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提交 03e77233 authored 作者: Frédéric Bastien's avatar Frédéric Bastien
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Merge pull request #3095 from harlouci/flake8_v4

flake8 for tensor/nnet/nnet.py
上级 f4edcc59 9b457370
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theano/tensor/nnet/Conv3D.py
浏览文件 @ 03e77233
from __future__ import print_function
import numpy as N
from six.moves import xrange
import theano
from theano.tensor import basic as T
import numpy as N
#from util import strutil
# from util import strutil
from theano.tensor.blas_headers import blas_header_text, blas_header_version
from theano.tensor.blas import ldflags
from theano.misc import strutil
......@@ -72,26 +74,28 @@ class Conv3D(theano.Op):
def grad(self, inputs, output_gradients):
V, W, b, d = inputs
dCdH , = output_gradients
dCdH, = output_gradients
# make all of these ops support broadcasting of scalar b to vector b and eplace the zeros_like in all their grads
# print dCdH.broadcastable
# print "dCdH.broadcastable"
# quit(-1)
#dCdH = printing.Print("dCdH = ",["shape"])
# dCdH = printing.Print("dCdH = ",["shape"])
# Make sure the broadcasting pattern of the gradient is the the same
# as the initial variable
dCdV = ConvTransp3D.convTransp3D(W, T.zeros_like(V[0, 0, 0, 0, :]), d, dCdH, V.shape[1:4])
dCdV = theano.tensor.nnet.convTransp3D(
W, T.zeros_like(V[0, 0, 0, 0, :]), d, dCdH, V.shape[1:4])
dCdV = T.patternbroadcast(dCdV, V.broadcastable)
WShape = W.shape
dCdW = ConvGrad3D.convGrad3D(V, d, WShape, dCdH)
dCdW = theano.tensor.nnet.convGrad3D(V, d, WShape, dCdH)
dCdW = T.patternbroadcast(dCdW, W.broadcastable)
dCdb = T.sum(dCdH, axis=(0, 1, 2, 3))
dCdb = T.patternbroadcast(dCdb, b.broadcastable)
dCdd = grad_undefined(self, 3, inputs[3],
"The gradient of Conv3D with respect to the convolution" +\
" stride is undefined because Conv3D is only defined for" +\
" integer strides.")
dCdd = grad_undefined(
self, 3, inputs[3],
"The gradient of Conv3D with respect to the convolution"
" stride is undefined because Conv3D is only defined for"
" integer strides.")
if 'name' in dir(dCdH) and dCdH.name is not None:
dCdH_name = dCdH.name
......@@ -113,11 +117,13 @@ class Conv3D(theano.Op):
else:
b_name = 'anon_b'
dCdV.name = 'Conv3D_dCdV(dCdH='+dCdH_name+',V='+V_name+')'
dCdW.name = 'Conv3D_dCdW(dCdH='+dCdH_name+',V='+V_name+',W='+W_name+')'
dCdb.name = 'Conv3D_dCdb(dCdH='+dCdH_name+',V='+V_name+',W='+W_name+',b='+b_name+')'
dCdV.name = 'Conv3D_dCdV(dCdH=' + dCdH_name + ',V=' + V_name + ')'
dCdW.name = ('Conv3D_dCdW(dCdH=' + dCdH_name + ',V=' + V_name +
',W=' + W_name + ')')
dCdb.name = ('Conv3D_dCdb(dCdH=' + dCdH_name + ',V=' + V_name +
',W=' + W_name + ',b=' + b_name + ')')
return [ dCdV, dCdW, dCdb, dCdd ]
return [dCdV, dCdW, dCdb, dCdd]
def perform(self, node, inputs, output_storage):
V, W, b, d = inputs
......@@ -144,7 +150,7 @@ class Conv3D(theano.Op):
output_width = T.floor((vidWidth - filterWidth) // dc) + 1
output_dur = T.floor((vidDur - filterDur) // dt) + 1
rval = (batch_size, output_height, output_width, output_dur, output_channels )
rval = (batch_size, output_height, output_width, output_dur, output_channels)
return [rval]
......@@ -155,7 +161,7 @@ class Conv3D(theano.Op):
return ldflags()
def c_compile_args(self):
flags = ldflags(libs=False, flags=True)
flags = ldflags(libs=False, flags=True)
return flags
def c_lib_dirs(self):
......@@ -170,7 +176,7 @@ class Conv3D(theano.Op):
H = outputs[0]
codeSource = """
codeSource = """
///////////// < code generated by Conv3D >
//printf("\t\t\t\tConv3D c code\\n");
......@@ -320,13 +326,13 @@ class Conv3D(theano.Op):
VV, WV, bv, dv = node.inputs
HV = node.outputs[0]
if (theano.config.blas.ldflags and
VV.dtype == WV.dtype and HV.dtype == VV.dtype):
VV.dtype == WV.dtype and HV.dtype == VV.dtype):
if VV.dtype == 'float64':
gemv = 'dgemv_'
elif VV.dtype == 'float32':
gemv = 'sgemv_'
else:
raise Exception('Unrecognized dtype for convolution '+V.value.dtype)
raise Exception('Unrecognized dtype for convolution ' + V.value.dtype)
codeSource += """
if (inputChannels > 20 && outputChannels > 20 && ws4 == sizeof(ELEM_AT(%(W)s,0)))
......@@ -571,7 +577,7 @@ def computeH(V, W, b, d):
outputChannels = W.shape[0]
inputChannels = V.shape[4]
if W.shape[4] != inputChannels:
raise Exception("W.shape[4] = "+str(W.shape[4])+" but inputChannels = "+str(inputChannels))
raise Exception("W.shape[4] = " + str(W.shape[4]) + " but inputChannels = " + str(inputChannels))
filterHeight = W.shape[1]
filterWidth = W.shape[2]
filterDur = W.shape[3]
......@@ -586,12 +592,12 @@ def computeH(V, W, b, d):
assert dy > 0
assert dt > 0
outputHeight = int( (vidHeight - filterHeight) / dx )+1
outputWidth = int( (vidWidth - filterWidth) / dy )+1
outputDur = int( (vidDur - filterDur) / dt ) + 1
outputHeight = int((vidHeight - filterHeight) / dx) + 1
outputWidth = int((vidWidth - filterWidth) / dy) + 1
outputDur = int((vidDur - filterDur) / dt) + 1
H = N.zeros( (batchSize, outputHeight,
outputWidth, outputDur, outputChannels ), dtype=V.dtype )
H = N.zeros((batchSize, outputHeight,
outputWidth, outputDur, outputChannels), dtype=V.dtype)
# H[i,j,x,y,t] = b_j + sum_k sum_l sum_m sum_z W[j,z,k,l,m] V[i,z, dx*x+k,dy*y+l,dt*t+m]
for i in xrange(0, H.shape[0]):
......@@ -610,12 +616,8 @@ def computeH(V, W, b, d):
# if (i,j,x,y,t) == (0,0,0,0,0):
# print (( W[j,z,k,l,m] , V[i,z,d[0]*x+k,d[1]*y+l,d[2]*t+m] ), (k,l,m) )
w = W[j, k, l, m, z]
v = V[i, d[0]*x+k, d[1]*y+l, d[2]*t+m, z]
v = V[i, d[0] * x + k, d[1] * y + l, d[2] * t + m, z]
# if i == 0 and x == 0 and y == 0 and t == 0 and j == 0:
# print 'setting H[0] += '+str(w*v)+' W['+str((j,z,k,l,m))+']='+str(w)+' V['+str((i,d[0]*x+k,d[1]*y+l,d[2]*t+m,z))+']='+str(v)
H[i, x, y, t, j] += w * v
return H
from . import ConvGrad3D
from . import ConvTransp3D
theano/tensor/nnet/ConvGrad3D.py
浏览文件 @ 03e77233
from six.moves import xrange
import numpy as N
import theano
from theano.tensor import basic as T
from theano.misc import strutil
import numpy as N
from six.moves import xrange
from theano.gradient import grad_undefined
from theano.gradient import DisconnectedType
......@@ -23,11 +25,15 @@ class ConvGrad3D(theano.Op):
WShape_ = T.as_tensor_variable(WShape)
dCdH_ = T.as_tensor_variable(dCdH)
return theano.Apply(self, inputs=[V_, d_, WShape_, dCdH_], outputs=[ T.TensorType(V_.dtype, (False, False, False, False, False))() ] )
return theano.Apply(self,
inputs=[V_, d_, WShape_, dCdH_],
outputs=[T.TensorType(
V_.dtype,
(False, False, False, False, False))()])
def infer_shape(self, node, input_shapes):
V, d, W_shape, dCdH = node.inputs
return [ ( W_shape[0], W_shape[1], W_shape[2], W_shape[3], W_shape[4] ) ]
return [(W_shape[0], W_shape[1], W_shape[2], W_shape[3], W_shape[4])]
def connection_pattern(self, node):
......@@ -38,12 +44,12 @@ class ConvGrad3D(theano.Op):
dLdA, = output_gradients
z = T.zeros_like(C[0, 0, 0, 0, :])
dLdC = convTransp3D(dLdA, z, d, B, C.shape[1:4])
dLdC = theano.tensor.nnet.convTransp3D(dLdA, z, d, B, C.shape[1:4])
# d actually does affect the outputs, so it's not disconnected
dLdd = grad_undefined(self, 1, d)
# The shape of the weights doesn't affect the output elements
dLdWShape = DisconnectedType()()
dLdB = conv3D(C, dLdA, T.zeros_like(B[0, 0, 0, 0, :]), d)
dLdB = theano.tensor.nnet.conv3D(C, dLdA, T.zeros_like(B[0, 0, 0, 0, :]), d)
return [dLdC, dLdd, dLdWShape, dLdB]
......@@ -54,15 +60,10 @@ class ConvGrad3D(theano.Op):
# partial C / partial W[j,z,k,l,m] = sum_i sum_p sum_q sum_r (partial C /partial H[i,j,p,q,r] ) * V[i,z,dr*p+k,dc*q+l,dt*r+m]
batchSize = dCdH.shape[0]
outputFilters = dCdH.shape[4]
outputHeight = dCdH.shape[1]
outputWidth = dCdH.shape[2]
outputDur = dCdH.shape[3]
assert V.shape[0] == batchSize
inputFilters = V.shape[4]
inputHeight = V.shape[1]
inputWidth = V.shape[2]
inputDur = V.shape[3]
dr, dc, dt = d
dCdW = N.zeros(WShape, dtype=V.dtype)
......@@ -78,7 +79,10 @@ class ConvGrad3D(theano.Op):
for r in xrange(0, outputDur):
for j in xrange(0, WShape[0]):
for z in xrange(0, WShape[4]):
dCdW[j, k, l, m, z] += dCdH[i, p, q, r, j] * V[i, dr*p+k, dc*q+l, dt*r+m, z]
dCdW[j, k, l, m, z] += (
dCdH[i, p, q, r, j] *
V[i, dr * p + k, dc * q + l,
dt * r + m, z])
output_storage[0][0] = dCdW
......@@ -272,6 +276,3 @@ class ConvGrad3D(theano.Op):
convGrad3D = ConvGrad3D()
from theano.tensor.nnet.Conv3D import conv3D
from theano.tensor.nnet.ConvTransp3D import convTransp3D
theano/tensor/nnet/ConvTransp3D.py
浏览文件 @ 03e77233
from __future__ import print_function
import numpy as N
from six.moves import xrange
import theano
from theano.tensor import basic as T
from theano.misc import strutil
import theano
from theano.gradient import grad_undefined
from theano.gradient import DisconnectedType
......@@ -31,12 +33,15 @@ class ConvTransp3D(theano.Op):
else:
RShape_ = T.as_tensor_variable([-1, -1, -1])
return theano.Apply(self, inputs=[W_, b_, d_, H_, RShape_], outputs=[ T.TensorType(H_.dtype, (False, False, False, False, False))() ] )
return theano.Apply(self,
inputs=[W_, b_, d_, H_, RShape_],
outputs=[T.TensorType(H_.dtype,
(False, False, False, False, False))()])
def infer_shape(self, node, input_shapes):
W, b, d, H, RShape = node.inputs
W_shape, b_shape, d_shape, H_shape, RShape_shape = input_shapes
return [(H_shape[0], RShape[0], RShape[1], RShape[2], W_shape[4])]
return [(H_shape[0], RShape[0], RShape[1], RShape[2], W_shape[4])]
def connection_pattern(self, node):
return [[True], [True], [True], [True], [False]]
......@@ -44,9 +49,9 @@ class ConvTransp3D(theano.Op):
def grad(self, inputs, output_gradients):
W, b, d, H, RShape = inputs
dCdR, = output_gradients
dCdH = conv3D(dCdR, W, T.zeros_like(H[0, 0, 0, 0, :]), d)
dCdH = theano.tensor.nnet.conv3D(dCdR, W, T.zeros_like(H[0, 0, 0, 0, :]), d)
WShape = W.shape
dCdW = convGrad3D(dCdR, d, WShape, H)
dCdW = theano.tensor.nnet.convGrad3D(dCdR, d, WShape, H)
dCdb = T.sum(dCdR, axis=(0, 1, 2, 3))
# not differentiable, since d affects the output elements
dCdd = grad_undefined(self, 2, d)
......@@ -73,11 +78,13 @@ class ConvTransp3D(theano.Op):
else:
b_name = 'anon_b'
dCdW.name = 'ConvTransp3D_dCdW.H='+H_name+',dCdR='+dCdR_name+',W='+W_name
dCdb.name = 'ConvTransp3D_dCdb.H='+H_name+',dCdR='+dCdR_name+',W='+W_name+',b='+b_name
dCdW.name = ('ConvTransp3D_dCdW.H=' + H_name + ',dCdR=' + dCdR_name +
',W=' + W_name)
dCdb.name = ('ConvTransp3D_dCdb.H=' + H_name + ',dCdR=' + dCdR_name +
',W=' + W_name + ',b=' + b_name)
dCdH.name = 'ConvTransp3D_dCdH.H=' + H_name + ',dCdR=' + dCdR_name
return [dCdW, dCdb, dCdd, dCdH, dCdRShape]
return [dCdW, dCdb, dCdd, dCdH, dCdRShape]
def perform(self, node, inputs, output_storage):
W, b, d, H, RShape = inputs
......@@ -335,7 +342,7 @@ def computeR(W, b, d, H, Rshape=None):
assert len(b.shape) == 1
assert len(d) == 3
outputChannels, filterHeight, filterWidth, filterDur, \
outputChannels, filterHeight, filterWidth, filterDur, \
inputChannels = W.shape
batchSize, outputHeight, outputWidth, outputDur, \
outputChannelsAgain = H.shape
......@@ -367,7 +374,7 @@ def computeR(W, b, d, H, Rshape=None):
# print "video size: "+str((videoHeight, videoWidth, videoDur))
R = N.zeros((batchSize, videoHeight,
videoWidth, videoDur, inputChannels), dtype=H.dtype)
videoWidth, videoDur, inputChannels), dtype=H.dtype)
# R[i,j,r,c,t] = b_j + sum_{rc,rk | d \circ rc + rk = r} sum_{cc,ck | ...} sum_{tc,tk | ...} sum_k W[k, j, rk, ck, tk] * H[i,k,rc,cc,tc]
for i in xrange(0, batchSize):
......@@ -404,8 +411,8 @@ def computeR(W, b, d, H, Rshape=None):
if tk < 0:
break
R[
i, r, c, t, j] += N.dot(W[:, rk, ck, tk, j], H[i, rc, cc, tc, :] )
R[i, r, c, t, j] += N.dot(
W[:, rk, ck, tk, j], H[i, rc, cc, tc, :])
tc += 1
"" # close loop over tc
......@@ -421,7 +428,3 @@ def computeR(W, b, d, H, Rshape=None):
"" # close loop over i
return R
from theano.tensor.nnet.Conv3D import conv3D
from theano.tensor.nnet.ConvGrad3D import convGrad3D
theano/tensor/nnet/conv.py
浏览文件 @ 03e77233
from __future__ import print_function
"""
Contains an Op for convolving input images with a set of filters. This was
developed especially for Convolutional Neural Networks.
......@@ -9,7 +8,7 @@ tensor.signal and tensor.signal.downsample.
See especially conv2d().
"""
__docformat__ = "restructuredtext en"
from __future__ import print_function
import logging
......@@ -17,12 +16,11 @@ import numpy
from six.moves import xrange
import theano
from theano import OpenMPOp
from theano.tensor import (as_tensor_variable, blas, get_scalar_constant_value,
patternbroadcast, NotScalarConstantError)
from theano import OpenMPOp, config
from theano.gof import Apply
imported_scipy_signal = False
try:
# TODO: move these back out to global scope when they no longer
# cause an atexit error
......@@ -30,8 +28,9 @@ try:
from scipy.signal.sigtools import _convolve2d
imported_scipy_signal = True
except ImportError:
pass
imported_scipy_signal = False
__docformat__ = "restructuredtext en"
_logger = logging.getLogger("theano.tensor.nnet.conv")
......@@ -103,7 +102,7 @@ def conv2d(input, filters, image_shape=None, filter_shape=None,
try:
image_shape[i] = get_scalar_constant_value(
as_tensor_variable(image_shape[i]))
except NotScalarConstantError as e:
except NotScalarConstantError:
raise NotScalarConstantError(
"The convolution need that the shape"
" information are constant values. We got"
......@@ -118,7 +117,7 @@ def conv2d(input, filters, image_shape=None, filter_shape=None,
try:
filter_shape[i] = get_scalar_constant_value(
as_tensor_variable(filter_shape[i]))
except NotScalarConstantError as e:
except NotScalarConstantError:
raise NotScalarConstantError(
"The convolution need that the shape"
" information are constant values. We got"
......@@ -267,9 +266,9 @@ class ConvOp(OpenMPOp):
# with s=1 for mode=='full' and s=-1 for mode=='valid'.
# To support symbolic shapes, we express this with integer arithmetics.
return tuple(None if i is None or k is None
else ((i - k) // d + 1) if mode == 'valid'
else ((i + k + d - 2) // d)
for i, k, d in zip(inshp, kshp, stride))
else ((i - k) // d + 1) if mode == 'valid'
else ((i + k + d - 2) // d)
for i, k, d in zip(inshp, kshp, stride))
def __init__(self, imshp=None, kshp=None, nkern=None, bsize=None,
dx=1, dy=1,
......@@ -402,11 +401,11 @@ class ConvOp(OpenMPOp):
if dy is None:
dy = 1
if int(dx) != dx:
if int(dx) != dx:
raise TypeError('ConvOp.__init__ param dx must be an int', dx)
dx = int(dx)
if int(dy) != dy:
if int(dy) != dy:
raise TypeError('ConvOp.__init__ param dy must be an int', dy)
dy = int(dy)
......@@ -509,7 +508,7 @@ class ConvOp(OpenMPOp):
self.out_mode = output_mode
if not self.out_mode in ["valid", "full"]:
if self.out_mode not in ["valid", "full"]:
raise Exception("Mode %s not implemented" % self.out_mode)
if any((shp is not None) and (shp <= 0) for shp in self.outshp):
......@@ -520,9 +519,8 @@ class ConvOp(OpenMPOp):
(self.imshp_logical, self.kshp_logical))
if (self.unroll_kern is None and
self.unroll_batch is None and
self.unroll_patch is None):
self.unroll_batch is None and
self.unroll_patch is None):
# no version specified. Find the faster we have
if self.bsize is None and self.nkern is None:
self.unroll_patch = True
......@@ -540,7 +538,7 @@ class ConvOp(OpenMPOp):
time_unroll_batch_kern = 9999999
for i in xrange(len(self.speed_unroll_batch_kern)):
if (bsize % self.speed_unroll_batch_kern[i][0] == 0 and
nkern % self.speed_unroll_batch_kern[i][1] == 0):
nkern % self.speed_unroll_batch_kern[i][1] == 0):
if self.speed_unroll_batch_kern[i][2 + mode_idx] < time_unroll_batch_kern:
time_unroll_batch_kern = self.speed_unroll_batch_kern[i][2 + mode_idx]
time_unroll_batch_kern_idx = i
......@@ -613,7 +611,6 @@ class ConvOp(OpenMPOp):
inputs - 4 dim: batches x stacksize x rows x cols
kerns - 4 dim: nkern x stackidx x rows x cols
"""
outdim = kerns.ndim
_inputs = as_tensor_variable(inputs)
_kerns = as_tensor_variable(kerns)
# TODO: lift this restriction by upcasting either inputs or kerns
......@@ -631,7 +628,7 @@ class ConvOp(OpenMPOp):
output = theano.tensor.tensor(dtype=_inputs.type.dtype,
broadcastable=[_inputs.broadcastable[0],
_kerns.broadcastable[0]] +
bcastable23)
bcastable23)
return Apply(self, [_inputs, _kerns], [output])
......@@ -778,7 +775,7 @@ class ConvOp(OpenMPOp):
img2d2[:, :, kshp[0] - 1:kshp[0] - 1 + imshp[1],
kshp[1] - 1:kshp[1] - 1 + imshp[2]] = img2d
img2d = img2d2
#N_image_shape = image_data.shape
# N_image_shape = image_data.shape
for b in xrange(bsize):
for n in xrange(nkern):
......@@ -786,8 +783,10 @@ class ConvOp(OpenMPOp):
for im0 in xrange(stacklen):
for row in xrange(0, zz.shape[2], self.dx):
for col in xrange(0, zz.shape[3], self.dy):
zz[b, n, row, col] += (img2d[b, im0, row:row + kshp[0], col:col + kshp[1]] *
filtersflipped[n, im0, ::-1, ::-1]).sum()
zz[b, n, row, col] += (
img2d[b, im0, row:row + kshp[0],
col:col + kshp[1]] *
filtersflipped[n, im0, ::-1, ::-1]).sum()
# We copy it to remove the Stride mismatch warning from DEBUG_MODE.
# The copy make that we return an object with the same stride as the c version.
......@@ -843,8 +842,8 @@ class ConvOp(OpenMPOp):
# mimic what happens inside theano.grad: get the input gradient
# of the final cost wrt all variables involved.
return theano.gradient.grad(cost=None,
known_grads={node: gz}, wrt=[inputs, kerns])
return theano.gradient.grad(cost=None, known_grads={node: gz},
wrt=[inputs, kerns])
if self.dx not in (1, 2) or self.dy not in (1, 2):
raise NotImplementedError(
......@@ -858,7 +857,7 @@ class ConvOp(OpenMPOp):
raise Exception("ConvOp.grad when dx!=1 or dy!=1 we must have all "
"the optional shape information")
####### Determine gradient on kernels ########
# Determine gradient on kernels ########
assert inputs.ndim == 4 and kerns.ndim == 4
newin = inputs.dimshuffle((1, 0, 2, 3))
......@@ -943,7 +942,7 @@ class ConvOp(OpenMPOp):
dw = dw.dimshuffle((1, 0, 2, 3))
dw = dw[:, :, ::-1, ::-1]
####### Determine gradient on inputs ########
# Determine gradient on inputs ########
mode = 'valid'
if not self.out_mode == 'full':
mode = 'full'
......@@ -1011,11 +1010,10 @@ using namespace std;
if self.out_mode == 'valid' and self.dx == 0 and self.dy == 0:
# We use a faster version in those case.
if (self.imshp != self.imshp_logical or
self.kshp != self.kshp_logical or
self.unroll_patch or
self.unroll_batch > 0 or
self.unroll_kern > 0):
self.kshp != self.kshp_logical or
self.unroll_patch or
self.unroll_batch > 0 or
self.unroll_kern > 0):
return False
return True
return False
......@@ -1029,8 +1027,7 @@ using namespace std;
# when the ksph==(1,1) gcc 4.3.0 segfault during the
# compilation with -O3. This don't happen at -O2
if (theano.gof.cmodule.gcc_version() in ['4.3.0'] and
self.kshp == (1, 1)):
self.kshp == (1, 1)):
return ['-O3']
else:
return []
......@@ -1041,7 +1038,7 @@ using namespace std;
if self.use_blas():
ret = blas.ldflags(libs=False, flags=True)
if (theano.gof.cmodule.gcc_version() in ['4.3.0'] and
self.kshp == (1, 1)):
self.kshp == (1, 1)):
ret += ['-O2']
# Add the -fopenmp flags
ret += super(ConvOp, self).c_compile_args()
......@@ -1068,7 +1065,7 @@ using namespace std;
d.update(sub)
all_shape = (self.has_all_shape(self.imshp, self.kshp,
self.nkern, self.bsize) and
self.nkern, self.bsize) and
self.has_all_shape(self.imshp_logical, self.kshp_logical))
d["self_out_mode"] = self.out_mode
......@@ -1228,9 +1225,9 @@ if(%(value)s != %(expected)s){
d["self_kshp_logical_stride_c"] = int(numpy.ceil(
self.kshp_logical[1] / float(self.kshp[1])))
d["self_imshp_logical_r"] = self.imshp_logical[1]
# numpy.B. 1 not 0
# numpy.B. 1 not 0
d["self_imshp_logical_c"] = self.imshp_logical[2]
# numpy.B. 2 not 1
# numpy.B. 2 not 1
d["self_imshp_logical_stride_r"] = int(numpy.ceil(
self.imshp_logical[1] / float(self.imshp[1])))
d["self_imshp_logical_stride_c"] = int(numpy.ceil(
......@@ -1300,7 +1297,7 @@ if(kerns_dim[1] != img2d_dim[1]){
all_shape)
return _conv_op_code_unroll_patch % d
if ((self.unroll_batch is not None and self.unroll_batch > 0) or
(self.unroll_kern is not None and self.unroll_kern > 0)):
(self.unroll_kern is not None and self.unroll_kern > 0)):
assert self.unroll_batch > 0
assert self.unroll_kern > 0
if self.verbose:
......
theano/tensor/nnet/conv3d2d.py
浏览文件 @ 03e77233
......@@ -194,13 +194,13 @@ def conv3d(signals, filters,
_signals_shape_5d[2],
_signals_shape_5d[3],
_signals_shape_5d[4],
)
)
_filters_shape_4d = (
_filters_shape_5d[0] * _filters_shape_5d[1],
_filters_shape_5d[2],
_filters_shape_5d[3],
_filters_shape_5d[4],
)
)
if border_mode[1] != border_mode[2]:
raise NotImplementedError('height and width bordermodes must match')
......@@ -228,7 +228,7 @@ def conv3d(signals, filters,
_filters_shape_5d[1], # Tf
_signals_shape_5d[3] - _filters_shape_5d[3] + 1,
_signals_shape_5d[4] - _filters_shape_5d[4] + 1,
))
))
elif border_mode[1] == 'full':
out_tmp = out_4d.reshape((
_signals_shape_5d[0], # Ns
......@@ -237,7 +237,7 @@ def conv3d(signals, filters,
_filters_shape_5d[1], # Tf
_signals_shape_5d[3] + _filters_shape_5d[3] - 1,
_signals_shape_5d[4] + _filters_shape_5d[4] - 1,
))
))
elif border_mode[1] == 'same':
raise NotImplementedError()
else:
......@@ -246,15 +246,15 @@ def conv3d(signals, filters,
# now sum out along the Tf to get the output
# but we have to sum on a diagonal through the Tf and Ts submatrix.
if border_mode[0] == 'valid':
if _filters_shape_5d[1]!=1:
out_5d = diagonal_subtensor(out_tmp, 1, 3).sum(axis=3)
else: # for Tf==1, no sum along Tf, the Ts-axis of the output is unchanged!
out_5d = out_tmp.reshape((
_signals_shape_5d[0],
_signals_shape_5d[1],
_filters_shape_5d[0],
_signals_shape_5d[3] - _filters_shape_5d[3] + 1,
_signals_shape_5d[4] - _filters_shape_5d[4] + 1,
if _filters_shape_5d[1] != 1:
out_5d = diagonal_subtensor(out_tmp, 1, 3).sum(axis=3)
else: # for Tf==1, no sum along Tf, the Ts-axis of the output is unchanged!
out_5d = out_tmp.reshape((
_signals_shape_5d[0],
_signals_shape_5d[1],
_filters_shape_5d[0],
_signals_shape_5d[3] - _filters_shape_5d[3] + 1,
_signals_shape_5d[4] - _filters_shape_5d[4] + 1,
))
elif border_mode[0] in ('full', 'same'):
raise NotImplementedError('sequence border mode', border_mode[0])
......@@ -316,7 +316,7 @@ if cuda.cuda_available:
def local_inplace_DiagonalSubtensor(node):
""" also work for IncDiagonalSubtensor """
if (isinstance(node.op, (DiagonalSubtensor, IncDiagonalSubtensor)) and
not node.op.inplace):
not node.op.inplace):
new_op = node.op.__class__(inplace=True)
new_node = new_op(*node.inputs)
return [new_node]
......
theano/tensor/nnet/neighbours.py
浏览文件 @ 03e77233
......@@ -2,15 +2,15 @@
TODO: implement Images2Neibs.infer_shape() methods
"""
from six.moves import xrange
import numpy
import theano
from theano import Op, Apply
import theano.tensor as T
from theano.gradient import grad_not_implemented
from theano.gradient import grad_undefined
import numpy
class Images2Neibs(Op):
......@@ -206,7 +206,7 @@ class Images2Neibs(Op):
z_col = j + d * i
z[0][z_row, z_col] = ten4[n, s, ten4_2, ten4_3]
def infer_shape(self, node, input_shape):
in_shape = input_shape[0]
c, d = node.inputs[1]
......@@ -223,7 +223,7 @@ class Images2Neibs(Op):
z_dim0 = grid_c * grid_d * in_shape[1] * in_shape[0]
z_dim1 = c * d
return [(z_dim0, z_dim1)]
def c_code(self, node, name, inp, out, sub):
ten4, neib_shape, neib_step = inp
z, = out
......@@ -417,21 +417,21 @@ class Images2Neibs(Op):
def images2neibs(ten4, neib_shape, neib_step=None, mode='valid'):
"""
"""
Function :func:`images2neibs <theano.sandbox.neighbours.images2neibs>`
allows to apply a sliding window operation to a tensor containing
allows to apply a sliding window operation to a tensor containing
images
or other two-dimensional objects.
The sliding window operation loops
over points in input data and stores a rectangular neighbourhood of
each point.
It is possible to assign a step of selecting patches (parameter
`neib_step`).
:param ten4: A 4-dimensional tensor which represents
or other two-dimensional objects.
The sliding window operation loops
over points in input data and stores a rectangular neighbourhood of
each point.
It is possible to assign a step of selecting patches (parameter
`neib_step`).
:param ten4: A 4-dimensional tensor which represents
a list of lists of images.a list of lists of images.
It should have shape (list 1 dim, list 2 dim,
row, col). The first two dimensions can be
row, col). The first two dimensions can be
useful to store different channels and batches.
:type ten4: A 4d tensor-like.
:param neib_shape: A tuple containing two
......@@ -442,20 +442,20 @@ def images2neibs(ten4, neib_shape, neib_step=None, mode='valid'):
:type neib_shape: A 1d tensor-like of 2 values.
:param neib_step: (dr,dc) where dr is the number of rows to
skip between patch and dc is the number of
columns. The parameter should be a tuple of two elements:
number
of rows and number of columns to skip each iteration.
columns. The parameter should be a tuple of two elements:
number
of rows and number of columns to skip each iteration.
Basically, when the step is 1, the neighbourhood of every
first element is taken and every possible rectangular
first element is taken and every possible rectangular
subset is returned. By default it is equal to
`neib_shape` in other words, the
patches are disjoint. When the step is greater than
patches are disjoint. When the step is greater than
`neib_shape`, some elements are omitted. When None, this
is the same as
neib_shape(patch are disjoint)
.. note:: Currently the step size should be chosen in the way that the
corresponding dimension :math:`i` (width or height) is equal to
.. note:: Currently the step size should be chosen in the way that the
corresponding dimension :math:`i` (width or height) is equal to
:math:`n * step\_size_i + neib\_shape_i` for some :math:`n`
:type neib_step: A 1d tensor-like of 2 values.
:param mode:
......@@ -489,29 +489,29 @@ def images2neibs(ten4, neib_shape, neib_step=None, mode='valid'):
= flattened version of ten4[i,j,l:l+r,k:k+c]
idx += 1
.. note:: The operation isn't necessarily implemented internally with
these for loops, they're just the easiest way to describe the
.. note:: The operation isn't necessarily implemented internally with
these for loops, they're just the easiest way to describe the
output pattern.
Example:
.. code-block:: python
# Defining variables
images = T.tensor4('images')
neibs = images2neibs(images, neib_shape=(5, 5))
# Constructing theano function
# Constructing theano function
window_function = theano.function([images], neibs)
# Input tensor (one image 10x10)
im_val = np.arange(100.).reshape((1, 1, 10, 10))
# Function application
neibs_val = window_function(im_val)
.. note:: The underlying code will construct a 2D tensor of disjoint
patches 5x5. The output has shape 4x25.
.. note:: The underlying code will construct a 2D tensor of disjoint
patches 5x5. The output has shape 4x25.
"""
return Images2Neibs(mode)(ten4, neib_shape, neib_step)
......@@ -524,25 +524,24 @@ def neibs2images(neibs, neib_shape, original_shape, mode='valid'):
the output of :func:`images2neibs <theano.sandbox.neigbours.neibs2images>`
and reconstructs its input.
:param neibs: matrix like the one obtained by
:param neibs: matrix like the one obtained by
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`
:param neib_shape: `neib_shape` that was used in
:param neib_shape: `neib_shape` that was used in
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`
:param original_shape: original shape of the 4d tensor given to
:param original_shape: original shape of the 4d tensor given to
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`
:return: Reconstructs the input of
:return: Reconstructs the input of
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`,
a 4d tensor of shape `original_shape`.
.. note:: Currently, the function doesn't support tensors created with
`neib_step` different from default value. This means that it may be
impossible to compute the gradient of a variable gained by
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>` w.r.t.
its inputs in this case, because it uses
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>` for
impossible to compute the gradient of a variable gained by
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>` w.r.t.
its inputs in this case, because it uses
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>` for
gradient computation.
Example, which uses a tensor gained in example for
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`:
......
theano/tensor/nnet/nnet.py
浏览文件 @ 03e77233
......@@ -15,6 +15,7 @@ from six.moves import xrange
import theano
from theano import gof
from theano import scalar
from theano.tensor import basic as tensor
from theano.tensor import subtensor
from theano.tensor import elemwise
......@@ -27,12 +28,12 @@ from theano.gradient import DisconnectedType
from theano.gradient import grad_not_implemented
from theano.tensor.type import values_eq_approx_remove_nan
############
#
# TENSOR OPS
#
class SoftmaxWithBias(gof.Op):
"""
An L{Op} for the output of neural-net multiclass classifiers.
......@@ -299,13 +300,13 @@ class SoftmaxGrad(gof.Op):
def grad(self, inp, grads):
dy, sm = inp
g, = grads
tmp = g + tensor.neg(tensor.sum(g*sm, axis=1).dimshuffle((0, 'x')))
tmp = g + tensor.neg(tensor.sum(g * sm, axis=1).dimshuffle((0, 'x')))
g_dy = tmp * sm
tmp2 = tensor.sum(dy*sm, axis=1).dimshuffle((0, 'x'))
g_sm = tmp*dy - g *tmp2
tmp2 = tensor.sum(dy * sm, axis=1).dimshuffle((0, 'x'))
g_sm = tmp * dy - g * tmp2
return g_dy, g_sm
def infer_shape(self, node, shape):
......@@ -571,12 +572,15 @@ class Softmax(gof.Op):
softmax_op = Softmax()
def softmax_graph(c):
return tensor.exp(c) / tensor.exp(c).sum(axis=-1, keepdims=True)
def softmax(c):
return softmax_op(c)
@opt.register_specialize('fast_compile_gpu')
@gof.local_optimizer([softmax_op])
def local_softmax_with_bias(node):
......@@ -593,15 +597,15 @@ def local_softmax_with_bias(node):
# tensor.DimShuffle) since specialization comes
# relatively late in optimization, we don't want to
# put in extra DimShuffles un-necessarily.
if (x_in.owner and isinstance(x_in.owner.op,
tensor.DimShuffle)
and list(x_in.owner.inputs[0].type.broadcastable) == [False]):
if (x_in.owner and
isinstance(x_in.owner.op, tensor.DimShuffle) and
list(x_in.owner.inputs[0].type.broadcastable) == [False]):
# cut out the DimShuffle that was broadcasting a vector
vectors.append(x_in.owner.inputs[0])
else:
# insert an extra DimShuffle to correct the old one
vectors.append(tensor.
DimShuffle((True, False), (1,))(x_in))
DimShuffle((True, False), (1,))(x_in))
else:
non_vectors.append(x_in)
......@@ -658,7 +662,7 @@ def softmax_simplifier(numerators, denominators):
tensor.DimShuffle):
if denominator.owner.op.new_order == (0, 'x'):
z = denominator.owner.inputs[0]
# thing getting dimshuffled
# thing getting dimshuffled
if z.owner and isinstance(z.owner.op, tensor.Sum):
# print 'ASDF', denominator.owner.op.new_order
# print z.owner.op.axis
......@@ -673,8 +677,7 @@ def softmax_simplifier(numerators, denominators):
numerators.append(softmax_op(x))
return numerators, denominators
opt.local_mul_canonizer.add_simplifier(softmax_simplifier,
'softmax_simplifier')
opt.local_mul_canonizer.add_simplifier(softmax_simplifier, 'softmax_simplifier')
if 0:
@opt.register_specialize
......@@ -694,11 +697,11 @@ if 0:
# First, prod_term
for add_in in add_inputs:
if (add_in.owner and
add_in.owner.op == tensor.mul and
prod_term is None):
add_in.owner.op == tensor.mul and
prod_term is None):
mul_inputs = add_in.owner.inputs
if (len(mul_inputs) == 2 and
all([mul_in.ndim == 2 for mul_in in mul_inputs])):
all([mul_in.ndim == 2 for mul_in in mul_inputs])):
prod_term = add_in
else:
other_terms.append(add_in)
......@@ -724,16 +727,16 @@ if 0:
maybe_ds = None
for i, mul2_in in enumerate(mul2_inputs):
if mul2_in.owner and isinstance(mul2_in.owner.op,
elemwise.DimShuffle):
elemwise.DimShuffle):
maybe_ds = mul2_in
maybe_sm = mul2_inputs[1 - i] # The other one
if (maybe_ds is None or
maybe_ds.ndim != 2 or
maybe_sm.ndim != 2):
maybe_ds.ndim != 2 or
maybe_sm.ndim != 2):
rest.append(add_in)
# print 'maybe_ds =', maybe_ds
# if maybe_ds:
# print 'maybe_ds.ndim =', maybe_ds.ndim, ', maybe_sm.ndim =', maybe_sm.ndim
# print 'maybe_ds.ndim =', maybe_ds.ndim, ', maybe_sm.ndim =', maybe_sm.ndim
continue
if maybe_sm is mul_inputs[0]:
......@@ -755,8 +758,8 @@ if 0:
sum_input = ds_input.owner.inputs[0]
if ((ds_order != (0, 'x')) or
(axis != (1,)) or
(sum_input is not prod_term)):
(axis != (1,)) or
(sum_input is not prod_term)):
rest.append(add_in)
# print 'ds_order =', ds_order
# print 'axis =', axis
......@@ -816,7 +819,7 @@ class CrossentropySoftmaxArgmax1HotWithBias(gof.Op):
nin = 3
nout = 3
__props__ = ()
def __init__(self, **kwargs):
gof.Op.__init__(self, **kwargs)
......@@ -836,7 +839,7 @@ class CrossentropySoftmaxArgmax1HotWithBias(gof.Op):
# TODO: Is this correct? It used to be y, not y_idx
nll = tensor.TensorType(x.type.dtype,
y_idx.type.broadcastable)()
y_idx.type.broadcastable).make_variable()
# nll = TensorType(x.dtype, y.broadcastable)
sm = x.type()
am = y_idx.type()
......@@ -866,15 +869,14 @@ class CrossentropySoftmaxArgmax1HotWithBias(gof.Op):
if any(y_idx < 0):
raise ValueError("y_i value out of bounds")
sm = numpy.zeros_like(x) # softmax
nll = numpy.zeros(x.shape[0], dtype=node.outputs[0].type.
dtype) # nll(y | softmax(x))
nll = numpy.zeros(x.shape[0], dtype=node.outputs[0].type.dtype) # nll(y | softmax(x))
am = numpy.zeros_like(y_idx)
for i in xrange(sm.shape[0]):
# add the bias vector to the i'th row of x
row = x[i] + b
# get the maximum value of i'th row for numerically safe
#softmax / nll
# softmax / nll
am[i] = numpy.argmax(row)
m = row[am[i]]
......@@ -956,7 +958,7 @@ class CrossentropySoftmaxArgmax1HotWithBias(gof.Op):
# TODO: use this to accept float32 and int32: node.inputs[0].type.dtype_specs()[1]
(init_decl, begin_row_loop, inside_row_loop, end_row_loop) = \
SoftmaxWithBias.c_code_template(dtype)
SoftmaxWithBias.c_code_template(dtype)
return (init_decl,
"""
if (PyArray_NDIM(%(y_idx)s) != 1)
......@@ -1038,7 +1040,7 @@ class CrossentropySoftmax1HotWithBiasDx(gof.Op):
nin = 3
nout = 1
__props__ = ()
"""Gradient wrt x of the CrossentropySoftmaxArgmax1HotWithBias Op"""
def make_node(self, dy, sm, y_idx, **kwargs):
......@@ -1046,13 +1048,13 @@ class CrossentropySoftmax1HotWithBiasDx(gof.Op):
sm = tensor.as_tensor_variable(sm)
y_idx = tensor.as_tensor_variable(y_idx)
if (dy.type.ndim > 1 or
dy.type.dtype not in tensor.float_dtypes):
dy.type.dtype not in tensor.float_dtypes):
raise ValueError('dy must be {0,1}-d tensor of floats', dy.type)
if (sm.type.ndim != 2 or
sm.type.dtype not in tensor.float_dtypes):
sm.type.dtype not in tensor.float_dtypes):
raise ValueError('sm must be 2-d tensor of floats', sm.type)
if (y_idx.type.ndim != 1 or
y_idx.type.dtype not in tensor.discrete_dtypes):
y_idx.type.dtype not in tensor.discrete_dtypes):
raise ValueError('y_idx must be 1-d tensor of [u]ints', y_idx.type)
return Apply(self, [dy, sm, y_idx], [sm.type()])
......@@ -1082,9 +1084,8 @@ class CrossentropySoftmax1HotWithBiasDx(gof.Op):
# typically we should not need the gradient w.r.t. dy).
y_idx_range = tensor.arange(y_idx.shape[0])
g_dy = tensor.sum(
g_dx * subtensor.AdvancedIncSubtensor()(
sm, tensor.fill(dy, -1), y_idx_range, y_idx),
axis=1)
g_dx * subtensor.AdvancedIncSubtensor()(
sm, tensor.fill(dy, -1), y_idx_range, y_idx), axis=1)
g_sm = dy.dimshuffle(0, 'x') * g_dx
g_y_idx = grad_not_implemented(self, 2, y_idx)
return [g_dy, g_sm, g_y_idx]
......@@ -1226,8 +1227,7 @@ def crossentropy_softmax_max_and_argmax_1hot_with_bias(x, b, y_idx, **kwargs):
unnecessary? e.g. CrossentropySoftmaxArgmax1HotWithBias should return
the appropriate information (i.e. the max probability)?
"""
(xent, softmax) = crossentropy_softmax_1hot_with_bias(x, b, y_idx,
**kwargs)
(xent, softmax) = crossentropy_softmax_1hot_with_bias(x, b, y_idx, **kwargs)
(max_pr, argmax) = tensor.max_and_argmax(softmax, axis=-1)
return (xent, softmax, max_pr, argmax)
......@@ -1239,7 +1239,7 @@ def crossentropy_softmax_max_and_argmax_1hot(x, y_idx, **kwargs):
class CrossentropyCategorical1HotGrad(gof.Op):
__props__ = ()
def make_node(self, g_y, coding_dist, true_one_of_n):
......@@ -1251,8 +1251,8 @@ class CrossentropyCategorical1HotGrad(gof.Op):
g_coding_strg, = out
g_coding = numpy.zeros_like(coding_dist)
for i in xrange(len(g_y)):
g_coding[i, true_one_of_n[i]] = -g_y[i] / coding_dist[i,
true_one_of_n[i]]
g_coding[i, true_one_of_n[i]] = (-g_y[i] /
coding_dist[i, true_one_of_n[i]])
g_coding_strg[0] = g_coding
def infer_shape(self, node, in_shapes):
......@@ -1297,8 +1297,8 @@ class CrossentropyCategorical1Hot(gof.Op):
tensor.lvector))
return Apply(self, [_coding_dist, _true_one_of_n],
[tensor.Tensor(dtype=_coding_dist.dtype,
broadcastable=[False])()])
[tensor.Tensor(dtype=_coding_dist.dtype,
broadcastable=[False])()])
def perform(self, node, inp, out):
coding, one_of_n = inp
......@@ -1346,10 +1346,11 @@ def crossentropy_to_crossentropy_with_softmax_with_bias(fgraph):
sm, one_of_n = node.inputs
if sm.owner and sm.owner.op == softmax_with_bias:
x, b = sm.owner.inputs
new_nll, new_sm, new_am = crossentropy_softmax_argmax_1hot_with_bias(x, b,
one_of_n)
fgraph.replace_all_validate([(nll, new_nll), (sm, new_sm)],
reason="crossentropy_to_crossentropy_with_softmax_with_bias")
new_nll, new_sm, new_am = crossentropy_softmax_argmax_1hot_with_bias(
x, b, one_of_n)
fgraph.replace_all_validate(
[(nll, new_nll), (sm, new_sm)],
reason="crossentropy_to_crossentropy_with_softmax_with_bias")
return True
return False
......@@ -1381,17 +1382,19 @@ def crossentropy_to_crossentropy_with_softmax(fgraph):
sm, one_of_n = node.inputs
if sm.owner and sm.owner.op == softmax_op:
x, = sm.owner.inputs
new_nll, new_sm, new_am = crossentropy_softmax_argmax_1hot_with_bias(x,
tensor.zeros_like(x[0]), one_of_n)
fgraph.replace_all_validate([(nll, new_nll), (sm, new_sm)],
reason="crossentropy_to_crossentropy_with_softmax")
new_nll, new_sm, new_am = crossentropy_softmax_argmax_1hot_with_bias(
x, tensor.zeros_like(x[0]), one_of_n)
fgraph.replace_all_validate(
[(nll, new_nll), (sm, new_sm)],
reason="crossentropy_to_crossentropy_with_softmax")
return True
if sm.owner and sm.owner.op == softmax_with_bias:
x, b = sm.owner.inputs
new_nll, new_sm, new_am = crossentropy_softmax_argmax_1hot_with_bias(x, b,
one_of_n)
fgraph.replace_all_validate([(nll, new_nll), (sm, new_sm)],
reason="crossentropy_to_crossentropy_with_softmax")
one_of_n)
fgraph.replace_all_validate(
[(nll, new_nll), (sm, new_sm)],
reason="crossentropy_to_crossentropy_with_softmax")
return True
return False
......@@ -1413,10 +1416,10 @@ def local_softmax_grad_to_crossentropy_with_softmax_grad(node):
if node.op == softmax_grad:
g_coding_dist, coding_dist = node.inputs
if (g_coding_dist.owner and
g_coding_dist.owner.op == crossentropy_categorical_1hot_grad):
g_coding_dist.owner.op == crossentropy_categorical_1hot_grad):
g_nll, coding_dist, true_one_of_n = g_coding_dist.owner.inputs
dx = crossentropy_softmax_1hot_with_bias_dx(g_nll,
coding_dist, true_one_of_n)
dx = crossentropy_softmax_1hot_with_bias_dx(g_nll, coding_dist,
true_one_of_n)
return [dx]
......@@ -1428,16 +1431,17 @@ def local_argmax_pushdown(node):
(softmax_op, softplus, tensor.exp, tensor.log, tensor.tanh, sigmoid,
softmax_with_bias):
if theano.config.warn.argmax_pushdown_bug:
logging.getLogger('theano.tensor.nnet.nnet').warn("WARNING: there "
"was a bug in Theano fixed on May 27th, 2010 in this case."
" I.E. when we take the max of a softplus, softmax, exp, "
"log, tanh, sigmoid, softmax_with_bias op, we were doing "
"the max of the parent of the input. To remove this "
"warning set the Theano flags 'warn.argmax_pushdown_bug' "
"to False")
logging.getLogger('theano.tensor.nnet.nnet').warn(
"WARNING: there "
"was a bug in Theano fixed on May 27th, 2010 in this case."
" I.E. when we take the max of a softplus, softmax, exp, "
"log, tanh, sigmoid, softmax_with_bias op, we were doing "
"the max of the parent of the input. To remove this "
"warning set the Theano flags 'warn.argmax_pushdown_bug' "
"to False")
if (node.op == tensor._max_and_argmax and
node.inputs[0].owner and len(node.outputs[0].clients) == 0):
node.inputs[0].owner and len(node.outputs[0].clients) == 0):
x_max, x_argmax = node.outputs
x, axis = node.inputs
# TODO: Make a list/set of monotonic ops...
......@@ -1657,15 +1661,15 @@ def local_advanced_indexing_crossentropy_onehot_grad(node):
if isinstance(denom.owner.op, subtensor.AdvancedSubtensor):
# Base case
adv_subtensor = denom
#out_grad /= 1.
# out_grad /= 1.
elif denom.owner.op == tensor.mul:
# Try to find the AdvancedSubtensor node mentionned above,
# and the output gradient
for i, input in enumerate(denom.owner.inputs):
if input.owner and isinstance(input.owner.op,
subtensor.AdvancedSubtensor):
other_inputs = [in_ for (j,
in_) in enumerate(denom.owner.inputs) if j != i]
other_inputs = [in_ for (j, in_) in
enumerate(denom.owner.inputs) if j != i]
if len(other_inputs) == 1:
rest = other_inputs[0]
else:
......@@ -1831,8 +1835,8 @@ def local_useless_crossentropy_softmax_1hot_with_bias_dx_alloc(node):
# `CrossentropySoftmax1HotWithBiasDx`) we do not need to
# check it at runtime.
if (dz_broad[0] and
not same_shape(sm, dy, dim_x=0, dim_y=0) and
shape_of[dy][0] != 1):
not same_shape(sm, dy, dim_x=0, dim_y=0) and
shape_of[dy][0] != 1):
# If `dz` is broadcastable, we need to check whether the shapes
# of `dy` and `sm` are the same or whether the shape of `dy` is
# equal to 1.
......@@ -1894,20 +1898,18 @@ def categorical_crossentropy(coding_dist, true_dist):
"""
if true_dist.ndim == coding_dist.ndim:
return -tensor.sum(true_dist * tensor.log(coding_dist), axis=coding_dist.ndim-1)
return -tensor.sum(true_dist * tensor.log(coding_dist),
axis=coding_dist.ndim - 1)
elif true_dist.ndim == coding_dist.ndim - 1:
return crossentropy_categorical_1hot(coding_dist, true_dist)
else:
raise TypeError('rank mismatch between coding and true distributions')
from theano import scalar
class Prepend_scalar_constant_to_each_row(gof.Op):
__props__ = ()
def __init__(self, val=0):
if isinstance(val, float):
val = scalar.constant(val)
......@@ -2026,7 +2028,7 @@ local_log_softmax = gof.PatternSub(in_pattern=(tensor.log, (softmax_op, 'x')),
# don't do register_stabilize, this is to make local_log_softmax run
# only after another more specific optimization that stabilizes cross entropy
#opt.register_stabilize(local_log_softmax, name = 'local_log_softmax')
# opt.register_stabilize(local_log_softmax, name = 'local_log_softmax')
opt.register_specialize(local_log_softmax, 'fast_compile_gpu', name='local_log_softmax')
......
theano/tensor/nnet/sigm.py
浏览文件 @ 03e77233
......@@ -7,7 +7,6 @@ from __future__ import print_function
import warnings
import numpy
from six.moves import xrange
import theano
from theano import config, gof, printing, scalar
......@@ -92,7 +91,7 @@ class ScalarSigmoid(scalar.UnaryScalarOp):
x, = inp
z, = out
if (not theano.config.lib.amdlibm or
node.inputs[0].dtype != node.outputs[0].dtype):
node.inputs[0].dtype != node.outputs[0].dtype):
raise theano.gof.utils.MethodNotDefined()
dtype = node.inputs[0].dtype
if dtype == 'float32' and self.amd_float32 is not None:
......@@ -129,9 +128,8 @@ class ScalarSigmoid(scalar.UnaryScalarOp):
"""
This method was used to generate the graph: sigmoid_prec.png in the doc
"""
import matplotlib
data = numpy.arange(-15, 15, .1)
val = 1/(1+numpy.exp(-data))
val = 1 / (1 + numpy.exp(-data))
def hard_sigmoid(x):
return theano.tensor.nnet.hard_sigmoid(x)
......@@ -164,10 +162,10 @@ scalar_sigmoid = ScalarSigmoid(scalar.upgrade_to_float, name='scalar_sigmoid')
sigmoid = elemwise.Elemwise(scalar_sigmoid, name='sigmoid')
sigmoid_inplace = elemwise.Elemwise(
ScalarSigmoid(scalar.transfer_type(0)),
inplace_pattern={0: 0},
name='sigmoid_inplace',
)
ScalarSigmoid(scalar.transfer_type(0)),
inplace_pattern={0: 0},
name='sigmoid_inplace',
)
pprint.assign(sigmoid, printing.FunctionPrinter('sigmoid'))
......@@ -240,7 +238,7 @@ pprint.assign(ultra_fast_sigmoid,
printing.FunctionPrinter('ultra_fast_sigmoid'))
#@opt.register_uncanonicalize
# @opt.register_uncanonicalize
@gof.local_optimizer([sigmoid])
def local_ultra_fast_sigmoid(node):
"""
......@@ -290,7 +288,7 @@ def hard_sigmoid(x):
return x
#@opt.register_uncanonicalize
# @opt.register_uncanonicalize
@gof.local_optimizer([sigmoid])
def local_hard_sigmoid(node):
if (isinstance(node.op, tensor.Elemwise) and
......@@ -412,7 +410,7 @@ def is_1pexp(t):
"""
if t.owner and t.owner.op == tensor.add:
scalars, scalar_inputs, nonconsts = \
opt.scalarconsts_rest(t.owner.inputs)
opt.scalarconsts_rest(t.owner.inputs)
# scalar_inputs are potentially dimshuffled and fill'd scalars
if len(nonconsts) == 1:
maybe_exp = nonconsts[0]
......@@ -439,11 +437,12 @@ def is_1pexp(t):
return None
AddConfigVar('warn.identify_1pexp_bug',
'Warn if Theano versions prior to 7987b51 (2011-12-18) could have '
'yielded a wrong result due to a bug in the is_1pexp function',
BoolParam(theano.configdefaults.warn_default('0.4.1')),
in_c_key=False)
AddConfigVar(
'warn.identify_1pexp_bug',
'Warn if Theano versions prior to 7987b51 (2011-12-18) could have '
'yielded a wrong result due to a bug in the is_1pexp function',
BoolParam(theano.configdefaults.warn_default('0.4.1')),
in_c_key=False)
def is_exp(var):
......@@ -778,9 +777,9 @@ def perform_sigm_times_exp(tree, exp_x=None, exp_minus_x=None, sigm_x=None,
rval = False
for sub_idx, sub_tree in enumerate(inputs):
rval |= perform_sigm_times_exp(
tree=sub_tree, parent=tree, child_idx=sub_idx,
exp_x=exp_x, exp_minus_x=exp_minus_x, sigm_x=sigm_x,
sigm_minus_x=sigm_minus_x, full_tree=full_tree)
tree=sub_tree, parent=tree, child_idx=sub_idx,
exp_x=exp_x, exp_minus_x=exp_minus_x, sigm_x=sigm_x,
sigm_minus_x=sigm_minus_x, full_tree=full_tree)
return rval
else:
# Reached a leaf: if it is an exponential or a sigmoid, then we
......@@ -867,15 +866,15 @@ def local_inv_1_plus_exp(node):
inv_arg = node.inputs[0]
if inv_arg.owner and inv_arg.owner.op == tensor.add:
scalars, scalar_inputs, nonconsts = \
opt.scalarconsts_rest(inv_arg.owner.inputs)
opt.scalarconsts_rest(inv_arg.owner.inputs)
# scalar_inputs are potentially dimshuffled and fill'd scalars
if len(nonconsts) == 1:
if nonconsts[0].owner and nonconsts[0].owner.op == tensor.exp:
if scalars and numpy.allclose(numpy.sum(scalars), 1):
return opt._fill_chain(
sigmoid(
tensor.neg(nonconsts[0].owner.inputs[0])),
scalar_inputs)
sigmoid(
tensor.neg(nonconsts[0].owner.inputs[0])),
scalar_inputs)
# Registration is below, and conditional.
......@@ -892,7 +891,7 @@ def local_1msigmoid(node):
if sub_r.owner and sub_r.owner.op == sigmoid:
try:
val_l = opt.get_scalar_constant_value(sub_l)
except Exception as e:
except Exception:
return
if numpy.allclose(numpy.sum(val_l), 1):
return [sigmoid(-sub_r.owner.inputs[0])]
......@@ -921,7 +920,6 @@ if 0:
print(sigm_canonicalize(node))
def sigm_canonicalize(node):
add = tensor.add
mul = tensor.mul
div = tensor.true_div
......
theano/tests/test_flake8.py
浏览文件 @ 03e77233
......@@ -88,15 +88,7 @@ whitelist_flake8 = [
"tensor/signal/conv.py",
"tensor/signal/tests/test_conv.py",
"tensor/signal/tests/test_downsample.py",
"tensor/nnet/nnet.py",
"tensor/nnet/Conv3D.py",
"tensor/nnet/__init__.py",
"tensor/nnet/ConvTransp3D.py",
"tensor/nnet/sigm.py",
"tensor/nnet/ConvGrad3D.py",
"tensor/nnet/conv3d2d.py",
"tensor/nnet/conv.py",
"tensor/nnet/neighbours.py",
"tensor/nnet/tests/test_conv.py",
"tensor/nnet/tests/test_neighbours.py",
"tensor/nnet/tests/test_nnet.py",
......
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