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提交 64726dfb authored 作者: amrithasuresh's avatar amrithasuresh
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Updated numpy as np

上级 3e3e3093
隐藏空白字符变更
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正在显示 包含 25 行增加 和 25 行删除
theano/tensor/nnet/nnet.py
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......@@ -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
......@@ -620,7 +620,7 @@ 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,
lsm = xdev - np.log(np.sum(np.exp(xdev), axis=1,
keepdims=True))
output_storage[0][0] = lsm
......@@ -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
......
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