462,19 +462,19 @@ class T_Scan(unittest.TestCase):
v_u = rng.uniform(size=(4,), low=-5., high=5.)
v_x0 = rng.uniform()
# compute the output i numpy
v_out = numpy.zeros((4,))
v_out = np.zeros((4,))
v_out[0] = v_u[0] * W_in.get_value() + v_x0 * W.get_value()
for step in xrange(1, 4):
v_out[step] = (v_u[step] * W_in.get_value() +
v_out[step - 1] * W.get_value())
theano_values = f3(v_u, v_x0)
assert numpy.allclose(theano_values, v_out)
assert np.allclose(theano_values, v_out)
# some rnn with multiple outputs and multiple inputs; other
# dimension instead of scalars/vectors
def test_multiple_inputs_multiple_outputs(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(size=(2,), low=-5., high=5.))
vW = asarrayX(rng.uniform(size=(2, 2), low=-5., high=5.))
vWout = asarrayX(rng.uniform(size=(2,), low=-5., high=5.))
......@@ -511,15 +511,15 @@ class T_Scan(unittest.TestCase):
allow_input_downcast=True)
# compute the values in numpy
v_x = numpy.zeros((3, 2), dtype=theano.config.floatX)
v_y = numpy.zeros((3,), dtype=theano.config.floatX)
v_x[0] = (numpy.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 +
numpy.dot(v_x0, vW))
v_y[0] = numpy.dot(v_x0, vWout)
v_x = np.zeros((3, 2), dtype=theano.config.floatX)
v_y = np.zeros((3,), dtype=theano.config.floatX)
v_x[0] = (np.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 +
np.dot(v_x0, vW))
v_y[0] = np.dot(v_x0, vWout)
for i in xrange(1, 3):
v_x[i] = (numpy.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 +
numpy.dot(v_x[i - 1], vW))
v_y[i] = numpy.dot(v_x[i - 1], vWout)
v_x[i] = (np.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 +
np.dot(v_x[i - 1], vW))
v_y[i] = np.dot(v_x[i - 1], vWout)
(theano_x, theano_y) = f4(v_u1, v_u2, v_x0, v_y0, vW_in1)
utt.assert_allclose(theano_x, v_x)
......@@ -527,7 +527,7 @@ class T_Scan(unittest.TestCase):
def test_multiple_outs_taps(self):
l = 5
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(size=(2,), low=-.2, high=.2))
vW = asarrayX(rng.uniform(size=(2, 2), low=-.2, high=.2))
vWout = asarrayX(rng.uniform(size=(2,), low=-.2, high=.2))
......@@ -579,40 +579,40 @@ class T_Scan(unittest.TestCase):
v_y0,
vW_in1)
ny0 = numpy.zeros((5, 2))
ny1 = numpy.zeros((5,))
ny2 = numpy.zeros((5, 2))
ny0[0] = numpy.dot(v_u1[0], vW_in1) + \
(v_u2[1] + v_u2[0] * v_u2[2]) * vW_in2 + numpy.dot(v_x0, vW)
ny0 = np.zeros((5, 2))
ny1 = np.zeros((5,))
ny2 = np.zeros((5, 2))
ny0[0] = np.dot(v_u1[0], vW_in1) + \
(v_u2[1] + v_u2[0] * v_u2[2]) * vW_in2 + np.dot(v_x0, vW)
ny1[0] = (v_y0[2] + v_y0[0]) * numpy.dot(v_x0, vWout)
ny2[0] = numpy.dot(v_u1[0], vW_in1)
ny1[0] = (v_y0[2] + v_y0[0]) * np.dot(v_x0, vWout)
ny2[0] = np.dot(v_u1[0], vW_in1)
ny0[1] = numpy.dot(v_u1[1], vW_in1) + \
(v_u2[2] + v_u2[1] * v_u2[3]) * vW_in2 + numpy.dot(ny0[0], vW)
ny0[1] = np.dot(v_u1[1], vW_in1) + \
(v_u2[2] + v_u2[1] * v_u2[3]) * vW_in2 + np.dot(ny0[0], vW)
ny1[1] = (ny1[0] + v_y0[1]) * numpy.dot(ny0[0], vWout)
ny2[1] = numpy.dot(v_u1[1], vW_in1)
ny1[1] = (ny1[0] + v_y0[1]) * np.dot(ny0[0], vWout)
ny2[1] = np.dot(v_u1[1], vW_in1)
ny0[2] = numpy.dot(v_u1[2], vW_in1) + \
ny0[2] = np.dot(v_u1[2], vW_in1) + \
(v_u2[3] + v_u2[2] * v_u2[4]) * vW_in2 + \
numpy.dot(ny0[1], vW)
ny1[2] = (ny1[1] + v_y0[2]) * numpy.dot(ny0[1], vWout)
ny2[2] = numpy.dot(v_u1[2], vW_in1)
np.dot(ny0[1], vW)
ny1[2] = (ny1[1] + v_y0[2]) * np.dot(ny0[1], vWout)
ny2[2] = np.dot(v_u1[2], vW_in1)
ny0[3] = numpy.dot(v_u1[3], vW_in1) + \
ny0[3] = np.dot(v_u1[3], vW_in1) + \
(v_u2[4] + v_u2[3] * v_u2[5]) * vW_in2 + \
numpy.dot(ny0[2], vW)
np.dot(ny0[2], vW)
ny1[3] = (ny1[2] + ny1[0]) * numpy.dot(ny0[2], vWout)
ny2[3] = numpy.dot(v_u1[3], vW_in1)
ny1[3] = (ny1[2] + ny1[0]) * np.dot(ny0[2], vWout)
ny2[3] = np.dot(v_u1[3], vW_in1)
ny0[4] = numpy.dot(v_u1[4], vW_in1) + \
ny0[4] = np.dot(v_u1[4], vW_in1) + \
(v_u2[5] + v_u2[4] * v_u2[6]) * vW_in2 + \
numpy.dot(ny0[3], vW)
np.dot(ny0[3], vW)
ny1[4] = (ny1[3] + ny1[1]) * numpy.dot(ny0[3], vWout)
ny2[4] = numpy.dot(v_u1[4], vW_in1)
ny1[4] = (ny1[3] + ny1[1]) * np.dot(ny0[3], vWout)
ny2[4] = np.dot(v_u1[4], vW_in1)
def test_using_taps_sequence(self):
# this test refers to a bug reported by Nicolas
......@@ -621,9 +621,9 @@ class T_Scan(unittest.TestCase):
y, updates = theano.scan(lambda x: [x],
sequences=dict(input=x, taps=[-1]),
outputs_info=[None])
inp = numpy.arange(5).astype('float64')
inp = np.arange(5).astype('float64')
rval = theano.function([x], y, updates=updates)(inp)
assert numpy.all(rval == inp[:-1])
assert np.all(rval == inp[:-1])
def test_using_negative_taps_sequence(self):
# This test refers to a bug reported on github on May 22 2015 by
......@@ -636,7 +636,7 @@ class T_Scan(unittest.TestCase):
f = theano.function([x], res, updates = upd)
output = f([1, 2, 3, 4, 5])
expected_output = numpy.array([1, 2, 3], dtype="float32")
expected_output = np.array([1, 2, 3], dtype="float32")
utt.assert_allclose(output, expected_output)
def test_connection_pattern(self):
......@@ -649,8 +649,8 @@ class T_Scan(unittest.TestCase):
def fn(a_m2, a_m1, b_m2, b_m1):
return a_m1, b_m1
a0 = theano.shared(numpy.arange(2))
b0 = theano.shared(numpy.arange(2))
a0 = theano.shared(np.arange(2))
b0 = theano.shared(np.arange(2))
(a, b), _ = theano.scan(fn,
outputs_info=[{'initial': a0, 'taps': [-2, -1]},
......@@ -741,7 +741,7 @@ class T_Scan(unittest.TestCase):
# Call verify_grad to ensure the correctness of the second gradients
floatX = theano.config.floatX
inputs_test_values = [numpy.random.random((3)).astype(floatX)]
inputs_test_values = [np.random.random((3)).astype(floatX)]
theano.tests.unittest_tools.verify_grad(get_sum_of_grad,
inputs_test_values)
......@@ -768,8 +768,8 @@ class T_Scan(unittest.TestCase):
# Call verify_grad to ensure the correctness of the second gradients
floatX = theano.config.floatX
inputs_test_values = [numpy.random.random((2, 3)).astype(floatX),
numpy.random.random((3)).astype(floatX)]
inputs_test_values = [np.random.random((2, 3)).astype(floatX),
np.random.random((3)).astype(floatX)]
theano.tests.unittest_tools.verify_grad(get_sum_of_grad,
inputs_test_values)
......@@ -781,7 +781,7 @@ class T_Scan(unittest.TestCase):
# forward pass
W = theano.shared(
numpy.random.randn(2, 2).astype('float32'),
np.random.randn(2, 2).astype('float32'),
name="W", borrow=True)
def forward_scanner(x_t):
......@@ -807,7 +807,7 @@ class T_Scan(unittest.TestCase):
# vectors, weights are scalars; using shared variables and past
# taps (sequences and outputs)
def test_using_taps_input_output(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW = asarrayX(rng.uniform())
vW_in = asarrayX(rng.uniform())
vu = asarrayX(rng.uniform(size=(4,), low=-5., high=5.))
......@@ -843,7 +843,7 @@ class T_Scan(unittest.TestCase):
# in scan) which might seem strange, but then again why not use
# v_0[t] instead of v_0[t-2] in a real application ??
# also vx0[0] corresponds to vx0[-2], vx0[1] to vx0[-1]
numpy_out = numpy.zeros((2,))
numpy_out = np.zeros((2,))
numpy_out[0] = vu[0] * vW_in + vx0[1] * vW + vx0[0]
numpy_out[1] = vu[1] * vW_in + numpy_out[0] * vW + vx0[1]
utt.assert_allclose(numpy_out, theano_out)
......@@ -852,7 +852,7 @@ class T_Scan(unittest.TestCase):
# vectors, weights are scalars; using shared variables and past
# taps (sequences and outputs) and future taps for sequences
def test_past_future_taps_shared(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW = asarrayX(rng.uniform())
vW_in = asarrayX(rng.uniform())
vu = asarrayX(rng.uniform(size=(6,), low=-5., high=5.))
......@@ -880,7 +880,7 @@ class T_Scan(unittest.TestCase):
allow_input_downcast=True)
theano_out = f8(vu, vx0)
# compute output in numpy
numpy_out = numpy.zeros(2)
numpy_out = np.zeros(2)
# think of vu[0] as vu[-2], vu[4] as vu[2]
# and vx0[0] as vx0[-2], vx0[1] as vx0[-1]
numpy_out[0] = (vu[0] + vu[4]) * vW_in + vx0[1] * vW + vx0[0]
......@@ -889,9 +889,9 @@ class T_Scan(unittest.TestCase):
# simple rnn ; compute inplace version 1
def test_inplace1(self):
rng = numpy.random.RandomState(utt.fetch_seed())
vW = asarrayX(numpy.random.uniform())
vW_in = asarrayX(numpy.random.uniform())
rng = np.random.RandomState(utt.fetch_seed())
vW = asarrayX(np.random.uniform())
vW_in = asarrayX(np.random.uniform())
vu0 = asarrayX(rng.uniform(size=(3,), low=-5., high=5.))
vu1 = asarrayX(rng.uniform(size=(3,), low=-5., high=5.))
vu2 = asarrayX(rng.uniform(size=(3,), low=-5., high=5.))
......@@ -934,8 +934,8 @@ class T_Scan(unittest.TestCase):
assert 0 in scan_node[0].op.destroy_map.keys()
assert 1 in scan_node[0].op.destroy_map.keys()
# compute output in numpy
numpy_x0 = numpy.zeros((3,))
numpy_x1 = numpy.zeros((3,))
numpy_x0 = np.zeros((3,))
numpy_x1 = np.zeros((3,))
numpy_x0[0] = vu0[0] * vW_in + vx0 * vW + vu1[0] * vu2[0]
numpy_x1[0] = vu0[0] * vW_in + vx1 * vW + vu1[0] + vu2[0]
for i in xrange(1, 3):
......@@ -953,9 +953,9 @@ class T_Scan(unittest.TestCase):
# simple rnn ; compute inplace version 2
def test_inplace2(self):
rng = numpy.random.RandomState(utt.fetch_seed())
vW = asarrayX(numpy.random.uniform())
vW_in = asarrayX(numpy.random.uniform())
rng = np.random.RandomState(utt.fetch_seed())
vW = asarrayX(np.random.uniform())
vW_in = asarrayX(np.random.uniform())
vu0 = asarrayX(rng.uniform(size=(3,), low=-5., high=5.))
vu1 = asarrayX(rng.uniform(size=(4,), low=-5., high=5.))
vu2 = asarrayX(rng.uniform(size=(5,), low=-5., high=5.))
......@@ -1006,8 +1006,8 @@ class T_Scan(unittest.TestCase):
assert 0 in scan_node[0].op.destroy_map.keys()
assert 1 in scan_node[0].op.destroy_map.keys()
# compute output in numpy
numpy_x0 = numpy.zeros((3,))
numpy_x1 = numpy.zeros((3,))
numpy_x0 = np.zeros((3,))
numpy_x1 = np.zeros((3,))
numpy_x0[0] = vu0[0] * vW_in + vx0 * vW + vu1[0] * vu1[1]
numpy_x1[0] = vu0[0] * vW_in + vx1 * vW + vu2[0] + vu2[1] + vu2[2]
for i in xrange(1, 3):
......@@ -1024,7 +1024,7 @@ class T_Scan(unittest.TestCase):
utt.assert_allclose(theano_x1, numpy_x1)
def test_inplace3(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vx0 = asarrayX(rng.uniform())
vx1 = asarrayX(rng.uniform())
......@@ -1035,7 +1035,7 @@ class T_Scan(unittest.TestCase):
[],
[x0, x1],
n_steps=3)
x0 = asarrayX(numpy.zeros((3,)))
x0 = asarrayX(np.zeros((3,)))
x0[0] = vx0
x0 = theano.tensor.constant(x0)
to_replace = outputs[0].owner.inputs[0].owner.inputs[1]
......@@ -1053,7 +1053,7 @@ class T_Scan(unittest.TestCase):
# Shared variable with updates
def test_shared_arguments_with_updates(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW1 = asarrayX(rng.rand(2, 3))
vW2 = asarrayX(rng.rand(3, 2))
......@@ -1128,22 +1128,22 @@ class T_Scan(unittest.TestCase):
theano_y0, theano_y1, theano_y2 = allstuff
# do things in numpy
numpy_y0 = numpy.zeros((6, 2))
numpy_y1 = numpy.zeros((4, 2))
numpy_y2 = numpy.zeros((3, 3))
numpy_y0 = np.zeros((6, 2))
numpy_y1 = np.zeros((4, 2))
numpy_y2 = np.zeros((3, 3))
numpy_y0[:3] = vy0
numpy_y1[0] = vy1
numpy_W1 = vW1.copy()
numpy_W2 = vW2.copy()
for idx in xrange(3):
numpy_y0[idx + 3] = numpy.dot(numpy.dot(vu1[idx, :], numpy_W1),
numpy_y0[idx + 3] = np.dot(np.dot(vu1[idx, :], numpy_W1),
numpy_W2) + \
0.1 * numpy_y0[idx + 2] + \
0.33 * numpy_y0[idx + 1] + \
0.17 * numpy_y0[idx]
numpy_y1[idx + 1] = (numpy.dot(vu2[idx, :], numpy_W2) +
numpy_y1[idx + 1] = (np.dot(vu2[idx, :], numpy_W2) +
numpy_y1[idx])
numpy_y2[idx] = numpy.dot(vu1[idx, :], numpy_W1)
numpy_y2[idx] = np.dot(vu1[idx, :], numpy_W1)
numpy_W1 = numpy_W1 + .1
numpy_W2 = numpy_W2 + .05
......@@ -1174,7 +1174,7 @@ class T_Scan(unittest.TestCase):
f = theano.function([c, x, y], [gX, gY],
allow_input_downcast=True)
# Check for runtime errors
f(numpy.int32(0), numpy.float32(1.), numpy.float32(.5))
f(np.int32(0), np.float32(1.), np.float32(.5))
def test_simple_shared_mrg_random(self):
theano_rng = theano.sandbox.rng_mrg.MRG_RandomStreams(utt.fetch_seed())
......@@ -1211,10 +1211,10 @@ class T_Scan(unittest.TestCase):
updates=updates,
allow_input_downcast=True)
rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2 ** 30)
rng = numpy.random.RandomState(int(rng_seed)) # int() is for 32bit
rng_seed = np.random.RandomState(utt.fetch_seed()).randint(2 ** 30)
rng = np.random.RandomState(int(rng_seed)) # int() is for 32bit
numpy_v = numpy.zeros((10, 2))
numpy_v = np.zeros((10, 2))
for i in xrange(10):
numpy_v[i] = rng.uniform(-1, 1, size=(2,))
......@@ -1224,12 +1224,12 @@ class T_Scan(unittest.TestCase):
utt.assert_allclose(theano_v, numpy_v[5:, :])
def test_gibbs_chain(self):
rng = numpy.random.RandomState(utt.fetch_seed())
v_W = numpy.array(rng.rand(20, 30) - .5, dtype='float32')
v_vsample = numpy.array(rng.binomial(1, .5, size=(3, 20),),
rng = np.random.RandomState(utt.fetch_seed())
v_W = np.array(rng.rand(20, 30) - .5, dtype='float32')
v_vsample = np.array(rng.binomial(1, .5, size=(3, 20),),
dtype='float32')
v_bvis = numpy.array(rng.rand(20) - .5, dtype='float32')
v_bhid = numpy.array(rng.rand(30) - .5, dtype='float32')
v_bvis = np.array(rng.rand(20) - .5, dtype='float32')
v_bhid = np.array(rng.rand(30) - .5, dtype='float32')
W = theano.shared(v_W, 'vW')
bhid = theano.shared(v_bhid, 'vbhid')
bvis = theano.shared(v_bvis, 'vbvis')
......@@ -1261,24 +1261,24 @@ class T_Scan(unittest.TestCase):
updates=updates,
allow_input_downcast=True)
_rng = numpy.random.RandomState(utt.fetch_seed())
_rng = np.random.RandomState(utt.fetch_seed())
rng_seed = _rng.randint(2 ** 30)
nrng1 = numpy.random.RandomState(int(rng_seed)) # int() is for 32bit
nrng1 = np.random.RandomState(int(rng_seed)) # int() is for 32bit
rng_seed = _rng.randint(2 ** 30)
nrng2 = numpy.random.RandomState(int(rng_seed)) # int() is for 32bit
nrng2 = np.random.RandomState(int(rng_seed)) # int() is for 32bit
def numpy_implementation(vsample):
for idx in range(10):
hmean = 1. / (1. + numpy.exp(-(numpy.dot(vsample, v_W) +\
hmean = 1. / (1. + np.exp(-(np.dot(vsample, v_W) +\
v_bhid)))
hsample = numpy.array(nrng1.binomial(1,
hsample = np.array(nrng1.binomial(1,
hmean,
size=hmean.shape),
dtype='float32')
vmean = 1. / (1. + numpy.exp(-(numpy.dot(hsample, v_W.T) +\
vmean = 1. / (1. + np.exp(-(np.dot(hsample, v_W.T) +\
v_bvis)))
vsample = numpy.array(nrng2.binomial(1,
vsample = np.array(nrng2.binomial(1,
vmean,
size=vmean.shape),
dtype='float32')
......@@ -1290,7 +1290,7 @@ class T_Scan(unittest.TestCase):
utt.assert_allclose(t_result, n_result)
def test_only_shared_no_input_no_output(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_state = asarrayX(rng.uniform())
state = theano.shared(v_state, 'vstate')
......@@ -1331,7 +1331,7 @@ class T_Scan(unittest.TestCase):
outputs,
updates=updates,
allow_input_downcast=True)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(5,), low=-5., high=5.)
numpy_result = v_u + 3
......@@ -1352,7 +1352,7 @@ class T_Scan(unittest.TestCase):
updates=abs_updates,
allow_input_downcast=True)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vals = rng.uniform(size=(10,), low=-5., high=5.)
abs_vals = abs(vals)
theano_vals = f(vals)
......@@ -1380,14 +1380,14 @@ class T_Scan(unittest.TestCase):
updates=updates,
allow_input_downcast=True)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(4,), low=-5., high=5.)
v_x0 = rng.uniform()
W = rng.uniform()
W_in = rng.uniform()
# compute the output in numpy
v_out = numpy.zeros((4,))
v_out = np.zeros((4,))
v_out[0] = v_u[3] * W_in + v_x0 * W
for step in xrange(1, 4):
v_out[step] = v_u[3 - step] * W_in + v_out[step - 1] * W
......@@ -1404,9 +1404,9 @@ class T_Scan(unittest.TestCase):
result,
updates=updates,
allow_input_downcast=True)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_v = rng.uniform(size=(5,), low=-5., high=5.)
assert abs(numpy.sum(v_v) - f(v_v, 0.)) < 1e-3
assert abs(np.sum(v_v) - f(v_v, 0.)) < 1e-3
def test_grad_one_output(self):
def f_rnn(u_t, x_tm1, W_in, W):
......@@ -1440,12 +1440,12 @@ class T_Scan(unittest.TestCase):
allow_input_downcast=True)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
v_u = numpy.array(rng.uniform(size=(10,), low=-.5, high=.5),
rng = np.random.RandomState(utt.fetch_seed())
v_u = np.array(rng.uniform(size=(10,), low=-.5, high=.5),
dtype=theano.config.floatX)
v_x0 = numpy.array(rng.uniform(), dtype=theano.config.floatX)
W = numpy.array(rng.uniform(), dtype=theano.config.floatX)
W_in = numpy.array(rng.uniform(), dtype=theano.config.floatX)
v_x0 = np.array(rng.uniform(), dtype=theano.config.floatX)
W = np.array(rng.uniform(), dtype=theano.config.floatX)
W_in = np.array(rng.uniform(), dtype=theano.config.floatX)
analytic_grad = grad_fn(v_u, v_x0, W_in, W)
num_grad = multiple_outputs_numeric_grad(
......@@ -1459,7 +1459,7 @@ class T_Scan(unittest.TestCase):
num_grad.gx[max_err_pos]))
def test_grad_multiple_outs(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(size=(2,), low=-.1, high=.1))
vW = asarrayX(rng.uniform(size=(2, 2), low=-.1, high=.1))
vWout = asarrayX(rng.uniform(size=(2,), low=-.1, high=.1))
......@@ -1524,7 +1524,7 @@ class T_Scan(unittest.TestCase):
@attr('slow')
def test_grad_multiple_outs_taps(self):
l = 5
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(size=(2,), low=-.2, high=.2))
vW = asarrayX(rng.uniform(size=(2, 2), low=-.2, high=.2))
vWout = asarrayX(rng.uniform(size=(2,), low=-.2, high=.2))
......@@ -1618,7 +1618,7 @@ class T_Scan(unittest.TestCase):
@attr('slow')
def test_grad_multiple_outs_taps_backwards(self):
l = 5
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(size=(2,), low=-.2, high=.2))
vW = asarrayX(rng.uniform(size=(2, 2), low=-.2, high=.2))
vWout = asarrayX(rng.uniform(size=(2,), low=-.2, high=.2))
......@@ -1685,10 +1685,10 @@ class T_Scan(unittest.TestCase):
num_grad.gx[max_err_pos]))
def test_grad_multiple_outs_some_uncomputable(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in = asarrayX(rng.uniform(size=(2, 2), low=-3., high=3.))
v_u = asarrayX(rng.uniform(size=(5, 2), low=-3., high=3.))
v_u2 = numpy.array([1, 3, 4, 6, 8], dtype='int32')
v_u2 = np.array([1, 3, 4, 6, 8], dtype='int32')
v_x0 = asarrayX(rng.uniform(size=(2,), low=-3., high=3.))
W_in = theano.tensor.matrix('win')
......@@ -1730,9 +1730,9 @@ class T_Scan(unittest.TestCase):
def reset_rng_fn(fn, *args):
for idx, arg in enumerate(fn.maker.expanded_inputs):
if (arg.value and type(arg.value.data) == \
type(numpy.random.RandomState(123))):
type(np.random.RandomState(123))):
obj = fn.maker.expanded_inputs[idx].value
obj.data = numpy.random.RandomState(123)
obj.data = np.random.RandomState(123)
fn.maker.expanded_inputs[idx].value = obj
return fn(*args)
......@@ -1764,7 +1764,7 @@ class T_Scan(unittest.TestCase):
assert(result == expected_result)
def test_grad_multiple_outs_some_truncate(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in = asarrayX(rng.uniform(size=(2, 2), low=-.1, high=.1))
v_u = asarrayX(rng.uniform(size=(5, 2), low=-.1, high=.1))
v_x0 = asarrayX(rng.uniform(size=(2,), low=-.1, high=.1))
......@@ -1807,9 +1807,9 @@ class T_Scan(unittest.TestCase):
def reset_rng_fn(fn, *args):
for idx, arg in enumerate(fn.maker.expanded_inputs):
if (arg.value and
isinstance(arg.value.data, numpy.random.RandomState)):
isinstance(arg.value.data, np.random.RandomState)):
obj = fn.maker.expanded_inputs[idx].value
obj.data = numpy.random.RandomState(123)
obj.data = np.random.RandomState(123)
fn.maker.expanded_inputs[idx].value = obj
out = fn(*args)
return out
......@@ -1819,7 +1819,7 @@ class T_Scan(unittest.TestCase):
num_grad = multiple_outputs_numeric_grad(
reset_rng_cost_fn, [v_u, v_x0, vW_in])
analytic_grad = reset_rng_grad_fn(v_u, v_x0, vW_in)
utt.assert_allclose(analytic_grad[0][:2], numpy.zeros((2, 2)))
utt.assert_allclose(analytic_grad[0][:2], np.zeros((2, 2)))
def test_grad_multiple_outs_some_disconnected(self):
final_cost = self._grad_mout_helper(100, mode_nodebug)
......@@ -1833,7 +1833,7 @@ class T_Scan(unittest.TestCase):
def _grad_mout_helper(self, n_iters, mode):
# Created on Tue Oct 07 13:28:51 2014
# @author: vaneetke
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
n_hid = 3
n_in = 1
n_out = 1
......@@ -1897,10 +1897,10 @@ class T_Scan(unittest.TestCase):
mode=mode)
# artificial data
x_v = numpy.arange(0., 10.49, 0.21, dtype=theano.config.floatX)
x_v = np.arange(0., 10.49, 0.21, dtype=theano.config.floatX)
x_v = x_v.reshape(len(x_v), 1)
s_v = numpy.sin(x_v)
t_v = numpy.roll(s_v, -1)[:-1]
s_v = np.sin(x_v)
t_v = np.roll(s_v, -1)[:-1]
s_v = s_v[:-1]
for i in xrange(n_iters):
cost = learn_rnn_fn(s_v, t_v)
......@@ -1919,14 +1919,14 @@ class T_Scan(unittest.TestCase):
updates=updates,
allow_input_downcast=True)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
nx = rng.uniform(size=(10, 10))
ny1, nz1 = f(nx)
ny2, nz2 = f(nx)
utt.assert_allclose([ny1, ny1], nz1)
utt.assert_allclose([ny2, ny2], nz2)
assert not numpy.allclose(ny1, ny2)
assert not np.allclose(ny1, ny2)
def test_grad_of_shared(self):
x1 = theano.shared(3.)
......@@ -1942,7 +1942,7 @@ class T_Scan(unittest.TestCase):
def test_computing_gradient(self):
x1 = theano.tensor.scalar('x1')
x2 = theano.shared(numpy.array([1, 2, 3, 4, 5]), name='x2')
x2 = theano.shared(np.array([1, 2, 3, 4, 5]), name='x2')
K = x2 * x1
out, updates = theano.scan(lambda i, v: theano.tensor.grad(K[i], v),
......@@ -1950,10 +1950,10 @@ class T_Scan(unittest.TestCase):
non_sequences=x1)
f = theano.function([x1], out, allow_input_downcast=True)
assert numpy.all(f(3.) != 0.)
assert np.all(f(3.) != 0.)
def test_shared_updates(self):
X = theano.shared(numpy.array(1))
X = theano.shared(np.array(1))
out, updates = theano.scan(
lambda: OrderedDict([(X, (X + 1))]),
......@@ -1967,8 +1967,8 @@ class T_Scan(unittest.TestCase):
assert X.get_value() == 11
def test_memory_aliasing_updates(self):
x = theano.shared(numpy.array(1))
y = theano.shared(numpy.array(1))
x = theano.shared(np.array(1))
y = theano.shared(np.array(1))
out, updates = theano.scan(
lambda: OrderedDict([(x, x + 1), (y, x)]),
......@@ -1979,7 +1979,7 @@ class T_Scan(unittest.TestCase):
f = theano.function([], [], updates=updates)
f()
assert not numpy.may_share_memory(x.container.storage[0],
assert not np.may_share_memory(x.container.storage[0],
y.container.storage[0])
assert x.get_value() != y.get_value()
......@@ -1998,7 +1998,7 @@ class T_Scan(unittest.TestCase):
"""
a = theano.tensor.vector()
init_a = theano.tensor.vector()
b = theano.shared(numpy.random.rand(5, 4))
b = theano.shared(np.random.rand(5, 4))
def inner_func(a):
return a + 1, OrderedDict([(b, 2 * b)])
......@@ -2032,9 +2032,9 @@ class T_Scan(unittest.TestCase):
non_sequences=[gy, x])
f = theano.function([x, A], hy, allow_input_downcast=True)
vx = numpy.array([1., 1.], dtype=theano.config.floatX)
vA = numpy.array([[1., 1.], [1., 0.]], dtype=theano.config.floatX)
vR = numpy.array([[3.6, 1.8], [1.8, 0.9]], dtype=theano.config.floatX)
vx = np.array([1., 1.], dtype=theano.config.floatX)
vA = np.array([[1., 1.], [1., 0.]], dtype=theano.config.floatX)
vR = np.array([[3.6, 1.8], [1.8, 0.9]], dtype=theano.config.floatX)
out = f(vx, vA)
utt.assert_allclose(out, vR)
......@@ -2157,7 +2157,7 @@ class T_Scan(unittest.TestCase):
# some rnn with multiple outputs and multiple inputs; other
# dimension instead of scalars/vectors
def test_reordering(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(size=(2,), low=-5., high=5.))
vW = asarrayX(rng.uniform(size=(2, 2), low=-5., high=5.))
vWout = asarrayX(rng.uniform(size=(2,), low=-5., high=5.))
......@@ -2200,15 +2200,15 @@ class T_Scan(unittest.TestCase):
allow_input_downcast=True)
# compute the values in numpy
v_x = numpy.zeros((3, 2), dtype=theano.config.floatX)
v_y = numpy.zeros((3,), dtype=theano.config.floatX)
v_x[0] = numpy.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 + \
numpy.dot(v_x0, vW)
v_y[0] = numpy.dot(v_x0, vWout) + v_y0[2]
v_x = np.zeros((3, 2), dtype=theano.config.floatX)
v_y = np.zeros((3,), dtype=theano.config.floatX)
v_x[0] = np.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 + \
np.dot(v_x0, vW)
v_y[0] = np.dot(v_x0, vWout) + v_y0[2]
for i in xrange(1, 3):
v_x[i] = numpy.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 + \
numpy.dot(v_x[i - 1], vW)
v_y[i] = numpy.dot(v_x[i - 1], vWout) + v_y[i - 1]
v_x[i] = np.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 + \
np.dot(v_x[i - 1], vW)
v_y[i] = np.dot(v_x[i - 1], vWout) + v_y[i - 1]
(theano_dump1, theano_dump2, theano_x, theano_y) = f4(v_u1,
v_u2,
......@@ -2247,7 +2247,7 @@ class T_Scan(unittest.TestCase):
allow_input_downcast=True)
def test_save_mem(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(size=(2,), low=-5., high=5.))
vW = asarrayX(rng.uniform(size=(2, 2), low=-5., high=5.))
vWout = asarrayX(rng.uniform(size=(2,), low=-5., high=5.))
......@@ -2288,16 +2288,16 @@ class T_Scan(unittest.TestCase):
allow_input_downcast=True)
# compute the values in numpy
v_x = numpy.zeros((8, 2), dtype=theano.config.floatX)
v_y = numpy.zeros((8,), dtype=theano.config.floatX)
v_x[0] = numpy.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 + \
numpy.dot(v_x0, vW)
v_y[0] = numpy.dot(v_x0, vWout) + v_y0[2]
v_x = np.zeros((8, 2), dtype=theano.config.floatX)
v_y = np.zeros((8,), dtype=theano.config.floatX)
v_x[0] = np.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 + \
np.dot(v_x0, vW)
v_y[0] = np.dot(v_x0, vWout) + v_y0[2]
for i in xrange(1, 8):
v_x[i] = numpy.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 + \
numpy.dot(v_x[i - 1], vW)
v_y[i] = numpy.dot(v_x[i - 1], vWout) + v_y[i - 1]
v_x[i] = np.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 + \
np.dot(v_x[i - 1], vW)
v_y[i] = np.dot(v_x[i - 1], vWout) + v_y[i - 1]
(theano_dump, theano_x, theano_y) = f4(v_u1, v_u2, v_x0, v_y0, vW_in1)
......@@ -2321,24 +2321,24 @@ class T_Scan(unittest.TestCase):
sh = expr.shape[0]
v1 = theano.shared(numpy.ones(5, dtype=theano.config.floatX))
v2 = theano.shared(numpy.ones((5, 5), dtype=theano.config.floatX))
v1 = theano.shared(np.ones(5, dtype=theano.config.floatX))
v2 = theano.shared(np.ones((5, 5), dtype=theano.config.floatX))
shapef = theano.function([W],
expr,
givens=OrderedDict([(initial, v1),
(inpt, v2)]))
# First execution to cache n_steps
shapef(numpy.ones((5, 5), dtype=theano.config.floatX))
shapef(np.ones((5, 5), dtype=theano.config.floatX))
cost = expr.sum()
d_cost_wrt_W = tensor.grad(cost, [W])
f = theano.function(
[W, inpt], d_cost_wrt_W,
givens=OrderedDict([(initial, theano.shared(numpy.zeros(5)))]))
givens=OrderedDict([(initial, theano.shared(np.zeros(5)))]))
rval = numpy.asarray([[5187989] * 5] * 5, dtype=theano.config.floatX)
arg1 = numpy.ones((5, 5), dtype=theano.config.floatX)
arg2 = numpy.ones((10, 5), dtype=theano.config.floatX)
rval = np.asarray([[5187989] * 5] * 5, dtype=theano.config.floatX)
arg1 = np.ones((5, 5), dtype=theano.config.floatX)
arg2 = np.ones((10, 5), dtype=theano.config.floatX)
utt.assert_allclose(f(arg1, arg2), rval)
def test_save_mem_reduced_number_of_steps(self):
......@@ -2372,7 +2372,7 @@ class T_Scan(unittest.TestCase):
updates=updates,
allow_input_downcast=True)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(20,), low=-5., high=5.)
# compute the output in numpy
......@@ -2428,7 +2428,7 @@ class T_Scan(unittest.TestCase):
allow_input_downcast=True)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(20,), low=-5., high=5.)
# compute the output in numpy
......@@ -2474,7 +2474,7 @@ class T_Scan(unittest.TestCase):
floatX = theano.config.floatX
init_value = 5.0
seq_value = numpy.arange(4, dtype=floatX)
seq_value = np.arange(4, dtype=floatX)
output1, output2 = fct(init_value, seq_value)
expected_output1 = [init_value]
......@@ -2509,13 +2509,13 @@ class T_Scan(unittest.TestCase):
[out1_direct, out2_direct])
# Test that the function returns valid outputs
x_val = numpy.arange(0, 4)[:, None]
seq_val = numpy.arange(4, 8)[:, None]
x_val = np.arange(0, 4)[:, None]
seq_val = np.arange(4, 8)[:, None]
out1, out2 = fct(x_val, seq_val)
expected_out1 = numpy.zeros((5, 4, 1))
expected_out2 = numpy.zeros((5, 4, 1))
expected_out1 = np.zeros((5, 4, 1))
expected_out2 = np.zeros((5, 4, 1))
for i in range(4):
expected_out2[i + 1] = expected_out2[i] + seq_val[i]
for i in range(5):
......@@ -2565,7 +2565,7 @@ class T_Scan(unittest.TestCase):
diff = mitsot_m1 + seq1
next_mitsot_val = mitsot_m2 + diff
next_sitsot_val = sitsot_m1 - diff
nitsot_out = tensor.alloc(numpy.asarray(0., 'float32'),
nitsot_out = tensor.alloc(np.asarray(0., 'float32'),
next_mitsot_val +
next_sitsot_val)
return next_sitsot_val, next_mitsot_val, nitsot_out
......@@ -2584,7 +2584,7 @@ class T_Scan(unittest.TestCase):
assert(len(scan_nodes_from_fct(f)) == 1)
# This generate a scan crash during execution.
# output_shape = f(numpy.arange(5), 5, [1, 2])
# output_shape = f(np.arange(5), 5, [1, 2])
# assert(all(output_shape == (5, 6)))
# The following test will fail in DebugMode if there are
......@@ -2608,7 +2608,7 @@ class T_Scan(unittest.TestCase):
go1 = theano.tensor.grad(o1.mean(), wrt=x)
f = theano.function([x], go1, updates=updates,
allow_input_downcast=True, mode=mode_with_opt)
self.assertTrue(numpy.allclose(f([1, 2, 3]), 2. / 3))
self.assertTrue(np.allclose(f([1, 2, 3]), 2. / 3))
topo = f.maker.fgraph.toposort()
# this new assert is here to test if scan_merging works ..
......@@ -2711,7 +2711,7 @@ class T_Scan(unittest.TestCase):
n.op, theano.scan_module.scan_op.Scan)]
self.assertTrue(len(scans) == 2)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
x_val = rng.uniform(size=(4,)).astype(theano.config.floatX)
y_val = rng.uniform(size=(4,)).astype(theano.config.floatX)
# Run it so DebugMode can detect optimization problems.
......@@ -2752,7 +2752,7 @@ class T_Scan(unittest.TestCase):
return M
# some initializations
hypx = numpy.log(numpy.tile([1,1,1,1,1,1,0.01], (3,1)))
hypx = np.log(np.tile([1,1,1,1,1,1,0.01], (3,1)))
# variables used in the following expressions
hyp = theano.shared(hypx)
......@@ -2763,10 +2763,10 @@ class T_Scan(unittest.TestCase):
M = init_predictive_output(inputs,targets,hyp,x_star,s_star)
X = numpy.random.random((10,4))
Y = numpy.random.random((10,3))
test_m = numpy.random.random((4,))
test_s = numpy.eye(4)
X = np.random.random((10,4))
Y = np.random.random((10,3))
test_m = np.random.random((4,))
test_s = np.eye(4)
# Compute expected outputs (jacobian of M wrt x_star)
dfdm = theano.function([inputs,targets,x_star,s_star],
......@@ -2851,9 +2851,9 @@ class T_Scan(unittest.TestCase):
x = theano.tensor.fmatrix('x')
mem_val = numpy.zeros((2,), dtype='float32')
mem_val = np.zeros((2,), dtype='float32')
memory = theano.shared(mem_val)
W = theano.shared(numpy.random.random((5, 2)).astype('float32'))
W = theano.shared(np.random.random((5, 2)).astype('float32'))
def f(inp, mem):
i = theano.tensor.join(0, inp, mem)
......@@ -2867,7 +2867,7 @@ class T_Scan(unittest.TestCase):
f = theano.function([x], outs[0])
f2 = theano.function([x], outs[1])
x_val = numpy.random.random((4, 3)).astype('float32')
x_val = np.random.random((4, 3)).astype('float32')
f_vals = f(x_val)
memory.set_value(mem_val)
......@@ -2876,12 +2876,12 @@ class T_Scan(unittest.TestCase):
def test_reduce_memory_consumption(self):
x = theano.shared(numpy.asarray(
numpy.random.uniform(size=(10,)), dtype=theano.config.floatX))
x = theano.shared(np.asarray(
np.random.uniform(size=(10,)), dtype=theano.config.floatX))
o, _ = theano.reduce(lambda v, acc: acc + v,
x,
theano.tensor.constant(
numpy.asarray(0.,
np.asarray(0.,
dtype=theano.config.floatX)))
mode = theano.compile.mode.FAST_RUN
mode = mode.excluding('inplace')
......@@ -2905,15 +2905,15 @@ class T_Scan(unittest.TestCase):
gx = theano.tensor.grad(o, x)
f2 = theano.function([], gx)
utt.assert_allclose(f2(), numpy.ones((10,)))
utt.assert_allclose(f2(), np.ones((10,)))
def test_foldl_memory_consumption(self):
x = theano.shared(numpy.asarray(
numpy.random.uniform(size=(10,)), dtype=theano.config.floatX))
x = theano.shared(np.asarray(
np.random.uniform(size=(10,)), dtype=theano.config.floatX))
o, _ = theano.foldl(lambda v, acc: acc + v,
x,
theano.tensor.constant(
numpy.asarray(0.,
np.asarray(0.,
dtype=theano.config.floatX)))
mode = theano.compile.mode.FAST_RUN
......@@ -2938,16 +2938,16 @@ class T_Scan(unittest.TestCase):
gx = theano.tensor.grad(o, x)
f2 = theano.function([], gx)
utt.assert_allclose(f2(), numpy.ones((10,)))
utt.assert_allclose(f2(), np.ones((10,)))
def test_foldr_memory_consumption(self):
x = theano.shared(numpy.asarray(
numpy.random.uniform(size=(10,)), dtype=theano.config.floatX))
x = theano.shared(np.asarray(
np.random.uniform(size=(10,)), dtype=theano.config.floatX))
o, _ = theano.foldr(lambda v, acc: acc + v,
x,
theano.tensor.constant(
numpy.asarray(0.,
np.asarray(0.,
dtype=theano.config.floatX)))
mode = theano.compile.mode.FAST_RUN
......@@ -2972,26 +2972,26 @@ class T_Scan(unittest.TestCase):
gx = theano.tensor.grad(o, x)
f2 = theano.function([], gx)
utt.assert_allclose(f2(), numpy.ones((10,)))
utt.assert_allclose(f2(), np.ones((10,)))
@attr('slow')
def test_rop2(self):
seed = utt.fetch_seed()
rng = numpy.random.RandomState(seed)
rng = np.random.RandomState(seed)
floatX = theano.config.floatX
v_u = numpy.array(rng.uniform(size=(3, 5)) - .5, dtype=floatX)
v_W = numpy.array(rng.uniform(size=(5, 5)) - .5, dtype=floatX)
v_h0 = numpy.array(rng.uniform(size=(5,)) - .5, dtype=floatX)
v_u = np.array(rng.uniform(size=(3, 5)) - .5, dtype=floatX)
v_W = np.array(rng.uniform(size=(5, 5)) - .5, dtype=floatX)
v_h0 = np.array(rng.uniform(size=(5,)) - .5, dtype=floatX)
v_eu = numpy.array(rng.uniform(size=(3, 5)) - .5, dtype=floatX)
v_eW = numpy.array(rng.uniform(size=(5, 5)) - .5, dtype=floatX)
v_eh0 = numpy.array(rng.uniform(size=(5,)) - .5, dtype=floatX)
v_eu = np.array(rng.uniform(size=(3, 5)) - .5, dtype=floatX)
v_eW = np.array(rng.uniform(size=(5, 5)) - .5, dtype=floatX)
v_eh0 = np.array(rng.uniform(size=(5,)) - .5, dtype=floatX)
def rnn_fn(_u, _y, _W):
srng = theano.tensor.shared_randomstreams.RandomStreams(seed)
tmp_val = _u + _y + srng.uniform(size=v_h0.shape) *\
numpy.asarray(1e-6, dtype=floatX)
np.asarray(1e-6, dtype=floatX)
sl_o = theano.tensor.tanh(theano.tensor.dot(_W, tmp_val))
return sl_o, tmp_val
......@@ -3053,15 +3053,15 @@ class T_Scan(unittest.TestCase):
def test_rop(self):
seed = utt.fetch_seed()
rng = numpy.random.RandomState(seed)
rng = np.random.RandomState(seed)
floatX = theano.config.floatX
v_u = numpy.array(rng.uniform(size=(20, 5)), dtype=floatX)
v_W = numpy.array(rng.uniform(size=(5, 5)), dtype=floatX)
v_h0 = numpy.array(rng.uniform(size=(5,)), dtype=floatX)
v_u = np.array(rng.uniform(size=(20, 5)), dtype=floatX)
v_W = np.array(rng.uniform(size=(5, 5)), dtype=floatX)
v_h0 = np.array(rng.uniform(size=(5,)), dtype=floatX)
v_eu = numpy.array(rng.uniform(size=(20, 5)), dtype=floatX)
v_eW = numpy.array(rng.uniform(size=(5, 5)), dtype=floatX)
v_eh0 = numpy.array(rng.uniform(size=(5,)), dtype=floatX)
v_eu = np.array(rng.uniform(size=(20, 5)), dtype=floatX)
v_eW = np.array(rng.uniform(size=(5, 5)), dtype=floatX)
v_eh0 = np.array(rng.uniform(size=(5,)), dtype=floatX)
def rnn_fn(_u, _y, _W):
sl_o = theano.tensor.tanh(theano.tensor.dot(_W, (_u + _y)))
......@@ -3163,14 +3163,14 @@ class T_Scan(unittest.TestCase):
assert len(scan_nodes) == 0
seed = utt.fetch_seed()
rng = numpy.random.RandomState(seed)
rng = np.random.RandomState(seed)
floatX = theano.config.floatX
v_h = numpy.array(rng.uniform(size=(2,)), dtype=floatX)
v_W1 = numpy.array(rng.uniform(size=(2, 2)), dtype=floatX)
v_W2 = numpy.array(rng.uniform(size=(2, 2)), dtype=floatX)
v_h = np.array(rng.uniform(size=(2,)), dtype=floatX)
v_W1 = np.array(rng.uniform(size=(2, 2)), dtype=floatX)
v_W2 = np.array(rng.uniform(size=(2, 2)), dtype=floatX)
v_out = numpy.dot(v_h, v_W1 + v_W2)
sol = numpy.zeros((5, 2))
v_out = np.dot(v_h, v_W1 + v_W2)
sol = np.zeros((5, 2))
# This line is here to make sol have the same shape as the output of
# theano. Note that what we ask theano to do is to repeat the 2
# elements vector v_out 5 times
......@@ -3206,9 +3206,9 @@ class T_Scan(unittest.TestCase):
f_ref = theano.function([W1, W2, step_indices], o, mode='FAST_COMPILE')
# Compare the results of the two implementations
input_values = [numpy.random.random((5, 5)).astype("float32"),
numpy.random.random((5, 5)).astype("float32"),
numpy.arange(5).astype("float32")]
input_values = [np.random.random((5, 5)).astype("float32"),
np.random.random((5, 5)).astype("float32"),
np.arange(5).astype("float32")]
out = f(*input_values)
out_ref = f_ref(*input_values)
......@@ -3243,10 +3243,10 @@ class T_Scan(unittest.TestCase):
([i_t, i_tm1], _) = theano.scan(
fn, sequences=[inp],
outputs_info=[numpy.asarray([0.0, 0.0], theano.config.floatX),
outputs_info=[np.asarray([0.0, 0.0], theano.config.floatX),
None])
f = theano.function([inp], [i_t, i_tm1])
val = numpy.arange(10).reshape(5, 2).astype(theano.config.floatX)
val = np.arange(10).reshape(5, 2).astype(theano.config.floatX)
ret = f(val)
utt.assert_allclose(ret[0], val + 10)
utt.assert_allclose(ret[1], [[0., 0.],
......@@ -3330,7 +3330,7 @@ class T_Scan(unittest.TestCase):
return x_t + 1, theano.scan_module.until(x_t > 3)
o, _ = theano.scan(lambda_fn, x)
f = theano.function([x], o)
vx = numpy.zeros((50,), dtype=theano.config.floatX)
vx = np.zeros((50,), dtype=theano.config.floatX)
vx[23] = 4
out = f(vx)
assert len(out) == 24
......@@ -3344,11 +3344,11 @@ class T_Scan(unittest.TestCase):
o2, _ = theano.scan(lambda x_t: x_t + 2, x)
f = theano.function([x], [o, o2], mode=mode_with_opt)
vx = numpy.zeros((50,), dtype=theano.config.floatX)
vx = np.zeros((50,), dtype=theano.config.floatX)
vx[23] = 4
out, out2 = f(vx)
assert len(out) == 24
assert numpy.all(out2 == vx + 2)
assert np.all(out2 == vx + 2)
lssc = [x for x in f.maker.fgraph.toposort()
if isinstance(x.op, theano.scan_module.scan_op.Scan)]
# One scan node gets optimnized out
......@@ -3402,7 +3402,7 @@ class T_Scan(unittest.TestCase):
polynomial3[-1],
polynomial4[-1]])
test_coeff = numpy.asarray([1, 0, 2], dtype=theano.config.floatX)
test_coeff = np.asarray([1, 0, 2], dtype=theano.config.floatX)
# This will be tested by DEBUG_MODE
out = calculate_polynomial(test_coeff, 3)
assert out[0] == 19
......@@ -3480,7 +3480,7 @@ class T_Scan(unittest.TestCase):
x)
f = theano.function([x], [o, o2], mode=mode_with_opt)
vx = numpy.zeros((50,), dtype=theano.config.floatX)
vx = np.zeros((50,), dtype=theano.config.floatX)
vx[23] = 4
out, out2 = f(vx)
assert len(out) == 24
......@@ -3497,7 +3497,7 @@ class T_Scan(unittest.TestCase):
o, _ = theano.scan(lambda_fn, x)
f = theano.function([x], o.shape[0], mode=mode_with_opt)
vx = numpy.zeros((50,), dtype=theano.config.floatX)
vx = np.zeros((50,), dtype=theano.config.floatX)
vx[23] = 4
out = f(vx)
assert out == 24
......@@ -3516,7 +3516,7 @@ class T_Scan(unittest.TestCase):
[o1.shape[0], o2.shape[0]],
mode=mode_with_opt)
vx = numpy.ones((10,), dtype=theano.config.floatX)
vx = np.ones((10,), dtype=theano.config.floatX)
out1, out2 = f(vx)
assert out1 == 10
assert out2 == 10
......@@ -3535,7 +3535,7 @@ class T_Scan(unittest.TestCase):
[o1.shape[0], o2.shape[0]],
mode=mode_with_opt)
vx = numpy.ones((30,), dtype=theano.config.floatX)
vx = np.ones((30,), dtype=theano.config.floatX)
o1, o2 = f(vx)
assert o1 == 20
assert o2 == 20
......@@ -3635,13 +3635,13 @@ class T_Scan(unittest.TestCase):
# Run the function and validate the outputs
dtype = theano.config.floatX
seq_value = numpy.random.random((10, 3)).astype(dtype)
out_init_value = numpy.random.random((3, 3)).astype(dtype)
non_seq_value = numpy.random.random((3)).astype(dtype)
seq_value = np.random.random((10, 3)).astype(dtype)
out_init_value = np.random.random((3, 3)).astype(dtype)
non_seq_value = np.random.random((3)).astype(dtype)
outputs = fct(seq_value, out_init_value, non_seq_value)
expected_g_seq = numpy.array([[4, 4, 4],
expected_g_seq = np.array([[4, 4, 4],
[3, 3, 3],
[3, 3, 3],
[3, 3, 3],
......@@ -3652,7 +3652,7 @@ class T_Scan(unittest.TestCase):
[1, 1, 1],
[1, 1, 1]])
expected_g_out_init = expected_g_seq[:3]
expected_g_non_seq = numpy.array([22, 22, 22])
expected_g_non_seq = np.array([22, 22, 22])
utt.assert_allclose(outputs[0], expected_g_seq)
utt.assert_allclose(outputs[1], expected_g_out_init)
......@@ -3729,7 +3729,7 @@ class T_Scan(unittest.TestCase):
assert tf([1.0, 2.0, -3.0, 4.0], 2.0) == 42
def test_return_steps(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(size=(2,), low=-5., high=5.))
vW = asarrayX(rng.uniform(size=(2, 2), low=-5., high=5.))
vWout = asarrayX(rng.uniform(size=(2,), low=-5., high=5.))
......@@ -3774,16 +3774,16 @@ class T_Scan(unittest.TestCase):
allow_input_downcast=True)
# compute the values in numpy
v_x = numpy.zeros((8, 2), dtype=theano.config.floatX)
v_y = numpy.zeros((8,), dtype=theano.config.floatX)
v_x[0] = numpy.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 + \
numpy.dot(v_x0, vW)
v_y[0] = numpy.dot(v_x0, vWout) + v_y0[2]
v_x = np.zeros((8, 2), dtype=theano.config.floatX)
v_y = np.zeros((8,), dtype=theano.config.floatX)
v_x[0] = np.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 + \
np.dot(v_x0, vW)
v_y[0] = np.dot(v_x0, vWout) + v_y0[2]
for i in xrange(1, 8):
v_x[i] = numpy.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 + \
numpy.dot(v_x[i - 1], vW)
v_y[i] = numpy.dot(v_x[i - 1], vWout) + v_y[i - 1]
v_x[i] = np.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 + \
np.dot(v_x[i - 1], vW)
v_y[i] = np.dot(v_x[i - 1], vWout) + v_y[i - 1]
(theano_dump, theano_x, theano_y) = f4(v_u1, v_u2, v_x0, v_y0, vW_in1)
......@@ -3811,14 +3811,14 @@ class T_Scan(unittest.TestCase):
assert any([isinstance(node.op, tensor.blas.Dot22)
for node in topo])
vx = numpy.array([[1., 1.], [2., 2.]], dtype=theano.config.floatX)
vA = numpy.array([[1., 1.], [1., 0.]], dtype=theano.config.floatX)
vR = numpy.array([[[2, 1], [4, 2]], [[2, 1], [4, 2]]],
vx = np.array([[1., 1.], [2., 2.]], dtype=theano.config.floatX)
vA = np.array([[1., 1.], [1., 0.]], dtype=theano.config.floatX)
vR = np.array([[[2, 1], [4, 2]], [[2, 1], [4, 2]]],
dtype=theano.config.floatX)
utt.assert_allclose(f(vx, vA), vR)
def test_savemem_opt(self):
y0 = theano.shared(numpy.ones((2, 10)))
y0 = theano.shared(np.ones((2, 10)))
[y1, y2], updates = theano.scan(lambda y: [y, y],
outputs_info=[dict(initial=y0,
taps=[-2]), None],
......@@ -3860,9 +3860,9 @@ class T_Scan(unittest.TestCase):
f = theano.function(inputs=[x, w], outputs=get_outputs(x, w))
# Test the function to ensure it returns valid results
x_value = numpy.random.random((2, 2, 3)).astype(theano.config.floatX)
w_value = numpy.random.random((3, 3)).astype(theano.config.floatX)
expected_output = numpy.tile(x_value[:, 0].sum(0), (3, 1)).transpose()
x_value = np.random.random((2, 2, 3)).astype(theano.config.floatX)
w_value = np.random.random((3, 3)).astype(theano.config.floatX)
expected_output = np.tile(x_value[:, 0].sum(0), (3, 1)).transpose()
output = f(x_value, w_value)
utt.assert_allclose(output, expected_output)
......@@ -3891,17 +3891,17 @@ class T_Scan(unittest.TestCase):
gw, gx = tensor.grad(loss, [w, xinit])
grad_fn = theano.function([xinit, w], [gx, gw],
allow_input_downcast=True)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
# If numbers are small, the gradients with respect to x are small
# and the numeric differentiation becomes unstable.
# To fix this issue I ensure we are sampling numbers larger in
# absolute value than 1.
v_x = numpy.array(rng.uniform(size=(5, 2, 2), low=1., high=3.),
v_x = np.array(rng.uniform(size=(5, 2, 2), low=1., high=3.),
dtype=theano.config.floatX)
# Making some entries to be negative.
pos = rng.uniform(size=(5, 2, 2), low=0., high=1) < .5
v_x[pos] = -1 * v_x[pos]
v_w = numpy.array(rng.uniform(size=(2, 2), low=1., high=3.),
v_w = np.array(rng.uniform(size=(2, 2), low=1., high=3.),
dtype=theano.config.floatX)
pos = rng.uniform(size=(2, 2), low=0., high=1.) < .5
v_w[pos] = -1 * v_w[pos]
......@@ -3916,11 +3916,11 @@ class T_Scan(unittest.TestCase):
num_grad.gx[max_err_pos]))
def test_grad_numeric_shared(self):
shared_var = theano.shared(numpy.float32(1.))
shared_var = theano.shared(np.float32(1.))
def inner_fn():
return [], OrderedDict(
[(shared_var, shared_var + numpy.float32(1.))])
[(shared_var, shared_var + np.float32(1.))])
_, updates = theano.scan(inner_fn,
n_steps=10,
truncate_gradient=-1,
......@@ -3940,7 +3940,7 @@ class T_Scan(unittest.TestCase):
n_pars = 1 * 3 + 3 * 3
# Allocate big parameter array.
pars = theano.shared(numpy.empty(n_pars))
pars = theano.shared(np.empty(n_pars))
# Assign slices.
W1 = pars[:3].reshape(W1shape)
......@@ -3983,15 +3983,15 @@ class T_Scan(unittest.TestCase):
Hp = tensor.Rop(d_cost_wrt_pars, pars, p)
def test_seq_tap_bug_jeremiah(self):
inp = numpy.arange(10).reshape(-1, 1).astype(theano.config.floatX)
exp_out = numpy.zeros((10, 1)).astype(theano.config.floatX)
inp = np.arange(10).reshape(-1, 1).astype(theano.config.floatX)
exp_out = np.zeros((10, 1)).astype(theano.config.floatX)
exp_out[4:] = inp[:-4]
def onestep(x, x_tm4):
return x, x_tm4
seq = tensor.matrix()
initial_value = theano.shared(numpy.zeros((4, 1),
initial_value = theano.shared(np.zeros((4, 1),
dtype=theano.config.floatX))
outputs_info = [OrderedDict(
[('initial', initial_value), ('taps', [-4])]), None]
......@@ -4000,7 +4000,7 @@ class T_Scan(unittest.TestCase):
outputs_info=outputs_info)
f = theano.function([seq], results[1])
assert numpy.all(exp_out == f(inp))
assert np.all(exp_out == f(inp))
def test_borrow_bug_jeremiah(self):
# This tests two things. The first is a bug occuring when scan wrongly
......@@ -4008,29 +4008,29 @@ class T_Scan(unittest.TestCase):
# method will be able to remove the Scan node from the graph in this
# case.
inp = numpy.arange(10).reshape(-1, 1).astype(theano.config.floatX)
exp_out = numpy.zeros((10, 1)).astype(theano.config.floatX)
inp = np.arange(10).reshape(-1, 1).astype(theano.config.floatX)
exp_out = np.zeros((10, 1)).astype(theano.config.floatX)
exp_out[4:] = inp[:-4]
def onestep(x, x_tm4):
return x, x_tm4
seq = tensor.matrix()
initial_value = theano.shared(numpy.zeros((4, 1),
initial_value = theano.shared(np.zeros((4, 1),
dtype=theano.config.floatX))
outputs_info = [OrderedDict([('initial', initial_value),
('taps', [-4])]), None]
results, _ = theano.scan(fn=onestep,
sequences=seq,
outputs_info=outputs_info)
sharedvar = theano.shared(numpy.zeros((1, 1),
sharedvar = theano.shared(np.zeros((1, 1),
dtype=theano.config.floatX))
updates = OrderedDict([(sharedvar, results[0][-1:])])
f = theano.function([seq], results[1], updates=updates)
# This fails if scan uses wrongly the borrow flag
assert numpy.all(exp_out == f(inp))
assert np.all(exp_out == f(inp))
# This fails if Scan's infer_shape() is unable to remove the Scan
# node from the graph.
......@@ -4070,9 +4070,9 @@ class T_Scan(unittest.TestCase):
# Compare obtained outputs with expected outputs
floatX = theano.config.floatX
outputs = fct(numpy.arange(9, dtype=floatX).reshape(3,3))
outputs = fct(np.arange(9, dtype=floatX).reshape(3,3))
states = numpy.array([[0, 1, 2],
states = np.array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8],
[9, 12, 15],
......@@ -4144,8 +4144,8 @@ class T_Scan(unittest.TestCase):
f = theano.function([v], gv)
# Ensure the output of the function is valid
output = f(numpy.random.random(5))
utt.assert_allclose(output, numpy.ones(5))
output = f(np.random.random(5))
utt.assert_allclose(output, np.ones(5))
def test_dot_optimization(self):
A = tensor.matrix('A')
......@@ -4155,10 +4155,10 @@ class T_Scan(unittest.TestCase):
B.dimshuffle(0, 'x', 1)],
outputs_info=[tensor.zeros_like(A)])
f = theano.function([A, B], S.owner.inputs[0][-1])
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
vA = rng.uniform(size=(5, 5)).astype(theano.config.floatX)
vB = rng.uniform(size=(5, 5)).astype(theano.config.floatX)
utt.assert_allclose(f(vA, vB), numpy.dot(vA.T, vB))
utt.assert_allclose(f(vA, vB), np.dot(vA.T, vB))
def test_pregreedy_optimizer(self):
W = tensor.zeros((5, 4))
......@@ -4171,7 +4171,7 @@ class T_Scan(unittest.TestCase):
lambda x: tensor.dot(tensor.dot(x, W) + bh_t, W.T) + bv_t,
outputs_info=v,
n_steps=2)
theano.function([v], chain)(numpy.zeros((3, 5),
theano.function([v], chain)(np.zeros((3, 5),
dtype=theano.config.floatX))
def test_savemem_does_not_duplicate_number_of_scan_nodes(self):
......@@ -4210,7 +4210,7 @@ class T_Scan(unittest.TestCase):
updates=updates,
mode=theano.Mode(linker='py'),
allow_input_downcast=True)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = asarrayX(rng.uniform(size=(5,)))
outs = f(v_u, [0, 0, 0], 0)
utt.assert_allclose(outs[0], v_u + 1)
......@@ -4243,7 +4243,7 @@ class T_Scan(unittest.TestCase):
updates=updates,
mode=theano.Mode(linker='py'),
allow_input_downcast=True)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_w = asarrayX(rng.uniform())
outs = f(v_w, [0, 0, 0], 0)
utt.assert_allclose(outs[0], v_w + 1)
......@@ -4252,7 +4252,7 @@ class T_Scan(unittest.TestCase):
utt.assert_allclose(sh.get_value(), v_w + 4)
def test_grad_bug_disconnected_input(self):
W = theano.shared(numpy.zeros((3, 3)), name='W')
W = theano.shared(np.zeros((3, 3)), name='W')
v = theano.tensor.ivector(name='v')
y, _ = theano.scan(lambda i, W: W[i], sequences=v, outputs_info=None, non_sequences=W)
......@@ -4270,14 +4270,14 @@ class T_Scan(unittest.TestCase):
# theano.printing.debugprint(out)
return theano.function([], out)()
x = theano.shared(numpy.asarray(0., dtype=theano.config.floatX))
x = theano.shared(np.asarray(0., dtype=theano.config.floatX))
utt.assert_allclose(test(x, tensor.sum((x+1)**2), mention_y=False),
1.21000003815)
utt.assert_allclose(test(x, tensor.sum((x+1)**2), mention_y=True),
1.21000003815)
def test_grad_find_input(self):
w = theano.shared(numpy.array(0, dtype='float32'), name='w')
w = theano.shared(np.array(0, dtype='float32'), name='w')
init = tensor.fscalar('init')
out, _ = theano.scan(
......@@ -4333,7 +4333,7 @@ class T_Scan(unittest.TestCase):
for out in [y1, y2, y3, y4, y5, y6]:
# This used to raise an exception
f = theano.function([W, v], out, mode=mode_with_opt)
f(numpy.zeros((3, 3), dtype=theano.config.floatX), [1, 2])
f(np.zeros((3, 3), dtype=theano.config.floatX), [1, 2])
scan_nodes = scan_nodes_from_fct(f)
assert len(scan_nodes) == 1
......@@ -4375,9 +4375,9 @@ class T_Scan(unittest.TestCase):
# This used to raise an exception
f = theano.function([W, v, vv], out, on_unused_input='ignore',
mode=mode_with_opt)
f(numpy.zeros((3, 3), theano.config.floatX),
f(np.zeros((3, 3), theano.config.floatX),
[1, 2],
numpy.zeros((3, 3), theano.config.floatX))
np.zeros((3, 3), theano.config.floatX))
scan_nodes = scan_nodes_from_fct(f)
assert len(scan_nodes) == 1
......@@ -4413,7 +4413,7 @@ class T_Scan(unittest.TestCase):
result_inner, _ = theano.scan(
fn=loss_inner,
outputs_info=tensor.as_tensor_variable(
numpy.asarray(0, dtype=numpy.float32)),
np.asarray(0, dtype=np.float32)),
non_sequences=[W],
n_steps=1,
)
......@@ -4422,7 +4422,7 @@ class T_Scan(unittest.TestCase):
result_outer, _ = theano.scan(
fn=loss_outer,
outputs_info=tensor.as_tensor_variable(
numpy.asarray(0, dtype=numpy.float32)),
np.asarray(0, dtype=np.float32)),
non_sequences=[W],
n_steps=n_steps,
return_list=True,
......@@ -4432,14 +4432,14 @@ class T_Scan(unittest.TestCase):
H = theano.gradient.hessian(cost, W)
print(".", file=sys.stderr)
f = theano.function([W, n_steps], H)
f(numpy.ones((8,), dtype='float32'), 1)
f(np.ones((8,), dtype='float32'), 1)
def test_strict_mode(self):
n = 10
w = numpy.array([[-1, 2], [3, -4]]).astype(theano.config.floatX)
w = np.array([[-1, 2], [3, -4]]).astype(theano.config.floatX)
w_ = theano.shared(w)
x0 = numpy.array([1, 2]).astype(theano.config.floatX)
x0 = np.array([1, 2]).astype(theano.config.floatX)
x0_ = tensor.vector(name='x0', dtype=theano.config.floatX)
def _scan_loose(x):
......@@ -4474,9 +4474,9 @@ class T_Scan(unittest.TestCase):
def test_strict_mode_ex(self):
n = 10
w = numpy.array([[-1, 2], [3, -4]]).astype(theano.config.floatX)
w = np.array([[-1, 2], [3, -4]]).astype(theano.config.floatX)
w_ = theano.shared(w)
x0 = numpy.array([1, 2]).astype(theano.config.floatX)
x0 = np.array([1, 2]).astype(theano.config.floatX)
x0_ = tensor.vector(name='x0', dtype=theano.config.floatX)
def _scan_loose(x):
......@@ -4497,7 +4497,7 @@ class T_Scan(unittest.TestCase):
# Build a MonitorMode that counts how many values are greater than 10
def detect_large_outputs(i, node, fn):
for output in fn.outputs:
if isinstance(output[0], numpy.ndarray):
if isinstance(output[0], np.ndarray):
detect_large_outputs.large_count += (output[0] > 10).sum()
detect_large_outputs.large_count = 0
......@@ -4516,7 +4516,7 @@ class T_Scan(unittest.TestCase):
f = theano.function(inputs=[A, k],
outputs=final_result,
updates=updates)
f(numpy.asarray([2, 3, .1, 0, 1], dtype=theano.config.floatX), 4)
f(np.asarray([2, 3, .1, 0, 1], dtype=theano.config.floatX), 4)
# There should be 3 outputs greater than 10: prior_result[0] at step 3,
# and prior_result[1] at steps 2 and 3.
......@@ -4574,19 +4574,19 @@ class ScanGpuTests:
mode=self.mode_with_gpu)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(4,), low=-5., high=5.)
v_x0 = rng.uniform()
W = rng.uniform()
W_in = rng.uniform()
v_u = numpy.asarray(v_u, dtype='float32')
v_x0 = numpy.asarray(v_x0, dtype='float32')
W = numpy.asarray(W, dtype='float32')
W_in = numpy.asarray(W_in, dtype='float32')
v_u = np.asarray(v_u, dtype='float32')
v_x0 = np.asarray(v_x0, dtype='float32')
W = np.asarray(W, dtype='float32')
W_in = np.asarray(W_in, dtype='float32')
# compute the output in numpy
v_out = numpy.zeros((4,))
v_out = np.zeros((4,))
v_out[0] = v_u[0] * W_in + v_x0 * W
for step in xrange(1, 4):
v_out[step] = v_u[step] * W_in + v_out[step - 1] * W
......@@ -4646,14 +4646,14 @@ class ScanGpuTests:
mode=self.mode_with_gpu)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(4,), low=-5., high=5.)
v_x0 = rng.uniform()
W = rng.uniform()
W_in = rng.uniform()
# compute the output in numpy
v_out = numpy.zeros((4,))
v_out = np.zeros((4,))
v_out[0] = v_u[0] * W_in + v_x0 * W
for step in xrange(1, 4):
v_out[step] = v_u[step] * W_in + v_out[step - 1] * W
......@@ -4708,20 +4708,20 @@ class ScanGpuTests:
mode=self.mode_with_gpu)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(4,), low=-5., high=5.)
v_x0 = rng.uniform()
W = rng.uniform()
W_in = rng.uniform()
# compute the output in numpy
v_out1 = numpy.zeros((4,))
v_out2 = numpy.zeros((4,), dtype='int64')
v_out1 = np.zeros((4,))
v_out2 = np.zeros((4,), dtype='int64')
v_out1[0] = v_u[0] * W_in + v_x0 * W
v_out2[0] = v_u[0] + v_x0
for step in xrange(1, 4):
v_out1[step] = v_u[step] * W_in + v_out1[step - 1] * W
v_out2[step] = numpy.int64(v_u[step] + v_out1[step - 1])
v_out2[step] = np.int64(v_u[step] + v_out1[step - 1])
theano_out1, theano_out2 = f2(v_u, v_x0, W_in, W)
utt.assert_allclose(theano_out1, v_out1)
......@@ -4735,8 +4735,8 @@ class ScanGpuTests:
assert self.is_scan_on_gpu(scan_node)
def test_gibbs_chain(self):
rng = numpy.random.RandomState(utt.fetch_seed())
v_vsample = numpy.array(rng.binomial(1, .5, size=(3, 20),),
rng = np.random.RandomState(utt.fetch_seed())
v_vsample = np.array(rng.binomial(1, .5, size=(3, 20),),
dtype='float32')
vsample = theano.shared(v_vsample)
trng = theano.sandbox.rng_mrg.MRG_RandomStreams(
......@@ -4788,11 +4788,11 @@ class ScanGpuTests:
# Initialize the network parameters
floatX = theano.config.floatX
U = theano.shared(numpy.zeros((n_in, n_hid), dtype="float32"),
U = theano.shared(np.zeros((n_in, n_hid), dtype="float32"),
name='W_xin_to_l1')
V = theano.shared(numpy.zeros((n_hid, n_hid), dtype="float32"),
V = theano.shared(np.zeros((n_hid, n_hid), dtype="float32"),
name='W_l1_to_l1')
W = theano.shared(numpy.zeros((n_hid, n_out), dtype="float32"),
W = theano.shared(np.zeros((n_hid, n_out), dtype="float32"),
name='W_l1_to_l2')
nparams = [U, V, W]
......@@ -4802,7 +4802,7 @@ class ScanGpuTests:
def scan_l(baseline, last_step):
return baseline + tensor.dot(last_step, V)
zero_output = tensor.alloc(numpy.asarray(0., dtype="float32"),
zero_output = tensor.alloc(np.asarray(0., dtype="float32"),
mb_size, n_hid)
l1_out, _ = theano.scan(scan_l, sequences=[l1_base],
......@@ -4833,9 +4833,9 @@ class ScanGpuTests:
assert len(grad_scan_node.outputs) == 2, len(grad_scan_node.outputs)
# Call the theano function to ensure the absence of a memory error
feval_backprop(numpy.zeros((mb_length, mb_size, n_in),
feval_backprop(np.zeros((mb_length, mb_size, n_in),
dtype="float32"),
numpy.zeros((mb_length, mb_size, n_out),
np.zeros((mb_length, mb_size, n_out),
dtype="float32"))
def test_memory_reuse_gpudimshuffle(self):
......@@ -4864,11 +4864,11 @@ class ScanGpuTests:
fct = theano.function([input1, init], [out1, out2],
mode=self.mode_with_gpu)
output = fct(numpy.ones((2, 1, 1), dtype="float32"),
numpy.ones((1, 1, 1), dtype="float32"))
output = fct(np.ones((2, 1, 1), dtype="float32"),
np.ones((1, 1, 1), dtype="float32"))
expected_output = (numpy.array([2, 4], dtype="float32"),
numpy.array([3, 7], dtype="float32"))
expected_output = (np.array([2, 4], dtype="float32"),
np.array([3, 7], dtype="float32"))
utt.assert_allclose(output, expected_output)
......@@ -4985,7 +4985,7 @@ def test_speed():
if not theano.config.cxx:
raise SkipTest("G++ not available, so we need to skip this test.")
r = numpy.arange(10000).astype(theano.config.floatX).reshape(1000, 10)
r = np.arange(10000).astype(theano.config.floatX).reshape(1000, 10)
t0 = time.time()
for i in xrange(1, 1000):
......@@ -4993,7 +4993,7 @@ def test_speed():
t1 = time.time()
print('python', t1 - t0)
r = numpy.arange(10000).astype(theano.config.floatX).reshape(1000, 10)
r = np.arange(10000).astype(theano.config.floatX).reshape(1000, 10)
t0 = time.time()
r_i = iter(r[1:])
r_ii = iter(r[:-1])
......@@ -5015,7 +5015,7 @@ def test_speed():
print('python with builtin iterator', t1 - t0)
if 1:
r = numpy.arange(10000).astype(theano.config.floatX).reshape(1000, 10)
r = np.arange(10000).astype(theano.config.floatX).reshape(1000, 10)
s_r = tensor.matrix()
s_y, updates = theano.scan(fn=lambda ri, rii: ri + rii,
sequences=[s_r[1:]],
......@@ -5030,9 +5030,9 @@ def test_speed():
print('theano (scan, cvm)', t3 - t2)
if 1:
r = numpy.arange(10000).astype(theano.config.floatX).reshape(-1, 10)
r = np.arange(10000).astype(theano.config.floatX).reshape(-1, 10)
shared_r = theano.shared(r)
s_i = theano.shared(numpy.array(1))
s_i = theano.shared(np.array(1))
s_rinc = tensor.inc_subtensor(shared_r[s_i], shared_r[s_i - 1],
tolerate_inplace_aliasing=True)
# theano.printing.debugprint(s_rinc)
......@@ -5075,18 +5075,18 @@ def test_speed_rnn():
L = 10000
N = 50
numpy.random.seed(2523452)
r = numpy.arange(L * N).astype(theano.config.floatX).reshape(L, N)
w = numpy.random.randn(N, N).astype(theano.config.floatX)
np.random.seed(2523452)
r = np.arange(L * N).astype(theano.config.floatX).reshape(L, N)
w = np.random.randn(N, N).astype(theano.config.floatX)
t0 = time.time()
for i in xrange(1, L):
r[i] = numpy.tanh(numpy.dot(r[i - 1], w))
r[i] = np.tanh(np.dot(r[i - 1], w))
t1 = time.time()
print('python', t1 - t0)
if 1:
r = numpy.arange(L * N).astype(theano.config.floatX).reshape(L, N)
r = np.arange(L * N).astype(theano.config.floatX).reshape(L, N)
s_r = tensor.matrix()
s_y, updates = theano.scan(
fn=lambda ri, rii: tensor.tanh(tensor.dot(rii, w)),
......@@ -5102,7 +5102,7 @@ def test_speed_rnn():
print('theano (scan, cvm)', t3 - t2)
if 1:
r = numpy.arange(L * N).astype(theano.config.floatX).reshape(L, N)
r = np.arange(L * N).astype(theano.config.floatX).reshape(L, N)
s_w = theano.shared(w)
shared_r = theano.shared(r)
s_i = theano.scalar.sharedvar.shared(1)
......@@ -5154,18 +5154,18 @@ def test_speed_batchrnn():
B = 50
N = 400
numpy.random.seed(2523452)
r = numpy.arange(B * L * N).astype(theano.config.floatX).reshape(L, B, N)
w = numpy.random.randn(N, N).astype(theano.config.floatX)
np.random.seed(2523452)
r = np.arange(B * L * N).astype(theano.config.floatX).reshape(L, B, N)
w = np.random.randn(N, N).astype(theano.config.floatX)
t0 = time.time()
for i in xrange(1, L):
r[i] = numpy.tanh(numpy.dot(r[i - 1], w))
r[i] = np.tanh(np.dot(r[i - 1], w))
t1 = time.time()
print('python', t1 - t0)
if 1:
r = numpy.arange(B * L * N).astype(
r = np.arange(B * L * N).astype(
theano.config.floatX).reshape(L, B, N)
s_w = theano.shared(w)
shared_r = theano.shared(r)
......@@ -5328,9 +5328,9 @@ def test_compute_test_value():
theano.config.compute_test_value = 'raise'
try:
x = tensor.vector('x')
xv = numpy.ones(3, dtype=theano.config.floatX)
xv = np.ones(3, dtype=theano.config.floatX)
x.tag.test_value = xv
y = theano.shared(numpy.arange(3, dtype=theano.config.floatX),
y = theano.shared(np.arange(3, dtype=theano.config.floatX),
name='y')
z, updates = theano.scan(
fn=lambda u, v: u + v,
......@@ -5351,10 +5351,10 @@ def test_compute_test_value_nonseq():
theano.config.compute_test_value = 'raise'
try:
x = tensor.vector('x')
xv = numpy.ones(3, dtype=theano.config.floatX)
xv = np.ones(3, dtype=theano.config.floatX)
x.tag.test_value = xv
y = theano.shared(
numpy.arange(9, dtype=theano.config.floatX).reshape(3, 3),
np.arange(9, dtype=theano.config.floatX).reshape(3, 3),
name='y')
z, updates = theano.scan(
fn=lambda u, v: u + v,
......@@ -5373,7 +5373,7 @@ def test_compute_test_value_nonseq():
def test_compute_test_value_grad():
# Test case originally reported by Bitton Tenessi
# https://groups.google.com/d/msg/theano-users/fAP3i2CbskQ/3OgBf4yjqiQJ
WEIGHT = numpy.array([1, 2, 1, 3, 4, 1, 5, 6, 1, 7, 8, 1],
WEIGHT = np.array([1, 2, 1, 3, 4, 1, 5, 6, 1, 7, 8, 1],
dtype='float32')
old_compute_test_val = theano.config.compute_test_value
......@@ -5387,13 +5387,13 @@ def test_compute_test_value_grad():
W = W_flat.reshape((2, 2, 3))
outputs_mi = tensor.as_tensor_variable(
numpy.asarray(0, dtype='float32'))
outputs_mi.tag.test_value = numpy.asarray(0, dtype='float32')
np.asarray(0, dtype='float32'))
outputs_mi.tag.test_value = np.asarray(0, dtype='float32')
def loss_mi(mi, sum_mi, W):
outputs_ti = tensor.as_tensor_variable(
numpy.asarray(0, dtype='float32'))
outputs_ti.tag.test_value = numpy.asarray(0, dtype='float32')
np.asarray(0, dtype='float32'))
outputs_ti.tag.test_value = np.asarray(0, dtype='float32')
def loss_ti(ti, sum_ti, mi, W):
return W.sum().sum().sum() + sum_ti
......@@ -5430,10 +5430,10 @@ def test_compute_test_value_grad_cast():
theano.config.compute_test_value = 'raise'
try:
h = tensor.matrix('h')
h.tag.test_value = numpy.array([[1, 2, 3, 4], [5, 6, 7, 8]],
h.tag.test_value = np.array([[1, 2, 3, 4], [5, 6, 7, 8]],
dtype=floatX)
w = theano.shared(numpy.random.randn(4, 3).astype(floatX), name='w')
w = theano.shared(np.random.randn(4, 3).astype(floatX), name='w')
outputs, _ = theano.scan(lambda i, h, w: (theano.dot(h[i], w), i),
outputs_info=[None, 0], non_sequences=[h, w],
......@@ -5473,10 +5473,10 @@ def test_outputs_taps_check():
def test_default_value_broadcasted():
def floatx(X):
return numpy.asarray(X, dtype=theano.config.floatX)
return np.asarray(X, dtype=theano.config.floatX)
def init_weights(shape, name):
return theano.shared(floatx(numpy.random.randn(*shape) * 0.1), name)
return theano.shared(floatx(np.random.randn(*shape) * 0.1), name)
X = theano.tensor.matrix('X')
in_size = 2
......@@ -5494,14 +5494,14 @@ def test_default_value_broadcasted():
gW_x = theano.tensor.grad(cost, W_x)
updates = [(W_x, W_x - 0.1 * gW_x)]
f = theano.function([X], outputs=cost, updates=updates)
f(numpy.random.rand(10, in_size).astype(X.dtype))
f(np.random.rand(10, in_size).astype(X.dtype))
class TestInconsistentBroadcast(unittest.TestCase):
def test_raise_error(self):
x = tensor.tensor3()
initial_x = tensor.constant(numpy.zeros((1, 10)))
initial_x = tensor.constant(np.zeros((1, 10)))
y, updates = theano.scan(fn=lambda x, prev_x: x + prev_x,
sequences=x,
outputs_info=[dict(initial=initial_x)])
......
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