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提交 e79c4e4c authored 作者: Frédéric Bastien's avatar Frédéric Bastien 提交者： GitHub
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Merge pull request #5835 from Amrithasuresh/master

Updated numpy as np #4218
上级 d0524fe5 7168c81b
隐藏空白字符变更
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正在显示 包含 110 行增加 和 111 行删除
theano/tensor/nnet/Conv3D.py
浏览文件 @ e79c4e4c
from __future__ import absolute_import, print_function, division
import numpy as N
import numpy as np
from six.moves import xrange
import theano
......@@ -407,7 +407,7 @@ class Conv3D(theano.Op):
long long Vposl = Vpos;
for (int m = 0; m < filterDur; m++) {
//H[i,r,c,t,:] += N.dot(W[:,k,l,m,:],V[i,dr*r+k,dc*c+l,dt*t+m,:])
//H[i,r,c,t,:] += np.dot(W[:,k,l,m,:],V[i,dr*r+k,dc*c+l,dt*t+m,:])
//note: changing the weights so that outputChannels and inputChannels were the last two rather than
......@@ -619,8 +619,8 @@ def computeH(V, W, b, d):
outputWidth = int((vidWidth - filterWidth) / dy) + 1
outputDur = int((vidDur - filterDur) / dt) + 1
H = N.zeros((batchSize, outputHeight,
outputWidth, outputDur, outputChannels), dtype=V.dtype)
H = np.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]):
......
theano/tensor/nnet/ConvGrad3D.py
浏览文件 @ e79c4e4c
from __future__ import absolute_import, print_function, division
from six.moves import xrange
import numpy as N
import numpy as np
import theano
from theano.tensor import basic as T
......@@ -71,7 +71,7 @@ class ConvGrad3D(theano.Op):
assert V.shape[0] == batchSize
dr, dc, dt = d
dCdW = N.zeros(WShape, dtype=V.dtype)
dCdW = np.zeros(WShape, dtype=V.dtype)
# print 'computing output of shape '+str(WShape)
......
theano/tensor/nnet/ConvTransp3D.py
浏览文件 @ e79c4e4c
from __future__ import absolute_import, print_function, division
import numpy as N
import numpy as np
from six.moves import xrange
import theano
......@@ -385,8 +385,8 @@ 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)
R = np.zeros((batchSize, videoHeight,
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):
......@@ -399,12 +399,12 @@ def computeR(W, b, d, H, Rshape=None):
for t in xrange(0, videoDur):
R[i, r, c, t, j] = b[j]
ftc = max([0, int(N.ceil(
ftc = max([0, int(np.ceil(
float(t - filterDur + 1) / float(dt)))])
fcc = max([0, int(N.ceil(
fcc = max([0, int(np.ceil(
float(c - filterWidth + 1) / float(dc)))])
rc = max([0, int(N.ceil(
rc = max([0, int(np.ceil(
float(r - filterHeight + 1) / float(dr)))])
while rc < outputHeight:
rk = r - rc * dr
......@@ -423,7 +423,7 @@ def computeR(W, b, d, H, Rshape=None):
if tk < 0:
break
R[i, r, c, t, j] += N.dot(
R[i, r, c, t, j] += np.dot(
W[:, rk, ck, tk, j], H[i, rc, cc, tc, :])
tc += 1
......
theano/tensor/nnet/abstract_conv.py
浏览文件 @ e79c4e4c
......@@ -17,7 +17,6 @@ from theano.gof import Apply, Op
from six.moves import xrange
import warnings
import numpy
import numpy as np
try:
......@@ -69,7 +68,7 @@ def get_conv_output_shape(image_shape, kernel_shape,
nkern, kshp = kernel_shape[0], kernel_shape[2:]
if filter_dilation is None:
filter_dilation = numpy.ones(len(subsample), dtype='int')
filter_dilation = np.ones(len(subsample), dtype='int')
if isinstance(border_mode, tuple):
out_shp = tuple(get_conv_shape_1axis(
......@@ -181,7 +180,7 @@ def get_conv_gradweights_shape(image_shape, top_shape,
nchan, topshp = top_shape[1], top_shape[2:]
if filter_dilation is None:
filter_dilation = numpy.ones(len(subsample), dtype='int')
filter_dilation = np.ones(len(subsample), dtype='int')
if isinstance(border_mode, tuple):
out_shp = tuple(get_conv_gradweights_shape_1axis(
......@@ -286,7 +285,7 @@ def get_conv_gradinputs_shape(kernel_shape, top_shape,
nkern, kshp = kernel_shape[1], kernel_shape[2:]
if filter_dilation is None:
filter_dilation = numpy.ones(len(subsample), dtype='int')
filter_dilation = np.ones(len(subsample), dtype='int')
if isinstance(border_mode, tuple):
out_shp = tuple(get_conv_gradinputs_shape_1axis(
......@@ -1508,11 +1507,11 @@ class BaseAbstractConv(Op):
out_shape = get_conv_output_shape(img.shape, kern.shape,
mode, [1] * self.convdim, dilation)
out = numpy.zeros(out_shape, dtype=img.dtype)
out = np.zeros(out_shape, dtype=img.dtype)
dil_kern_shp = kern.shape[:-self.convdim] + tuple(
(kern.shape[-self.convdim + i] - 1) * dilation[i] + 1
for i in range(self.convdim))
dilated_kern = numpy.zeros(dil_kern_shp, dtype=kern.dtype)
dilated_kern = np.zeros(dil_kern_shp, dtype=kern.dtype)
dilated_kern[(slice(None), slice(None)) +
tuple(slice(None, None, dilation[i]) for i in range(self.convdim))
] = kern
......@@ -1522,7 +1521,7 @@ class BaseAbstractConv(Op):
bval = _bvalfromboundary('fill')
with warnings.catch_warnings():
warnings.simplefilter('ignore', numpy.ComplexWarning)
warnings.simplefilter('ignore', np.ComplexWarning)
for b in xrange(img.shape[0]):
for n in xrange(kern.shape[0]):
for im0 in xrange(img.shape[1]):
......@@ -1592,8 +1591,8 @@ class AbstractConv(BaseAbstractConv):
def perform(self, node, inp, out_):
img, kern = inp
img = numpy.asarray(img)
kern = numpy.asarray(kern)
img = np.asarray(img)
kern = np.asarray(kern)
dil_kernshp = tuple((kern.shape[2 + i] - 1) * self.filter_dilation[i] + 1
for i in range(self.convdim))
o, = out_
......@@ -1613,10 +1612,10 @@ class AbstractConv(BaseAbstractConv):
if isinstance(mode, tuple):
pad = tuple(int(mode[i]) for i in range(self.convdim))
mode = "valid"
new_img = numpy.zeros((img.shape[0], img.shape[1]) +
tuple(img.shape[i + 2] + 2 * pad[i]
for i in range(self.convdim)),
dtype=img.dtype)
new_img = np.zeros((img.shape[0], img.shape[1]) +
tuple(img.shape[i + 2] + 2 * pad[i]
for i in range(self.convdim)),
dtype=img.dtype)
new_img[(slice(None), slice(None)) +
tuple(slice(pad[i], img.shape[i + 2] + pad[i])
for i in range(self.convdim))] = img
......@@ -1809,8 +1808,8 @@ class AbstractConv_gradWeights(BaseAbstractConv):
def perform(self, node, inp, out_):
img, topgrad, shape = inp
img = numpy.asarray(img)
topgrad = numpy.asarray(topgrad)
img = np.asarray(img)
topgrad = np.asarray(topgrad)
o, = out_
......@@ -1833,10 +1832,10 @@ class AbstractConv_gradWeights(BaseAbstractConv):
pad = tuple(int(mode[i]) for i in range(self.convdim))
mode = "valid"
new_img = numpy.zeros((img.shape[0], img.shape[1]) +
tuple(img.shape[i + 2] + 2 * pad[i]
for i in range(self.convdim)),
dtype=img.dtype)
new_img = np.zeros((img.shape[0], img.shape[1]) +
tuple(img.shape[i + 2] + 2 * pad[i]
for i in range(self.convdim)),
dtype=img.dtype)
new_img[(slice(None), slice(None)) +
tuple(slice(pad[i], img.shape[i + 2] + pad[i])
for i in range(self.convdim))] = img
......@@ -1846,7 +1845,7 @@ class AbstractConv_gradWeights(BaseAbstractConv):
new_shape = ((topgrad.shape[0], topgrad.shape[1]) +
tuple(img.shape[i + 2] - dil_shape[i] + 1
for i in range(self.convdim)))
new_topgrad = numpy.zeros((new_shape), dtype=topgrad.dtype)
new_topgrad = np.zeros((new_shape), dtype=topgrad.dtype)
new_topgrad[(slice(None), slice(None)) +
tuple(slice(None, None, self.subsample[i])
for i in range(self.convdim))] = topgrad
......@@ -2049,8 +2048,8 @@ class AbstractConv_gradInputs(BaseAbstractConv):
def perform(self, node, inp, out_):
kern, topgrad, shape = inp
kern = numpy.asarray(kern)
topgrad = numpy.asarray(topgrad)
kern = np.asarray(kern)
topgrad = np.asarray(topgrad)
o, = out_
mode = self.border_mode
......@@ -2089,7 +2088,7 @@ class AbstractConv_gradInputs(BaseAbstractConv):
new_shape = ((topgrad.shape[0], topgrad.shape[1]) +
tuple(shape[i] + 2 * pad[i] - dil_kernshp[i] + 1
for i in range(self.convdim)))
new_topgrad = numpy.zeros((new_shape), dtype=topgrad.dtype)
new_topgrad = np.zeros((new_shape), dtype=topgrad.dtype)
new_topgrad[(slice(None), slice(None)) +
tuple(slice(None, None, self.subsample[i])
for i in range(self.convdim))] = topgrad
......
theano/tensor/nnet/blocksparse.py
浏览文件 @ e79c4e4c
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import theano
from theano import Op, Apply
......@@ -106,7 +106,7 @@ class SparseBlockGemv(Op):
for i in range(h.shape[1]):
inputIdx = iIdx[b, i]
w = W[inputIdx, outputIdx]
o[b, j, :] += numpy.dot(h[b, i], w)
o[b, j, :] += np.dot(h[b, i], w)
out_[0][0] = o
def infer_shape(self, node, input_shapes):
......@@ -185,7 +185,7 @@ class SparseBlockOuter(Op):
Which blocks will be computed is specified in `yIdx`.
"""
one = theano.tensor.constant(numpy.asarray(1.0, dtype='float32'))
one = theano.tensor.constant(np.asarray(1.0, dtype='float32'))
o = theano.tensor.as_tensor_variable(o)
x = theano.tensor.as_tensor_variable(x)
y = theano.tensor.as_tensor_variable(y)
......@@ -208,8 +208,8 @@ class SparseBlockOuter(Op):
for b in range(x.shape[0]):
for i in range(xIdx.shape[1]):
for j in range(yIdx.shape[1]):
o[xIdx[b, i], yIdx[b, j]] += numpy.outer(x[b, i],
y[b, j, :])
o[xIdx[b, i], yIdx[b, j]] += np.outer(x[b, i],
y[b, j, :])
out_[0][0] = o
......
theano/tensor/nnet/bn.py
浏览文件 @ e79c4e4c
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import theano
from theano import Apply, Op
from theano.gof import local_optimizer
......@@ -89,7 +89,7 @@ def _prepare_batch_normalization_axes(axes, ndim):
axes = (0,)
elif axes == 'spatial':
axes = (0,) + tuple(range(2, ndim))
elif isinstance(axes, (tuple, list, numpy.ndarray)):
elif isinstance(axes, (tuple, list, np.ndarray)):
axes = tuple(int(a) for a in axes)
else:
raise ValueError('invalid axes: %s', str(axes))
......@@ -215,7 +215,7 @@ def batch_normalization_train(inputs, gamma, beta, axes='per-activation',
# epsilon will be converted to floatX later. we need to check
# for rounding errors now, since numpy.float32(1e-5) < 1e-5.
epsilon = numpy.cast[theano.config.floatX](epsilon)
epsilon = np.cast[theano.config.floatX](epsilon)
if epsilon < 1e-5:
raise ValueError("epsilon must be at least 1e-5, got %s" % str(epsilon))
......@@ -337,7 +337,7 @@ def batch_normalization_test(inputs, gamma, beta, mean, var,
# epsilon will be converted to floatX later. we need to check
# for rounding errors now, since numpy.float32(1e-5) < 1e-5.
epsilon = numpy.cast[theano.config.floatX](epsilon)
epsilon = np.cast[theano.config.floatX](epsilon)
if epsilon < 1e-5:
raise ValueError("epsilon must be at least 1e-5, got %s" % str(epsilon))
......@@ -480,7 +480,7 @@ class AbstractBatchNormTrain(Op):
mean = x.mean(axes, keepdims=True)
var = x.var(axes, keepdims=True)
invstd = 1.0 / numpy.sqrt(var + epsilon)
invstd = 1.0 / np.sqrt(var + epsilon)
out = (x - mean) * (scale * invstd) + bias
output_storage[0][0] = out
......@@ -493,7 +493,7 @@ class AbstractBatchNormTrain(Op):
mean * running_average_factor
output_storage[3][0] = running_mean
if len(inputs) > 6:
m = float(numpy.prod(x.shape) / numpy.prod(scale.shape))
m = float(np.prod(x.shape) / np.prod(scale.shape))
running_var = inputs[6]
running_var = running_var * (1.0 - running_average_factor) + \
(m / (m - 1)) * var * running_average_factor
......@@ -568,7 +568,7 @@ class AbstractBatchNormInference(Op):
def perform(self, node, inputs, output_storage):
x, scale, bias, estimated_mean, estimated_variance, epsilon = inputs
out = (x - estimated_mean) * (scale / numpy.sqrt(estimated_variance + epsilon)) + bias
out = (x - estimated_mean) * (scale / np.sqrt(estimated_variance + epsilon)) + bias
output_storage[0][0] = out
......@@ -607,12 +607,12 @@ class AbstractBatchNormTrainGrad(Op):
raise ValueError('axes should be less than ndim (<%d), but %s given' % (x.ndim, str(axes)))
x_diff = x - x_mean
mean_dy_x_diff = numpy.mean(dy * x_diff, axis=axes, keepdims=True)
mean_dy_x_diff = np.mean(dy * x_diff, axis=axes, keepdims=True)
c = (dy * x_invstd) - (x_diff * mean_dy_x_diff * (x_invstd ** 3))
g_wrt_inputs = scale * (c - numpy.mean(c, axis=axes, keepdims=True))
g_wrt_scale = numpy.sum(dy * x_invstd * x_diff, axis=axes, keepdims=True)
g_wrt_bias = numpy.sum(dy, axis=axes, keepdims=True)
g_wrt_inputs = scale * (c - np.mean(c, axis=axes, keepdims=True))
g_wrt_scale = np.sum(dy * x_invstd * x_diff, axis=axes, keepdims=True)
g_wrt_bias = np.sum(dy, axis=axes, keepdims=True)
output_storage[0][0] = g_wrt_inputs
output_storage[1][0] = g_wrt_scale
......
theano/tensor/nnet/conv.py
浏览文件 @ e79c4e4c
......@@ -12,7 +12,7 @@ from __future__ import absolute_import, print_function, division
import logging
import numpy
import numpy as np
from six.moves import xrange
import warnings
......@@ -756,8 +756,8 @@ class ConvOp(OpenMPOp):
(1, 1))[2:]
if z[0] is None or z[0].shape != (bsize, nkern,) + fulloutshp:
z[0] = numpy.zeros((bsize, nkern,) + fulloutshp,
dtype=img2d.dtype)
z[0] = np.zeros((bsize, nkern,) + fulloutshp,
dtype=img2d.dtype)
zz = z[0]
stacklen = imshp[0]
......@@ -767,18 +767,18 @@ class ConvOp(OpenMPOp):
if self.imshp != self.imshp_logical:
# assuming that to get from imshp to imshp logical we insert zeros in missing spots
rstride = int(numpy.ceil(imshp_logical[1] / float(imshp[1])))
cstride = int(numpy.ceil(imshp_logical[2] / float(imshp[2])))
buf = numpy.zeros((bsize,) + imshp_logical, dtype=img2d.dtype)
rstride = int(np.ceil(imshp_logical[1] / float(imshp[1])))
cstride = int(np.ceil(imshp_logical[2] / float(imshp[2])))
buf = np.zeros((bsize,) + imshp_logical, dtype=img2d.dtype)
buf[:, :, ::rstride, ::cstride] = img2d
img2d = buf
del buf, rstride, cstride
if kshp != kshp_logical:
rstride = int(numpy.ceil(kshp_logical[0] / float(kshp[0])))
cstride = int(numpy.ceil(kshp_logical[1] / float(kshp[1])))
buf = numpy.zeros((nkern, stacklen) +
self.kshp_logical, dtype=filtersflipped.dtype)
rstride = int(np.ceil(kshp_logical[0] / float(kshp[0])))
cstride = int(np.ceil(kshp_logical[1] / float(kshp[1])))
buf = np.zeros((nkern, stacklen) +
self.kshp_logical, dtype=filtersflipped.dtype)
if self.kshp_logical_top_aligned:
roffset = coffset = 0
else:
......@@ -796,7 +796,7 @@ class ConvOp(OpenMPOp):
bval = _bvalfromboundary('fill')
with warnings.catch_warnings():
warnings.simplefilter('ignore', numpy.ComplexWarning)
warnings.simplefilter('ignore', np.ComplexWarning)
for b in xrange(bsize):
for n in xrange(nkern):
zz[b, n, ...].fill(0)
......@@ -808,9 +808,9 @@ class ConvOp(OpenMPOp):
if False:
if False and self.out_mode == "full":
img2d2 = numpy.zeros((bsize, stacklen,
imshp[1] + 2 * kshp[0] - 2,
imshp[2] + 2 * kshp[1] - 2))
img2d2 = np.zeros((bsize, stacklen,
imshp[1] + 2 * kshp[0] - 2,
imshp[2] + 2 * kshp[1] - 2))
img2d2[:, :, kshp[0] - 1:kshp[0] - 1 + imshp[1],
kshp[1] - 1:kshp[1] - 1 + imshp[2]] = img2d
img2d = img2d2
......@@ -873,7 +873,7 @@ class ConvOp(OpenMPOp):
tmp_node = theano.tensor.nnet.conv3D(
V=shuffled_inputs,
W=shuffled_kerns,
b=theano.tensor.alloc(numpy.asarray(0, dtype=kerns.dtype),
b=theano.tensor.alloc(np.asarray(0, dtype=kerns.dtype),
kerns.shape[0]),
d=(self.dx, self.dy, 1))
node = theano.tensor.addbroadcast(
......@@ -1260,17 +1260,17 @@ if(%(value)s != %(expected)s){
if all_shape:
d["self_kshp_logical_r"] = self.kshp_logical[0]
d["self_kshp_logical_c"] = self.kshp_logical[1]
d["self_kshp_logical_stride_r"] = int(numpy.ceil(
d["self_kshp_logical_stride_r"] = int(np.ceil(
self.kshp_logical[0] / float(self.kshp[0])))
d["self_kshp_logical_stride_c"] = int(numpy.ceil(
d["self_kshp_logical_stride_c"] = int(np.ceil(
self.kshp_logical[1] / float(self.kshp[1])))
d["self_imshp_logical_r"] = self.imshp_logical[1]
# numpy.B. 1 not 0
d["self_imshp_logical_c"] = self.imshp_logical[2]
# numpy.B. 2 not 1
d["self_imshp_logical_stride_r"] = int(numpy.ceil(
d["self_imshp_logical_stride_r"] = int(np.ceil(
self.imshp_logical[1] / float(self.imshp[1])))
d["self_imshp_logical_stride_c"] = int(numpy.ceil(
d["self_imshp_logical_stride_c"] = int(np.ceil(
self.imshp_logical[2] / float(self.imshp[2])))
if not self.imshp[0] == 1:
d["affectation"] = "+="
......
theano/tensor/nnet/neighbours.py
浏览文件 @ e79c4e4c
......@@ -4,7 +4,7 @@ TODO: implement Images2Neibs.infer_shape() methods
"""
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import theano
from theano import Op, Apply
......@@ -224,7 +224,7 @@ class Images2Neibs(Op):
z_dim0 = grid_c * grid_d * ten4.shape[1] * ten4.shape[0]
z_dim1 = c * d
z[0] = numpy.empty((z_dim0, z_dim1), dtype=node.outputs[0].dtype)
z[0] = np.empty((z_dim0, z_dim1), dtype=node.outputs[0].dtype)
nb_batch = ten4.shape[0]
nb_stack = ten4.shape[1]
......
theano/tensor/nnet/nnet.py
浏览文件 @ e79c4e4c
......@@ -15,7 +15,7 @@ revisited later when all the intermediate part are on the GPU.
from __future__ import absolute_import, print_function, division
import logging
import warnings
import numpy
import numpy as np
from six.moves import xrange
import theano
......@@ -85,7 +85,7 @@ class SoftmaxWithBias(gof.Op):
if x.size == 0:
# Numpy doesn't like the max of a zero-sized object.
output_storage[0][0] = numpy.zeros(x.shape, dtype=x.dtype)
output_storage[0][0] = np.zeros(x.shape, dtype=x.dtype)
return
x_dtype = x.dtype
......@@ -94,7 +94,7 @@ class SoftmaxWithBias(gof.Op):
x = x.astype('float32')
x_plus_b = x + b[None, :]
e_x = numpy.exp(x_plus_b - x_plus_b.max(axis=1)[:, None])
e_x = np.exp(x_plus_b - x_plus_b.max(axis=1)[:, None])
e_x *= 1.0 / e_x.sum(axis=1)[:, None]
# default for copy is True and we don't need a copy if the
# data type matches.
......@@ -314,7 +314,7 @@ class SoftmaxGrad(gof.Op):
def perform(self, node, input_storage, output_storage):
dy, sm = input_storage
dx = numpy.zeros_like(sm)
dx = np.zeros_like(sm)
# dx[i,j] = - (\sum_k dy[i,k] sm[i,k]) sm[i,j] + dy[i,j] sm[i,j]
for i in xrange(sm.shape[0]):
dy_times_sm_i = dy[i] * sm[i]
......@@ -435,7 +435,7 @@ class Softmax(gof.Op):
def perform(self, node, input_storage, output_storage):
x, = input_storage
e_x = numpy.exp(x - x.max(axis=1)[:, None])
e_x = np.exp(x - x.max(axis=1)[:, None])
sm = e_x / e_x.sum(axis=1)[:, None]
output_storage[0][0] = sm
......@@ -620,8 +620,8 @@ class LogSoftmax(gof.Op):
def perform(self, node, input_storage, output_storage):
x, = input_storage
xdev = x - x.max(axis=1)[:, None]
lsm = xdev - numpy.log(numpy.sum(numpy.exp(xdev), axis=1,
keepdims=True))
lsm = xdev - np.log(np.sum(np.exp(xdev), axis=1,
keepdims=True))
output_storage[0][0] = lsm
def grad(self, inp, grads):
......@@ -1003,27 +1003,27 @@ class CrossentropySoftmaxArgmax1HotWithBias(gof.Op):
raise ValueError('y_idx must have same number of rows as x')
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))
am = numpy.zeros_like(y_idx)
sm = np.zeros_like(x) # softmax
nll = np.zeros(x.shape[0], dtype=node.outputs[0].type.dtype) # nll(y | softmax(x))
am = np.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
am[i] = numpy.argmax(row)
am[i] = np.argmax(row)
m = row[am[i]]
# compute the unnormalized softmax, and normalization constant
sm[i] = numpy.exp(row - m)
sum_j = numpy.sum(sm[i]) # sum_j(exp(x[j] - m))
sm[i] = np.exp(row - m)
sum_j = np.sum(sm[i]) # sum_j(exp(x[j] - m))
# normalized our softmax
sm[i] *= 1.0 / sum_j
# store the nll
nll[i] = -row[y_idx[i]] + m + numpy.log(sum_j)
nll[i] = -row[y_idx[i]] + m + np.log(sum_j)
output_storage[0][0] = nll
output_storage[1][0] = sm
......@@ -1200,7 +1200,7 @@ class CrossentropySoftmax1HotWithBiasDx(gof.Op):
dy, sm, y_idx = input_storage
if any(y_idx < 0):
raise ValueError("y_i value out of bounds")
dx = numpy.zeros_like(sm)
dx = np.zeros_like(sm)
if dy.ndim == 0:
dy = dy[None]
incr = int(dy.shape[0] > 1)
......@@ -1391,7 +1391,7 @@ class CrossentropyCategorical1HotGrad(gof.Op):
def perform(self, node, inp, out):
g_y, coding_dist, true_one_of_n = inp
g_coding_strg, = out
g_coding = numpy.zeros_like(coding_dist)
g_coding = np.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]])
......@@ -1450,9 +1450,9 @@ class CrossentropyCategorical1Hot(gof.Op):
def perform(self, node, inp, out):
coding, one_of_n = inp
y_out, = out
y = numpy.zeros_like(coding[:, 0])
y = np.zeros_like(coding[:, 0])
for i in xrange(len(y)):
y[i] = -numpy.log(coding[i, one_of_n[i]])
y[i] = -np.log(coding[i, one_of_n[i]])
y_out[0] = y
def infer_shape(self, node, in_shapes):
......@@ -1659,9 +1659,9 @@ def _is_const(z, val, approx=False):
except tensor.NotScalarConstantError:
return False
if approx:
return numpy.allclose(maybe, val)
return np.allclose(maybe, val)
else:
return numpy.all(maybe == val)
return np.all(maybe == val)
@opt.register_specialize('fast_compile_gpu')
......@@ -1792,7 +1792,7 @@ def local_advanced_indexing_crossentropy_onehot_grad(node):
# set out_grad according to the numerator, it may be divided later
# num should be a vector or a scalar
if num.ndim == 1 or numpy.all(num.broadcastable):
if num.ndim == 1 or np.all(num.broadcastable):
out_grad *= -num
else:
return
......@@ -1818,7 +1818,7 @@ def local_advanced_indexing_crossentropy_onehot_grad(node):
rest = tensor.mul(*[other_inputs])
# Check that rest is a vector or a scalar
if rest.ndim == 1 or numpy.all(rest.broadcastable):
if rest.ndim == 1 or np.all(rest.broadcastable):
adv_subtensor = input
out_grad /= rest
break
......@@ -2099,14 +2099,14 @@ class Prepend_scalar_constant_to_each_row(gof.Op):
output, = out
new_shape = (mat.shape[0], mat.shape[1] + 1)
if output[0] is None:
output[0] = numpy.empty(new_shape, dtype=mat.dtype)
output[0] = np.empty(new_shape, dtype=mat.dtype)
out = output[0]
else:
if output[0].shape != new_shape:
try:
output[0].resize(new_shape)
except Exception:
output[0] = numpy.empty(new_shape, dtype=mat.dtype)
output[0] = np.empty(new_shape, dtype=mat.dtype)
out = output[0]
out[:, 0].fill(self.val.data)
......@@ -2147,14 +2147,14 @@ class Prepend_scalar_to_each_row(gof.Op):
output, = out
new_shape = (mat.shape[0], mat.shape[1] + 1)
if output[0] is None:
output[0] = numpy.empty(new_shape, dtype=mat.dtype)
output[0] = np.empty(new_shape, dtype=mat.dtype)
out = output[0]
else:
if output[0].shape != new_shape:
try:
output[0].resize(new_shape)
except Exception:
output[0] = numpy.empty(new_shape, dtype=mat.dtype)
output[0] = np.empty(new_shape, dtype=mat.dtype)
out = output[0]
out[:, 0].fill(val)
out[:, 1:] = mat
......
theano/tensor/nnet/sigm.py
浏览文件 @ e79c4e4c
......@@ -9,7 +9,7 @@ from __future__ import absolute_import, print_function, division
import warnings
import numpy
import numpy as np
import theano
from theano import config, gof, printing, scalar
......@@ -41,8 +41,8 @@ class ScalarSigmoid(scalar.UnaryScalarOp):
# half-precision (float16), where we want float32.
x_dtype = str(getattr(x, 'dtype', ''))
if x_dtype in ('int8', 'uint8'):
return 1.0 / (1.0 + numpy.exp(-x, sig='f'))
return 1.0 / (1.0 + numpy.exp(-x))
return 1.0 / (1.0 + np.exp(-x, sig='f'))
return 1.0 / (1.0 + np.exp(-x))
def impl(self, x):
return ScalarSigmoid.st_impl(x)
......@@ -134,8 +134,8 @@ class ScalarSigmoid(scalar.UnaryScalarOp):
This method was used to generate the graph: sigmoid_prec.png in the doc.
"""
data = numpy.arange(-15, 15, .1)
val = 1 / (1 + numpy.exp(-data))
data = np.arange(-15, 15, .1)
val = 1 / (1 + np.exp(-data))
def hard_sigmoid(x):
return theano.tensor.nnet.hard_sigmoid(x)
......@@ -330,8 +330,8 @@ class ScalarSoftplus(scalar.UnaryScalarOp):
# half-precision (float16), where we want float32.
x_dtype = str(getattr(x, 'dtype', ''))
if x_dtype in ('int8', 'uint8'):
return numpy.log1p(numpy.exp(x, sig='f'))
return numpy.log1p(numpy.exp(x))
return np.log1p(np.exp(x, sig='f'))
return np.log1p(np.exp(x))
def impl(self, x):
return ScalarSoftplus.static_impl(x)
......@@ -399,7 +399,7 @@ def _is_1(expr):
"""
try:
v = opt.get_scalar_constant_value(expr)
return numpy.allclose(v, 1)
return np.allclose(v, 1)
except tensor.NotScalarConstantError:
return False
......@@ -457,7 +457,7 @@ def is_1pexp(t, only_process_constants=True):
scal_sum = scalars[0]
for s in scalars[1:]:
scal_sum = scal_sum + s
if numpy.allclose(scal_sum, 1):
if np.allclose(scal_sum, 1):
return False, maybe_exp.owner.inputs[0]
# Before 7987b51 there used to be a bug where *any* constant
# was considered as if it was equal to 1, and thus this
......@@ -569,7 +569,7 @@ def is_neg(var):
for idx, mul_input in enumerate(apply.inputs):
try:
constant = opt.get_scalar_constant_value(mul_input)
is_minus_1 = numpy.allclose(constant, -1)
is_minus_1 = np.allclose(constant, -1)
except NotScalarConstantError:
is_minus_1 = False
if is_minus_1:
......@@ -968,7 +968,7 @@ def local_inv_1_plus_exp(node):
# 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):
if scalars and np.allclose(np.sum(scalars), 1):
out = opt._fill_chain(
sigmoid(
tensor.neg(nonconsts[0].owner.inputs[0])),
......@@ -999,7 +999,7 @@ def local_1msigmoid(node):
val_l = opt.get_scalar_constant_value(sub_l)
except Exception:
return
if numpy.allclose(numpy.sum(val_l), 1):
if np.allclose(np.sum(val_l), 1):
out = sigmoid(-sub_r.owner.inputs[0])
copy_stack_trace([sub_r, node.outputs[0]], out)
return [out]
......
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