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提交 618a8e25 authored 作者: Pascal Lamblin's avatar Pascal Lamblin
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examples/logistic_regression.py deleted 100644 → 0
浏览文件 @ cfecf720
import sys
sys.path.insert(0, '..')
import theano
from theano import tensor as T
from theano.tensor import nnet
from theano.compile import module
from theano import printing, pprint
from theano import compile
import numpy as N
class LogisticRegressionN(module.FancyModule):
class InstanceType(module.FancyModuleInstance):
def initialize(self, n_in, n_out, seed = None):
#self.component is the LogisticRegressionTemplate instance that built this guy.
rng = N.random.RandomState(seed)
self.w = rng.randn(n_in, n_out)
self.b = rng.randn(n_out)
self.lr = 0.01
self.__hide__ = ['params']
def __eq__(self, other):
if not isinstance(other.component, LogisticRegressionN) and not isinstance(other.component, LogisticRegression2):
raise NotImplementedError
#we compare the member.
if (N.abs(self.w-other.w)<1e-8).all() and (N.abs(self.b-other.b)<1e-8).all() and self.lr == other.lr:
return True
return False
def __hash__(self):
raise NotImplementedError
def __init__(self, x = None, targ = None):
super(LogisticRegressionN, self).__init__() #boilerplate
self.x = x if x is not None else T.matrix()
self.targ = targ if targ is not None else T.lvector()
self.w = module.Member(T.matrix()) #automatically names
self.b = module.Member(T.vector()) #automatically names
self.lr = module.Member(T.dscalar()) #provides an external interface to change it
#and makes it an implicit input to any Method you build.
self.params = [self.w, self.b]
xent, y = nnet.crossentropy_softmax_1hot(
T.dot(self.x, self.w) + self.b, self.targ)
xent = T.sum(xent)
self.y = y
self.xent = xent
gparams = T.grad(xent, self.params)
self.update = module.Method([self.x, self.targ], xent,
updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gparams)))
self.apply = module.Method([self.x], T.argmax(T.dot(self.x, self.w) + self.b, axis=1))
class LogisticRegression2(module.FancyModule):
class InstanceType(module.FancyModuleInstance):
def initialize(self, n_in, seed = 1827):
#self.component is the LogisticRegressionTemplate instance that built this guy.
rng = N.random.RandomState(seed)
self.w = rng.randn(n_in, 1)
self.b = rng.randn(1)
self.lr = 0.01
self.__hide__ = ['params']
def __eq__(self, other):
if not isinstance(other.component, LogisticRegressionN) and not isinstance(other.component, LogisticRegression2):
raise NotImplementedError
#we compare the member.
if (N.abs(self.w-other.w)<1e-8).all() and (N.abs(self.b-other.b)<1e-8).all() and self.lr == other.lr:
return True
return False
def __hash__(self):
raise NotImplementedError
def __init__(self, x = None, targ = None):
super(LogisticRegression2, self).__init__() #boilerplate
self.x = x if x is not None else T.matrix()
self.targ = targ if targ is not None else T.lcol()
self.w = module.Member(T.dmatrix()) #automatically names
self.b = module.Member(T.dvector()) #automatically names
self.lr = module.Member(T.dscalar()) #provides an external interface to change it
#and makes it an implicit input to any Method you build.
self.params = [self.w, self.b]
y = nnet.sigmoid(T.dot(self.x, self.w))
xent_elem = -self.targ * T.log(y) - (1.0 - self.targ) * T.log(1.0 - y)
xent = T.sum(xent_elem)
self.y = y
self.xent_elem = xent_elem
self.xent = xent
gparams = T.grad(xent, self.params)
self.update = module.Method([self.x, self.targ], xent,
updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gparams)))
self.apply = module.Method([self.x], T.argmax(T.dot(self.x, self.w) + self.b, axis=1))
def main():
pprint.assign(nnet.crossentropy_softmax_1hot_with_bias_dx, printing.FunctionPrinter('xsoftmaxdx'))
pprint.assign(nnet.crossentropy_softmax_argmax_1hot_with_bias, printing.FunctionPrinter('nll', 'softmax', 'argmax'))
if 1:
lrc = LogisticRegressionN()
print '================'
print lrc.update.pretty()
print '================'
print lrc.update.pretty(mode = theano.Mode('py', 'fast_run'))
print '================'
# print lrc.update.pretty(mode = compile.FAST_RUN.excluding('inplace'))
# print '================'
# sys.exit(0)
lr = lrc.make(10, 2, mode=theano.Mode('c|py', 'fast_run'))
#lr = lrc.make(10, 2, mode=compile.FAST_RUN.excluding('fast_run'))
#lr = lrc.make(10, 2, mode=theano.Mode('py', 'merge')) #'FAST_RUN')
data_x = N.random.randn(5, 10)
data_y = (N.random.randn(5) > 0)
for i in xrange(10000):
lr.lr = 0.02
xe = lr.update(data_x, data_y)
if i % 100 == 0:
print i, xe
print
print 'TRAINED MODEL:'
print lr
if 0:
lrc = LogisticRegression2()
lr = lrc.make(10, mode=theano.Mode('c|py', 'merge')) #'FAST_RUN')
data_x = N.random.randn(5, 10)
data_y = (N.random.randn(5, 1) > 0)
for i in xrange(10000):
xe = lr.update(data_x, data_y)
if i % 100 == 0:
print i, xe
print
print 'TRAINED MODEL:'
print lr
if __name__ == '__main__':
main()
examples/tests/test_logistic_regression.py deleted 100644 → 0
浏览文件 @ cfecf720
#!/usr/bin/env python
#
# UNIT TEST
#
import unittest
import numpy
from theano import gof
from theano.gradient import *
from theano import gradient
import theano
import sys
from theano import tensor as T
from theano.tensor import nnet
from theano.compile import module
from theano import printing, pprint
from theano import compile
import numpy as N
class test_logistic_regression_example(unittest.TestCase):
def test_example_main(self):
"""Test that the file execute without trouble"""
import os
sys.path.append(os.path.realpath(".."))
import logistic_regression
logistic_regression.main()
def test_example_moduleN(self):
"""Test that the LogisticRegressionN module execute the same with different mode"""
import os
sys.path.append(os.path.realpath(".."))
import logistic_regression
pprint.assign(nnet.crossentropy_softmax_1hot_with_bias_dx, printing.FunctionPrinter('xsoftmaxdx'))
pprint.assign(nnet.crossentropy_softmax_argmax_1hot_with_bias, printing.FunctionPrinter('nll', 'softmax', 'argmax'))
lrc = logistic_regression.LogisticRegressionN()
lr0 = lrc.make(10, 2, seed=1827)
lr1 = lrc.make(10, 2, mode=theano.Mode('c|py', 'fast_run'), seed=1827)
lr2 = lrc.make(10, 2, mode=theano.Mode('py', 'fast_run'), seed=1827)
lr3 = lrc.make(10, 2, mode=theano.Mode('py', 'merge'), seed=1827) #'FAST_RUN')
lr4 = lrc.make(10, 2, mode=compile.FAST_RUN.excluding('fast_run'), seed=1827)
#FAST_RUN, FAST_COMPILE,
data_x = N.random.randn(5, 10)
data_y = (N.random.randn(5) > 0)
def train(lr):
for i in xrange(1000):
lr.lr = 0.02
xe = lr.update(data_x, data_y)
train(lr0)
train(lr1)
train(lr2)
train(lr3)
train(lr4)
assert lr0==lr1
assert lr0==lr2
assert lr0==lr3
assert lr0==lr4
def test_example_module2(self):
"""Test that the LogisticRegression2 module execute the same with different mode"""
import os
sys.path.append(os.path.realpath(".."))
import logistic_regression
lrc = logistic_regression.LogisticRegression2() #TODO: test 2==N
lr0 = lrc.make(10,1827)
lr1 = lrc.make(10, mode=theano.Mode('c|py', 'fast_run'), seed=1827)
lr2 = lrc.make(10, mode=theano.Mode('py', 'fast_run'), seed=1827)
lr3 = lrc.make(10, mode=theano.Mode('py', 'merge'), seed=1827) #'FAST_RU
lr4 = lrc.make(10, mode=compile.FAST_RUN.excluding('fast_run'), seed=1827)
#FAST_RUN, FAST_COMPILE,
data_x = N.random.randn(5, 10)
data_y = (N.random.randn(5) > 0)
data_y = data_y.reshape((data_y.shape[0],1))#need to be a column
def train(lr):
for i in xrange(1000):
lr.lr = 0.02
xe = lr.update(data_x, data_y)
train(lr0)
train(lr1)
train(lr2)
train(lr3)
train(lr4)
assert lr0==lr1
assert lr0==lr2
assert lr0==lr3
assert lr0==lr4
# self.fail("NotImplementedError")
if __name__ == '__main__':
from theano.tests import main
main(__file__)
examples/tests/test_wiki.py deleted 100644 → 0
浏览文件 @ cfecf720
差异被折叠。
theano/compile/profilemode.py
浏览文件 @ 618a8e25
......@@ -4,6 +4,7 @@ from theano.gof.link import WrapLinkerMany
from theano.gof.cutils import run_cthunk
from theano.compile.mode import Mode, register_mode, predefined_modes, predefined_linkers, predefined_optimizers, default_linker, default_optimizer
from theano.gof.cc import OpWiseCLinker
from theano.gof.python25 import any
from theano import gof
import theano.config as config
......
theano/sandbox/conv.py
浏览文件 @ 618a8e25
差异被折叠。
theano/sandbox/my_test_scan.py deleted 100644 → 0
浏览文件 @ cfecf720
import numpy
import theano
import theano.sandbox.scan
# generator network, only one output , type scalar ; no sequence or
# non sequence arguments
def test_1():
def f_pow2(x_tm1):
return (2*x_tm1, {})
s = theano.tensor.dvector()
n_steps = theano.tensor.dscalar()
Y = theano.sandbox.scan.scan(f_pow2, [],s, [],n_steps = n_steps)
f1 = theano.function([s,n_steps], Y)
assert( numpy.any(f1([1],3)== [2,4,8]) )
# simple rnn, one input, one state, weights for each; input/state are
# vectors, weights are scalars
def test_2():
def f_rnn(u_t,x_tm1,W_in, W):
return (u_t*W_in+x_tm1*W, {})
u = theano.tensor.dvector()
x0 = theano.tensor.dvector()
W_in = theano.tensor.dscalar()
W = theano.tensor.dscalar()
Y = theano.sandbox.scan.scan(f_rnn, u,x0,[W_in,W])
f2 = theano.function([u,x0,W_in,W], Y)
assert(numpy.any(f2([1,2,3,4],[1],.1,1)== numpy.array([1.1,1.3,1.6,2.])))
# simple rnn, one input, one state, weights for each; input/state are
# vectors, weights are scalars; using shared variables
def test_3():
u = theano.tensor.dvector()
x0 = theano.tensor.dvector()
W_in = theano.shared(.1, name = 'w_in')
W = theano.shared(1., name ='w')
def f_rnn_shared(u_t,x_tm1):
return (u_t*W_in+x_tm1*W, {})
Y = theano.sandbox.scan.scan(f_rnn_shared, u,x0,[])
f3 = theano.function([u,x0], Y)
assert(numpy.any(f3([1,2,3,4],[1])== numpy.array([1.1,1.3,1.6,2.])))
# some rnn with multiple outputs and multiple inputs; other dimension
# instead of scalars/vectors
def test_4():
W_in2 = theano.shared(numpy.array([1.,2.]), name='win2')
W = theano.shared(numpy.array([[2.,1.],[1.,1.]]), name='w')
W_out = theano.shared(numpy.array([.5,1.]), name = 'wout')
W_in1 = theano.tensor.dmatrix('win')
u1 = theano.tensor.dmatrix('u1')
u2 = theano.tensor.dvector('u2')
x0 = theano.tensor.dmatrix('x0')
y0 = theano.tensor.dvector('y0')
## Why dot doesn;t work with scalars !??
## Why * doesn't support SharedVariable and TensorVariable
def f_rnn_cmpl(u1_t, u2_t, x_tm1, y_tm1, W_in1):
return ({}, [theano.dot(u1_t,W_in1) + u2_t* W_in2 + \
theano.dot(x_tm1, W), theano.dot(x_tm1, W_out)])
Y = theano.sandbox.scan.scan(f_rnn_cmpl,[u1,u2],[x0,y0],W_in1)
f4 = theano.function([u1,u2,x0,y0,W_in1], Y)
(x,y) = f4( numpy.array([[1,2],[1,2],[1,2]]), \
numpy.array([1,2,3]), \
numpy.array([[0,0]]), \
numpy.array([1]), \
numpy.array([[1,1],[1,1]]))
assert( numpy.all(x == numpy.array([[4.,5.],[18.,16.],[58.,43.]])))
assert( numpy.all(y == numpy.array([0.,7.,25.])))
# basic ESN using updates
def test_5():
W_in = theano.shared(numpy.array([1.,1.]), name='win')
W = theano.shared(numpy.array([[.1,0.],[.0,.1]]),name='w')
W_out= theano.shared(numpy.array([.5,1.]), name='wout')
u = theano.tensor.dvector('u')
x = theano.shared(numpy.array([0.,0.]),'x')
y0 = theano.tensor.dvector('y0')
def f_ESN(u_t):
return ( theano.dot(x,W_out), \
{ x: W_in*u_t + theano.dot(x,W) } )
Y = theano.sandbox.scan.scan(f_ESN,u,y0,[],outputs_taps={0:[]})
f5 = theano.function([u,y0],Y)
assert( f5( numpy.array([1,2,3]), numpy.array([0])) == \
numpy.array([0.,1.4,3.15]))
# basic ESN using updates ; moving backwards
def test_6():
W_in = theano.shared(numpy.array([1.,1.]), name='win')
W = theano.shared(numpy.array([[.1,0.],[.0,.1]]),name='w')
W_out= theano.shared(numpy.array([.5,1.]), name='wout')
u = theano.tensor.dvector('u')
x = theano.shared(numpy.array([0.,0.]),'x')
y0 = theano.tensor.dvector('y0')
def f_ESN(u_t):
return ( theano.dot(x,W_out), \
{ x: W_in*u_t + theano.dot(x,W) } )
Y = theano.sandbox.scan.scan(f_ESN,u,y0,[],outputs_taps={0:[]}, \
go_backwards = True)
f6 = theano.function([u,y0],Y)
assert( f6( numpy.array([1,2,3]), numpy.array([0])) == \
numpy.array([0., 4.5, 3.45]))
'''
TO TEST:
- test taps (for sequences and outputs )
- test gradient (one output)
- test gradient (multiple outputs)
- test gradient (go_bacwards)
- test gradient (multiple outputs / some uncomputable )
- test gradient (truncate_gradient)
- test gradient (force_gradient)
- test inplace map
'''
if __name__=='__main__':
test_1()
test_2()
test_3()
test_4()
test_5()
test_6()
theano/sandbox/scan.py
浏览文件 @ 618a8e25
......@@ -13,29 +13,24 @@ def info(*msg):
_logger.info('INFO theano.scan: '+' '.join(msg))
# Hashing a list; list used by scan are list of numbers, therefore a list
# can be hashed by hashing all elements in the list
def hash_list(list):
# Hashing a dictionary or a list or a tuple or any type that is hashable with
# the hash() function
def hash_listsDictsTuples(x):
hash_value = 0
for v in list:
hash_value ^= hash(v)
return hash_value
# Hashing a dictionary; the dictionary used by scan has as keys numbers and
# as values either numbers or list of numbers
def hash_dict(dictionary):
hash_value = 0
for k,v in dictionary.iteritems():
# hash key
hash_value ^= hash(k)
if type(v) in (list,tuple):
hash_value ^= hash_list(v)
else:
hash_value ^= hash(v)
if type(x) == dict :
for k,v in x.iteritems():
hash_value ^= hash_listsDictsTuples(k)
hash_value ^= hash_listsDictsTuples(v)
elif type(x) in (list,tuple):
for v in x:
hash_value ^= hash_listsDictsTuples(v)
else:
try:
hash_value ^= hash(x)
except:
pass
return hash_value
def scan(fn, sequences, initial_states, non_sequences, inplace_map={},
sequences_taps={}, outputs_taps = {},
n_steps = theano.tensor.zero(), force_gradient = False,
......@@ -174,7 +169,8 @@ class Scan(theano.Op):
self.destroy_map = {}
if inplace:
self.destroy_map = inplace_map
for i in inplace_map.keys():
self.destroy_map.update({i: [inplace_map[i]] } )
self.seqs_taps = seqs_taps
self.outs_taps = outs_taps
......@@ -192,13 +188,25 @@ class Scan(theano.Op):
self.fn = theano.function(inputs,outputs, \
updates = updates, mode = mode)
g_y = [outputs[0].type()]
g_args = theano.tensor.grad(outputs[0],inputs, g_cost = g_y[-1])
def compute_gradient(y, g_y):
gmap = theano.gradient.grad_sources_inputs( \
[(y,g_y)], theano.gof.graph.inputs([y]), False)
def zero(p):
return theano.tensor.TensorConstant(theano.tensor.TensorType(\
dtype=p.type.dtype, broadcastable=[]),
numpy.asarray(0,dtype = p.type.dtype))
return [gmap.get(p, zero(p)) for p in inputs]
g_args = compute_gradient( outputs[0], g_y[-1])
# for all outputs compute gradients and then sum them up
for y in outputs[1:]:
g_y += [y.type()]
g_args_y = theano.tensor.grad(y,inputs, g_cost=g_y[-1])
g_args_y = compute_gradient( y,g_y[-1])
for i in xrange(len(g_args)):
g_args[i] += g_args_y[i]
......@@ -244,6 +252,7 @@ class Scan(theano.Op):
(self.n_outs == other.n_outs) and\
(self.n_args == other.n_args)
return rval
def __hash__(self):
return hash(type(self)) ^ \
......@@ -254,13 +263,13 @@ class Scan(theano.Op):
hash(self.go_backwards) ^\
hash(self.truncate_gradient) ^\
hash(self.n_args) ^ \
hash_list(self.outputs) ^ \
hash_list(self.inputs) ^ \
hash_list(self.g_ins) ^ \
hash_list(self.g_outs) ^ \
hash_dict(self.seqs_taps) ^\
hash_dict(self.outs_taps) ^\
hash_dict(self.updates)
hash_listsDictsTuples(self.outputs) ^ \
hash_listsDictsTuples(self.inputs) ^ \
hash_listsDictsTuples(self.g_ins) ^ \
hash_listsDictsTuples(self.g_outs) ^ \
hash_listsDictsTuples(self.seqs_taps) ^\
hash_listsDictsTuples(self.outs_taps) ^\
hash_listsDictsTuples(self.updates)
......
theano/sandbox/test_conv.py
浏览文件 @ 618a8e25
......@@ -121,7 +121,12 @@ def exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp, kshps, nkerns, unroll
hidval1=outval.copy()
# ConvOp
conv_op = ConvOp(imshp, kshp, nkern, bsize, ss[0],ss[1], conv_mode, unroll_batch=unroll_batch, unroll_kern=unroll_kern, unroll_patch=unroll_patch)(inputs4, kerns4)
if unroll_patch:
conv_op = ConvOp(dx=ss[0],dy=ss[1], output_mode=conv_mode,
unroll_patch=unroll_patch)(inputs4, kerns4)
else:
conv_op = ConvOp(imshp, kshp, nkern, bsize, ss[0],ss[1], conv_mode,
unroll_batch=unroll_batch, unroll_kern=unroll_kern, unroll_patch=unroll_patch)(inputs4, kerns4)
l1shp=N.hstack((nkern,
getFilterOutShp(imshp, kshp, ss, conv_mode)))
propup2 = function([inputs4, kerns4], conv_op)
......@@ -328,7 +333,7 @@ class TestConvOp(unittest.TestCase):
ssizess = [[(1,1),(1,2)],[(1,1),(2,2)]]
convmodes = ['valid','full']
do_convolve2=True
unroll = [(0,0,False),(0,0,True),(1,1,False),(2,2,False),(3,2,False)]#(batch,kern,patch)
unroll = [(0,0,True),(0,0,False),(1,1,False),(2,2,False),(3,2,False)]#(batch,kern,patch)
do_speed_test = False
# TODO: this version show a bug that was fixed
......@@ -515,23 +520,32 @@ class TestConvOp(unittest.TestCase):
for un_b,un_k, un_p in unroll:
for ss in ssizes:
print 'test_ConvOpGrad'
print 'mode type:', mode, typ
print 'imshp:', imshp
print 'kshp:', kshp
print 'un_b:', un_b
print 'un_k:', un_k
print 'ss:', ss
print 'bsize:', bsize
print 'nkern:', 4
# print 'mode:',mode,'type:', typ
# print 'imshp:', imshp,
# print 'kshp:', kshp
# print 'un_b:', un_b,
# print 'un_k:', un_k,
# print 'un_p:', un_p
# print 'ss:', ss,
# print 'bsize:', bsize,
# print 'nkern:', nkern
def test_i(imgs):
convop = ConvOp(imshp, kshp, nkern, bsize, ss[0], ss[1],
output_mode=mode, unroll_batch=un_b, unroll_kern=un_k, unroll_patch=un_p)
if un_p and ss[0]==1 and ss[1]==1:
convop = ConvOp(dx=ss[0], dy=ss[1],
output_mode=mode, unroll_patch=un_p)
else:
convop = ConvOp(imshp, kshp, nkern, bsize, ss[0], ss[1],
output_mode=mode, unroll_batch=un_b, unroll_kern=un_k, unroll_patch=un_p)
return convop(imgs, kernvals)
def test_k(kerns):
convop = ConvOp(imshp, kshp, nkern, bsize, ss[0], ss[1],
output_mode=mode, unroll_batch=un_b, unroll_kern=un_k, unroll_patch=un_p)
if un_p and ss[0]==1 and ss[1]==1:
convop = ConvOp(dx=ss[0], dy=ss[1],
output_mode=mode, unroll_patch=un_p)
else:
convop = ConvOp(imshp, kshp, nkern, bsize, ss[0], ss[1],
output_mode=mode, unroll_batch=un_b, unroll_kern=un_k, unroll_patch=un_p)
return convop(imgvals, kerns)
print mode, imshp, kshp, un_b, un_k, ss
#TODO the tolerance needed to pass is very high for float32(0.17). Is this acceptable? Expected?
......
theano/sandbox/test_scan.py
浏览文件 @ 618a8e25
......@@ -91,7 +91,6 @@ class T_Scan(unittest.TestCase):
utt.seed_rng()
# generator network, only one output , type scalar ; no sequence or
# non sequence arguments
def test_1(self):
......@@ -243,9 +242,11 @@ class T_Scan(unittest.TestCase):
Y = theano.sandbox.scan.scan(f_rnn_shared, u,x0, [], \
sequences_taps = {0:[-2]}, outputs_taps = {0:[-1,-2]})
f7 = theano.function([u,x0], Y)
#print f7([1,2,3,4],[1,2])
f7 = theano.function([u,x0], Y)
v_u = numpy.asarray([1.,2.,3.,4.])
v_x0 = numpy.asarray([1.,2.])
out = numpy.asarray([3.1,5.3])
assert (compareArrays( out, f7(v_u, v_x0)))
# simple rnn, one input, one state, weights for each; input/state are
# vectors, weights are scalars; using shared variables and past
......@@ -263,16 +264,46 @@ class T_Scan(unittest.TestCase):
Y = theano.sandbox.scan.scan(f_rnn_shared, u,x0, [], \
sequences_taps = {0:[-2,2]}, outputs_taps = {0:[-1,-2]})
f8 = theano.function([u,x0], Y)
f8 = theano.function([u,x0], Y)
v_u = numpy.array([1.,2.,3.,4.,5.,6.])
v_x0 = numpy.array([1.,2.])
out = numpy.array([3.6, 6.4])
assert (compareArrays( out, f8(v_u, v_x0) ) )
#print f8([1,2,3,4,5,6],[1,2])
'''
# simple rnn ; compute inplace
def test_9(self):
u = theano.tensor.dvector()
mu = theano.Param( u, mutable = True)
x0 = theano.tensor.dvector()
W_in = theano.shared(.1)
W = theano.shared(1.)
def f_rnn_shared(u_t, x_tm1):
return (u_t*W_in + x_tm1*W, {})
Y = theano.sandbox.scan.scan(f_rnn_shared, u, x0,[], \
inplace_map={0:0} )
f9 = theano.function([mu,x0], Y , #mode = 'FAST_RUN')
mode = 'DEBUG_MODE')
v_u = numpy.array([1.,2.,3.])
v_x0 = numpy.array([1.])
out = f9(v_u, v_x0)
v_out = numpy.array([1.1,1.3,1.6])
assert (compareArrays(out, v_out))
print v_u
assert (compareArrays(v_u, out))
'''
# test gradient simple network
def test_10(self):
pass
'''
TO TEST:
- test taps (for sequences and outputs )
- test gradient (one output)
- test gradient (multiple outputs)
- test gradient (go_bacwards)
......@@ -280,7 +311,6 @@ class T_Scan(unittest.TestCase):
- test gradient (truncate_gradient)
- test gradient (force_gradient)
- test_gradient (taps past/future)
- test inplace map
'''
......
theano/sparse/basic.py
浏览文件 @ 618a8e25
......@@ -343,12 +343,29 @@ class CSM(gof.Op):
"""
data = tensor.as_tensor_variable(data)
# Note that we use `view(numpy.int32)` in addition to providing the
# 'int32' dtype to `numpy.asarray`. This is because on some computers
# (e.g. a Windows 32 bits machine), we can have the following assert
# fail:
# x = numpy.array([0], dtype=numpy.intc)
# y = numpy.asarray(x, dtype=numpy.int32)
# assert y.dtype.num == numpy.dtype(numpy.int32).num
# while the assert does *not* fail when replacing the second line by:
# y = numpy.asarray(x, dtype='int32').view(numpy.int32)
# This is a known defect in Numpy. For more information see ticket
# http://projects.scipy.org/numpy/ticket/870
# Note also that it is important to keep "dtype='int32'" when calling
# `numpy.asarray`. This is because `view` is only some kind of cast to
# the exact data type we want to use. If a conversion is required (e.g.
# from int64 to int32), it must be done in the call to `numpy.asarray`.
if not isinstance(indices, tensor.TensorVariable):
indices = numpy.asarray(indices, dtype='int32')
indices = numpy.asarray(indices, dtype='int32').view(numpy.int32)
if not isinstance(indptr, tensor.TensorVariable):
indptr = numpy.asarray(indptr, dtype='int32')
indptr = numpy.asarray(indptr, dtype='int32').view(numpy.int32)
if not isinstance(shape, tensor.TensorVariable):
shape = numpy.asarray(shape, dtype='int32')
shape = numpy.asarray(shape, dtype='int32').view(numpy.int32)
indices = tensor.as_tensor_variable(indices)
indptr = tensor.as_tensor_variable(indptr)
shape = tensor.as_tensor_variable(shape)
......
theano/sparse/tests/test_basic.py
浏览文件 @ 618a8e25
......@@ -169,15 +169,17 @@ class test_structureddot(unittest.TestCase):
# iterate for a few different random graph patterns
for i in range(10):
spmat = sp.csc_matrix((4,6), dtype=sparse_dtype)
for i in range(5):
for k in range(5):
# set non-zeros in random locations (row x, col y)
x = numpy.floor(numpy.random.rand()*spmat.shape[0])
y = numpy.floor(numpy.random.rand()*spmat.shape[1])
spmat[x,y] = numpy.random.rand()*10
spmat = sp.csc_matrix(spmat)
kerns = tensor.Tensor(broadcastable=[False], dtype=sparse_dtype)('kerns')
images = tensor.Tensor(broadcastable=[False, False], dtype=dense_dtype)('images')
kerns = tensor.Tensor(broadcastable=[False],
dtype=sparse_dtype)('kerns')
images = tensor.Tensor(broadcastable=[False, False],
dtype=dense_dtype)('images')
output_dtype = theano.scalar.upcast(sparse_dtype, dense_dtype)
##
......@@ -186,7 +188,8 @@ class test_structureddot(unittest.TestCase):
# build symbolic theano graph
def buildgraphCSC(kerns,images):
csc = CSC(kerns, spmat.indices[:spmat.size], spmat.indptr, spmat.shape)
csc = CSC(kerns, spmat.indices[:spmat.size],
spmat.indptr, spmat.shape)
assert csc.type.dtype == sparse_dtype
rval = structured_dot(csc, images.T)
assert rval.type.dtype == output_dtype
......@@ -197,8 +200,12 @@ class test_structureddot(unittest.TestCase):
# compute theano outputs
kernvals = spmat.data[:spmat.size]
imvals = 1.0 + 1.0 * numpy.array(numpy.arange(bsize*spmat.shape[1]).\
imvals = 1.0 + 1.0 * numpy.array(
numpy.arange(bsize*spmat.shape[1]).\
reshape(bsize,spmat.shape[1]), dtype=dense_dtype)
#print('dense_dtype=%s' % dense_dtype)
#print('sparse_dtype=%s' % sparse_dtype)
#print('i=%s' % i)
print 'kerntype', str(kernvals.dtype), kernvals.dtype.num
outvals = f(kernvals,imvals)
print 'YAY'
......@@ -210,9 +217,10 @@ class test_structureddot(unittest.TestCase):
assert _is_dense(c)
assert str(outvals.dtype) == output_dtype
assert numpy.all(numpy.abs(outvals -
numpy.array(c, dtype=output_dtype)) < 1e-4)
numpy.array(c, dtype=output_dtype)) < 1e-4)
if sparse_dtype.startswith('float') and dense_dtype.startswith('float'):
if (sparse_dtype.startswith('float') and
dense_dtype.startswith('float')):
utt.verify_grad(buildgraphCSC,
[kernvals, imvals])
......
theano/tensor/__init__.py
浏览文件 @ 618a8e25
......@@ -12,6 +12,7 @@ import blas
import xlogx
import raw_random, randomstreams
import shared_randomstreams
from randomstreams import \
RandomStreams
......
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