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提交 3efc0fd2 authored 作者: Razvan Pascanu's avatar Razvan Pascanu
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renamed every TT to tensor to follow coding style

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正在显示 包含 34 行增加 和 34 行删除
theano/tensor/tests/test_rop.py
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......@@ -54,7 +54,7 @@ import unittest
from theano.tests import unittest_tools as utt
from theano import function
import theano
import theano.tensor as TT
from theano import tensor
import numpy
from theano.gof import Op, Apply
......@@ -89,19 +89,19 @@ class test_RopLop(unittest.TestCase):
def setUp(self):
# Using vectors make things a lot simpler for generating the same
# computations using scan
self.x = TT.vector('x')
self.v = TT.vector('v')
self.x = tensor.vector('x')
self.v = tensor.vector('v')
self.rng = numpy.random.RandomState(utt.fetch_seed())
self.in_shape = ( 5+self.rng.randint(30),)
self.mx = TT.matrix('mx')
self.mv = TT.matrix('mv')
self.mx = tensor.matrix('mx')
self.mv = tensor.matrix('mv')
self.mat_in_shape = ( 5 + self.rng.randint(30),
5+self.rng.randint(30))
def check_nondiff_rop(self, y):
raised = False
try:
tmp = TT.Rop(y, self.x, self.v)
tmp = tensor.Rop(y, self.x, self.v)
except ValueError:
raised = True
if not raised:
......@@ -112,10 +112,10 @@ class test_RopLop(unittest.TestCase):
def check_mat_rop_lop(self, y, out_shape):
vx = numpy.asarray(self.rng.uniform(size=self.mat_in_shape), theano.config.floatX)
vv = numpy.asarray(self.rng.uniform(size=self.mat_in_shape), theano.config.floatX)
yv = TT.Rop(y, self.mx, self.mv)
yv = tensor.Rop(y, self.mx, self.mv)
rop_f = function([self.mx, self.mv], yv)
sy, _ = theano.scan( lambda i,y,x,v: (TT.grad(y[i],x)*v).sum(),
sequences = TT.arange(y.shape[0]),
sy, _ = theano.scan( lambda i,y,x,v: (tensor.grad(y[i],x)*v).sum(),
sequences = tensor.arange(y.shape[0]),
non_sequences = [y,self.mx,self.mv])
scan_f = function([self.mx,self.mv], sy)
......@@ -129,10 +129,10 @@ class test_RopLop(unittest.TestCase):
replace={self.mx:break_op(self.mx)}))
vv = numpy.asarray(self.rng.uniform(size=out_shape), theano.config.floatX)
yv = TT.Lop(y, self.mx, self.v)
yv = tensor.Lop(y, self.mx, self.v)
lop_f = function([self.mx, self.v], yv)
sy = TT.grad((self.v*y).sum(), self.mx)
sy = tensor.grad((self.v*y).sum(), self.mx)
scan_f = function([self.mx, self.v], sy)
......@@ -147,12 +147,12 @@ class test_RopLop(unittest.TestCase):
vx = numpy.asarray(self.rng.uniform(size=self.in_shape), theano.config.floatX)
vv = numpy.asarray(self.rng.uniform(size=self.in_shape), theano.config.floatX)
yv = TT.Rop(y,self.x,self.v)
yv = tensor.Rop(y,self.x,self.v)
rop_f = function([self.x,self.v], yv)
J, _ = theano.scan( lambda i,y,x: TT.grad(y[i],x),
sequences = TT.arange(y.shape[0]),
J, _ = theano.scan( lambda i,y,x: tensor.grad(y[i],x),
sequences = tensor.arange(y.shape[0]),
non_sequences = [y,self.x])
sy = TT.dot(J, self.v)
sy = tensor.dot(J, self.v)
scan_f = function([self.x,self.v], sy)
......@@ -167,12 +167,12 @@ class test_RopLop(unittest.TestCase):
vx = numpy.asarray(self.rng.uniform(size=self.in_shape), theano.config.floatX)
vv = numpy.asarray(self.rng.uniform(size=out_shape), theano.config.floatX)
yv = TT.Lop(y,self.x,self.v)
yv = tensor.Lop(y,self.x,self.v)
lop_f = function([self.x,self.v], yv)
J, _ = theano.scan( lambda i,y,x: TT.grad(y[i],x),
sequences = TT.arange(y.shape[0]),
J, _ = theano.scan( lambda i,y,x: tensor.grad(y[i],x),
sequences = tensor.arange(y.shape[0]),
non_sequences = [y,self.x])
sy = TT.dot(self.v, J)
sy = tensor.dot(self.v, J)
scan_f = function([self.x,self.v], sy)
......@@ -185,22 +185,22 @@ class test_RopLop(unittest.TestCase):
self.check_nondiff_rop( self.x.shape[0])
def test_specifyshape(self):
self.check_rop_lop(TT.specify_shape(self.x, self.in_shape),
self.check_rop_lop(tensor.specify_shape(self.x, self.in_shape),
self.in_shape)
def test_max(self):
## If we call max directly, we will return an CAReduce object
## and he don't have R_op implemented!
#self.check_mat_rop_lop(TT.max(self.mx, axis=[0,1])[0],
#self.check_mat_rop_lop(tensor.max(self.mx, axis=[0,1])[0],
# ())
self.check_mat_rop_lop(TT.max(self.mx, axis=0),
self.check_mat_rop_lop(tensor.max(self.mx, axis=0),
(self.mat_in_shape[1],))
self.check_mat_rop_lop(TT.max(self.mx, axis=1),
self.check_mat_rop_lop(tensor.max(self.mx, axis=1),
(self.mat_in_shape[0],))
def test_argmax(self):
self.check_nondiff_rop(TT.argmax(self.mx,axis=1))
self.check_nondiff_rop(tensor.argmax(self.mx,axis=1))
def test_subtensor(self):
self.check_rop_lop(self.x[:4], (4,))
......@@ -209,7 +209,7 @@ class test_RopLop(unittest.TestCase):
tv = numpy.asarray( self.rng.uniform(size=(3,)),
theano.config.floatX)
t = theano.shared(tv)
out = TT.inc_subtensor(self.x[:3], t)
out = tensor.inc_subtensor(self.x[:3], t)
self.check_rop_lop(out, self.in_shape)
......@@ -217,7 +217,7 @@ class test_RopLop(unittest.TestCase):
tv = numpy.asarray( self.rng.uniform(size=(10,)),
theano.config.floatX)
t = theano.shared(tv)
out = TT.inc_subtensor(t[:4], self.x[:4])
out = tensor.inc_subtensor(t[:4], self.x[:4])
self.check_rop_lop(out, (10,))
......@@ -225,7 +225,7 @@ class test_RopLop(unittest.TestCase):
tv = numpy.asarray( self.rng.uniform(size=(3,)),
theano.config.floatX)
t = theano.shared(tv)
out = TT.set_subtensor(self.x[:3], t)
out = tensor.set_subtensor(self.x[:3], t)
self.check_rop_lop(out, self.in_shape)
......@@ -233,7 +233,7 @@ class test_RopLop(unittest.TestCase):
tv = numpy.asarray( self.rng.uniform(size=(10,)),
theano.config.floatX)
t = theano.shared(tv)
out = TT.set_subtensor(t[:4], self.x[:4])
out = tensor.set_subtensor(t[:4], self.x[:4])
self.check_rop_lop(out, (10,))
......@@ -241,7 +241,7 @@ class test_RopLop(unittest.TestCase):
tv = numpy.asarray( self.rng.uniform(size=(10,)),
theano.config.floatX)
t = theano.shared(tv)
out = TT.join(0, self.x, t)
out = tensor.join(0, self.x, t)
self.check_rop_lop(out, (self.in_shape[0]+10,))
def test_dot(self):
......@@ -249,14 +249,14 @@ class test_RopLop(unittest.TestCase):
vW = numpy.asarray(self.rng.uniform(size=(insh,insh)),
theano.config.floatX)
W = theano.shared(vW)
self.check_rop_lop( TT.dot(self.x, W), self.in_shape)
self.check_rop_lop( tensor.dot(self.x, W), self.in_shape)
def test_elemwise0(self):
self.check_rop_lop( (self.x+1)**2, self.in_shape)
def test_elemwise1(self):
self.check_rop_lop( self.x+TT.cast(self.x, 'int32'),
self.check_rop_lop( self.x+tensor.cast(self.x, 'int32'),
self.in_shape)
def test_sum(self):
......@@ -265,15 +265,15 @@ class test_RopLop(unittest.TestCase):
def test_softmax(self):
# Softmax adds an extra dimnesion !
self.check_rop_lop( TT.nnet.softmax(self.x)[0], self.in_shape[0])
self.check_rop_lop( tensor.nnet.softmax(self.x)[0], self.in_shape[0])
def test_alloc(self):
# Alloc of the sum of x into a vector
out1d = TT.alloc(self.x.sum(), self.in_shape[0])
out1d = tensor.alloc(self.x.sum(), self.in_shape[0])
self.check_rop_lop(out1d, self.in_shape[0])
# Alloc of x into a 3-D tensor, flattened
out3d = TT.alloc(self.x,
out3d = tensor.alloc(self.x,
self.mat_in_shape[0], self.mat_in_shape[1], self.in_shape[0])
self.check_rop_lop(out3d.flatten(),
self.mat_in_shape[0] * self.mat_in_shape[1] * self.in_shape[0])
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