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提交 dcbe40c8 authored 作者: Pierre Luc Carrier's avatar Pierre Luc Carrier 提交者： Arnaud Bergeron
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Remove compile.module as well as references to it and tests for it

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theano/__init__.py
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......@@ -58,15 +58,11 @@ from theano.compile import \
Mode, \
predefined_modes, predefined_linkers, predefined_optimizers, \
FunctionMaker, function, function_dump, OpFromGraph, \
Component, External, Member, Method, \
Composite, ComponentList, ComponentDict, Module, \
ProfileMode, ProfileStats, \
Param, shared, as_op
from theano.misc.safe_asarray import _asarray
FancyModule = Module
from theano.printing import pprint, pp
from theano.scan_module import scan, map, reduce, foldl, foldr, clone
......
theano/compile/__init__.py
浏览文件 @ dcbe40c8
......@@ -12,8 +12,6 @@ from theano.compile.mode import *
from theano.compile.io import *
from theano.compile.module import *
from theano.compile.debugmode import DebugMode
from theano.compile.monitormode import MonitorMode
......
theano/compile/module.py deleted 100644 → 0
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差异被折叠。
theano/compile/tests/test_inplace_opt_for_value.py deleted 100644 → 0
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#!/usr/bin/env python
import numpy as N
import theano
from theano import Op, Apply, tensor as T, Module, Method, Mode, compile
from theano.gof import OpSub, TopoOptimizer
from theano.printing import Print
from theano.tests import unittest_tools
####################
# Library-type stuff
####################
from theano.compile import module
from theano import tensor as T
class StochasticGradientDescent(module.FancyModule):
"""Fixed stepsize gradient descent"""
def __init__(self, args, cost, params, gradients=None, stepsize=None, WEIRD_STUFF=True):
"""
:param stepsize: the step to take in (negative) gradient direction
:type stepsize: None, scalar value, or scalar TensorVariable
"""
super(StochasticGradientDescent, self).__init__()
self.WEIRD_STUFF = WEIRD_STUFF
self.stepsize_init = None
if stepsize is None:
self.stepsize = (T.dscalar())
elif isinstance(stepsize, T.TensorVariable):
self.stepsize = stepsize
else:
if self.WEIRD_STUFF:
#TODO: why is this necessary? why does the else clause not work?
# self.stepsize = module.Member(T.dscalar(), init = stepsize)
self.stepsize = (T.dscalar())
self.stepsize_init = stepsize
else:
# self.stepsize = module.Member(T.value(stepsize))
self.stepsize = (T.constant(stepsize))#work!
if self.stepsize.ndim != 0:
raise ValueError('stepsize must be a scalar', stepsize)
self.params = params
if gradients is None:
self.gparams = T.grad(cost, self.params)
else:
self.gparams = gradients
self.updates = dict((p, p - self.stepsize * g) for p, g in zip(self.params, self.gparams))
self.step = module.Method(
args, [],
updates=self.updates)
self.step_cost = module.Method(
args, cost,
updates=self.updates)
def _instance_initialize(self, obj):
if self.WEIRD_STUFF:
obj.stepsize = self.stepsize_init
else:
pass
def sgd_minimizer(stepsize=None, **args):
def m(i,c,p,g=None):
return StochasticGradientDescent(i, c, p, stepsize=stepsize, **args)
return m
class TanhRnn(Op):
"""
This class implements the recurrent part of a recurrent neural network.
There is not a neat way to include this in a more fine-grained way in Theano at the moment,
so to get something working, I'm implementing a relatively complicated Op that could be
broken down later into constituents.
Anyway, this Op implements recursive computation of the form:
.. latex-eqn:
z_t &= \tanh( z_{t-1} A + x_{t-1})
For z0 a vector, and x a TxM matrix, it returns a matrix z of shape (T+1, M),
in which z[0] = z0.
"""
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
def make_node(self, x, z0, A):
"""
:type x: matrix (each row is an x_t) (shape: (T, M))
:type z0: vector (the first row of output) (shape: M)
:type A: matrix (M by M)
"""
x = T.as_tensor_variable(x)
z0 = T.as_tensor_variable(z0)
A = T.as_tensor_variable(A)
z = x.type() #make a new symbolic variable with the same type as x
return Apply(self, [x, z0, A], [z])
def perform(self, node, inp, out):
x, z0, A = inp
assert x is not None
assert z0 is not None
assert A is not None
T,M = x.shape
z = N.zeros((T+1, M))
z[0] = z0
for i in xrange(T):
z[i+1] = N.tanh(N.dot(z[i], A) + x[i])
out[0][0] = z
def grad(self, inp, grads):
x, z0, A = inp
gz, = grads
z = tanh_rnn(x, z0, A)
gz_incl_rnn, gx = tanh_rnn_grad(A, z, gz)
return [gx, gz_incl_rnn[0], (T.dot(z[:-1].T, gx))]
tanh_rnn = TanhRnn()
class TanhRnnGrad(Op):
"""Gradient calculation for TanhRnn"""
view_map = {0: [2]}
def __init__(self):
pass
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
def make_node(self, A, z, gz):
return Apply(self, [A,z,gz], (z.type(), gz.type()))
def perform(self, node, inp, out):
A, z, gz = inp
Tp1,M = z.shape
T = Tp1 - 1
gx = N.zeros((T, M))
for i in xrange(T-1, -1, -1):
#back through the tanh
gx[i] = gz[i+1] * (1.0 - z[i+1] * z[i+1])
out[0][0] = gz
out[1][0] = gx
def __str__(self):
return super(TanhRnnGrad, self).__str__()
tanh_rnn_grad = TanhRnnGrad()
#######################
# Experiment-type stuff
#######################
class ExampleRNN(Module):
def __init__(self, n_vis, minimizer):
super(ExampleRNN, self).__init__()
self.n_vis = n_vis
#recurrent weight matrix in latent space
self.z0 = (T.dvector())
self.w = (T.dmatrix())
self.params = [self.z0, self.w]
#input and target
x, y = T.dmatrix(), T.dmatrix()
z = tanh_rnn(x, self.z0, self.w)
self.cost = T.sum(z[1:])
# using the make_minimizer protocol
self.minimizer = minimizer([x, y], self.cost, self.params)
def _instance_initialize(self, obj):
#print 'INITIALIZE EXAMPLE RNN'
n_vis = self.n_vis
rng = N.random.RandomState(unittest_tools.fetch_seed(2342))
obj.z0 = N.zeros(n_vis)
obj.w = rng.randn(n_vis, n_vis) * 0.01
obj.minimizer.initialize()
def test_example_rnn():
minimizer_fn = sgd_minimizer(stepsize = 0.001)
n_vis = 5
n_out = 3
n_hid = 4
rnn_module = ExampleRNN(n_vis, minimizer_fn)
rnn = rnn_module.make()
rng = N.random.RandomState(unittest_tools.fetch_seed(7722342))
x = rng.randn(10,n_vis)
y = rng.randn(10,n_out)
#set y to be like x with a lag of LAG
LAG = 4
y[LAG:] = x[:-LAG, 0:n_out]
if 0:
for i, node in enumerate(rnn.minimizer.step_cost.maker.fgraph.toposort()):
print i, node
niter=1500
if theano.config.mode=='DEBUG_MODE':
niter=30
for i in xrange(niter):
rnn.minimizer.step_cost(x, y)
if theano.config.mode=='DEBUG_MODE':
assert rnn.minimizer.step_cost(x,y) < -.9 #it starts around -.28
else:
assert rnn.minimizer.step_cost(x,y) < -20 #it starts around -.28
def test_WEIRD_STUFF():
n_vis = 3
rng = N.random.RandomState(unittest_tools.fetch_seed(7722342))
x = rng.randn(10,n_vis)
y = rng.randn(10,n_vis)
#set y to be like x with a lag of LAG
LAG = 4
y[LAG:] = x[:-LAG, 0:n_vis]
minimizer_fn1 = sgd_minimizer(stepsize = 0.001, WEIRD_STUFF = False)
minimizer_fn2 = sgd_minimizer(stepsize = 0.001, WEIRD_STUFF = True)
rnn_module1 = ExampleRNN(n_vis, minimizer_fn1)
rnn_module2 = ExampleRNN(n_vis, minimizer_fn2)
rnn1 = rnn_module1.make(mode='FAST_RUN')
# rnn2 = rnn_module1.make(mode='FAST_COMPILE')#work
# rnn2 = rnn_module1.make(mode='FAST_RUN')#fail
rnn2 = rnn_module2.make(mode=Mode('c|py', 'fast_run'))#fail
# rnn2 = rnn_module1.make(mode=Mode('c|py', 'fast_run').excluding("inplace"))#work
# rnn2 = rnn_module1.make(mode=Mode('c|py', 'fast_compile'))#work
# rnn2 = rnn_module1.make(mode=Mode('py', 'fast_run_stable'))#work
# rnn2 = rnn_module1.make(mode=Mode('py', 'merge'))#work
# rnn2 = rnn_module1.make(mode=Mode('c|py', 'fast_run').excluding("inplace_opt"))#work
# rnn2 = rnn_module1.make(mode=Mode('py', 'fast_run'))#fail
m = Mode('py', 'fast_run')
# for n in m.optimizer: print n.name
if 0:
topo1=rnn1.minimizer.step_cost.maker.fgraph.toposort()
topo2=rnn2.minimizer.step_cost.maker.fgraph.toposort()
for i in range(len(topo1)):
print '1',i, topo1[i]
print '2',i, topo2[i]
if 0:
topo1=rnn1.minimizer.step.maker.fgraph.toposort()
topo2=rnn2.minimizer.step.maker.fgraph.toposort()
for i in range(len(topo1)):
print '1',i, topo1[i]
print '2',i, topo2[i]
import theano.printing
#print len(rnn1.minimizer.step.maker.inputs)
#print len(rnn2.minimizer.step.maker.inputs)
#print rnn1.minimizer.step.maker.inputs
#print rnn2.minimizer.step.maker.inputs
# for i in range(1,len(rnn1.minimizer.step.maker.inputs)):
# print "valid update:",theano.printing.pp(rnn1.minimizer.step.maker.inputs[i].update),
# print rnn1.minimizer.step.maker.inputs[i].update.name
# print "other update",theano.printing.pp(rnn2.minimizer.step.maker.inputs[i].update),
# print rnn2.minimizer.step.maker.inputs[i].update.name
# print dir(rnn1.minimizer.step.maker.inputs[5].update)
# print dir(rnn2.minimizer.step.maker.inputs[5].update)
niter=3
for i in xrange(niter):
#print rnn1.minimizer.step_cost(x, y)
#print rnn2.minimizer.step_cost(x, y)
# assert rnn1.n_vis != rnn2.n_vis or slef.n_hid != rnn2.n_hid or rnn1.n_out != rnn2.n_out
assert (N.abs(rnn1.z0-rnn2.z0)<1e-8).all()
#print (N.abs(rnn1.w-rnn2.w)<1e-8).all()
#print (N.abs(rnn1.w-rnn2.w))
#print rnn1.w
#print rnn2.w
assert (N.abs(rnn1.w-rnn2.w)<1e-8).all()
# assert b
if __name__ == '__main__':
# from theano.tests import main
# main(__file__)
# test_example_rnn()
test_WEIRD_STUFF()
theano/compile/tests/test_module.py deleted 100644 → 0
浏览文件 @ 238b38d1
差异被折叠。
theano/tensor/basic.py
浏览文件 @ dcbe40c8
......@@ -443,12 +443,6 @@ def _obj_is_wrappable_as_tensor(x):
return False
def _wrap_tensor_into_member(x):
return compile.module.Member(constant(x))
compile.module.register_wrapper(_obj_is_wrappable_as_tensor,
_wrap_tensor_into_member, no_warn=True)
if int(config.tensor.cmp_sloppy) > 1:
# This config variable is a quick-and-dirty way to get low-precision
# comparisons. For a more precise setting of these tolerances set
......
theano/tensor/tests/test_naacl09.py deleted 100644 → 0
浏览文件 @ 238b38d1
差异被折叠。
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