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提交 9614222e authored 作者: amrithasuresh's avatar amrithasuresh
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Updated numpy as np

上级 cdaed905
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正在显示 包含 44 行增加 和 44 行删除
theano/tensor/nnet/tests/test_bn.py
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......@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import theano
import theano.tensor as T
from theano.tests import unittest_tools as utt
import numpy
import numpy as np
from theano.tensor.nnet import bn
......@@ -17,12 +17,12 @@ def test_BNComposite():
n = (x - M) / V
return n * G + B
numpy.random.seed(1234)
X = 1 + numpy.random.random([10, 20]).astype('float32')
B = 1 + numpy.random.random([20]).astype('float32')
G = 1 + numpy.random.random([20]).astype('float32')
M = 1 + numpy.random.random([20]).astype('float32')
V = 1 + numpy.random.random([20]).astype('float32')
np.random.seed(1234)
X = 1 + np.random.random([10, 20]).astype('float32')
B = 1 + np.random.random([20]).astype('float32')
G = 1 + np.random.random([20]).astype('float32')
M = 1 + np.random.random([20]).astype('float32')
V = 1 + np.random.random([20]).astype('float32')
x = theano.tensor.matrix('x')
b = theano.tensor.vector('b')
......@@ -30,11 +30,11 @@ def test_BNComposite():
m = theano.tensor.vector('m')
v = theano.tensor.vector('v')
x.tag.test_value = numpy.random.rand(2, 2).astype(theano.config.floatX)
b.tag.test_value = numpy.random.rand(2).astype(theano.config.floatX)
g.tag.test_value = numpy.random.rand(2).astype(theano.config.floatX)
m.tag.test_value = numpy.random.rand(2).astype(theano.config.floatX)
v.tag.test_value = numpy.random.rand(2).astype(theano.config.floatX)
x.tag.test_value = np.random.rand(2, 2).astype(theano.config.floatX)
b.tag.test_value = np.random.rand(2).astype(theano.config.floatX)
g.tag.test_value = np.random.rand(2).astype(theano.config.floatX)
m.tag.test_value = np.random.rand(2).astype(theano.config.floatX)
v.tag.test_value = np.random.rand(2).astype(theano.config.floatX)
bn_ref_op = bn_ref(x, g, b, m, v)
f_ref = theano.function([x, b, g, m, v], [bn_ref_op])
......@@ -54,12 +54,12 @@ def test_batch_normalization():
n = (x - M) / V
return n * G + B
numpy.random.seed(1234)
X = 1 + numpy.random.random([10, 20]).astype('float32')
B = 1 + numpy.random.random([20]).astype('float32')
G = 1 + numpy.random.random([20]).astype('float32')
M = 1 + numpy.random.random([20]).astype('float32')
V = 1 + numpy.random.random([20]).astype('float32')
np.random.seed(1234)
X = 1 + np.random.random([10, 20]).astype('float32')
B = 1 + np.random.random([20]).astype('float32')
G = 1 + np.random.random([20]).astype('float32')
M = 1 + np.random.random([20]).astype('float32')
V = 1 + np.random.random([20]).astype('float32')
x = theano.tensor.matrix('x')
b = theano.tensor.vector('b')
......@@ -92,7 +92,7 @@ def test_batch_normalization():
def bn_f(inputs, gamma, beta, mean, std):
return bn.batch_normalization(inputs, gamma, beta, mean, std, mode=mode)
utt.verify_grad(bn_f, [X, G, B,
X.mean(axis=0)[numpy.newaxis], X.std(axis=0)[numpy.newaxis]])
X.mean(axis=0)[np.newaxis], X.std(axis=0)[np.newaxis]])
def test_bn_feature_maps():
......@@ -101,12 +101,12 @@ def test_bn_feature_maps():
n = (x - M) / V
return n * G + B
numpy.random.seed(1234)
X = 1 + numpy.random.random([2, 3, 4, 4]).astype('float32')
B = 1 + numpy.random.random([3]).astype('float32')
G = 1 + numpy.random.random([3]).astype('float32')
M = 1 + numpy.random.random([3]).astype('float32')
V = 1 + numpy.random.random([3]).astype('float32')
np.random.seed(1234)
X = 1 + np.random.random([2, 3, 4, 4]).astype('float32')
B = 1 + np.random.random([3]).astype('float32')
G = 1 + np.random.random([3]).astype('float32')
M = 1 + np.random.random([3]).astype('float32')
V = 1 + np.random.random([3]).astype('float32')
x = theano.tensor.tensor4('x')
b = theano.tensor.vector('b')
......@@ -205,20 +205,20 @@ def test_batch_normalization_train():
data_shape = data_shape[:ndim]
param_shape = tuple(1 if d in axes2 else s
for d, s in enumerate(data_shape))
X = 4 + 3 * numpy.random.randn(*data_shape).astype(theano.config.floatX)
Dy = -1 + 2 * numpy.random.randn(*data_shape).astype(theano.config.floatX)
Scale = numpy.random.randn(*param_shape).astype(theano.config.floatX)
Bias = numpy.random.randn(*param_shape).astype(theano.config.floatX)
Running_mean = numpy.random.randn(*param_shape).astype(theano.config.floatX)
Running_var = numpy.random.randn(*param_shape).astype(theano.config.floatX)
X = 4 + 3 * np.random.randn(*data_shape).astype(theano.config.floatX)
Dy = -1 + 2 * np.random.randn(*data_shape).astype(theano.config.floatX)
Scale = np.random.randn(*param_shape).astype(theano.config.floatX)
Bias = np.random.randn(*param_shape).astype(theano.config.floatX)
Running_mean = np.random.randn(*param_shape).astype(theano.config.floatX)
Running_var = np.random.randn(*param_shape).astype(theano.config.floatX)
outputs = f(X, Scale, Bias, Running_mean, Running_var, Dy)
# compare outputs
utt.assert_allclose(outputs[0], outputs[0 + 5]) # out
utt.assert_allclose(outputs[1], outputs[1 + 5]) # mean
utt.assert_allclose(outputs[2], outputs[2 + 5]) # invstd
utt.assert_allclose(outputs[3], outputs[3 + 5]) # running_mean
utt.assert_allclose(numpy.nan_to_num(outputs[4]),
numpy.nan_to_num(outputs[4 + 5])) # running_var
utt.assert_allclose(np.nan_to_num(outputs[4]),
np.nan_to_num(outputs[4 + 5])) # running_var
# compare gradients
utt.assert_allclose(outputs[10], outputs[10 + 3], atol=1e-4) # dx
utt.assert_allclose(outputs[11], outputs[11 + 3], rtol=2e-4, atol=1e-4) # dscale
......@@ -245,10 +245,10 @@ def test_batch_normalization_train_without_running_averages():
bn.AbstractBatchNormTrainGrad))
for n in f.maker.fgraph.toposort()])
# run
X = 4 + 3 * numpy.random.randn(*data_shape).astype(theano.config.floatX)
Dy = -1 + 2 * numpy.random.randn(*data_shape).astype(theano.config.floatX)
Scale = numpy.random.randn(*param_shape).astype(theano.config.floatX)
Bias = numpy.random.randn(*param_shape).astype(theano.config.floatX)
X = 4 + 3 * np.random.randn(*data_shape).astype(theano.config.floatX)
Dy = -1 + 2 * np.random.randn(*data_shape).astype(theano.config.floatX)
Scale = np.random.randn(*param_shape).astype(theano.config.floatX)
Bias = np.random.randn(*param_shape).astype(theano.config.floatX)
f(X, Scale, Bias, Dy)
......@@ -330,7 +330,7 @@ def test_batch_normalization_train_broadcast():
if theano.config.mode != "FAST_COMPILE":
assert len(nodes) == 1
assert isinstance(nodes[0].op, theano.compile.DeepCopyOp)
inputs = [numpy.asarray(numpy.random.rand(*((4,) * n)), x.dtype)
inputs = [np.asarray(np.random.rand(*((4,) * n)), x.dtype)
for n in [x.ndim, scale.ndim, bias.ndim,
running_mean.ndim, running_var.ndim]]
assert 0.0 == f(*inputs)
......@@ -381,12 +381,12 @@ def test_batch_normalization_test():
data_shape = data_shape[:ndim]
param_shape = tuple(1 if d in axes2 else s
for d, s in enumerate(data_shape))
X = 4 + 3 * numpy.random.randn(*data_shape).astype(theano.config.floatX)
Dy = -1 + 2 * numpy.random.randn(*data_shape).astype(theano.config.floatX)
Scale = numpy.random.randn(*param_shape).astype(theano.config.floatX)
Bias = numpy.random.randn(*param_shape).astype(theano.config.floatX)
Mean = numpy.random.randn(*param_shape).astype(theano.config.floatX)
Var = numpy.random.rand(*param_shape).astype(theano.config.floatX)
X = 4 + 3 * np.random.randn(*data_shape).astype(theano.config.floatX)
Dy = -1 + 2 * np.random.randn(*data_shape).astype(theano.config.floatX)
Scale = np.random.randn(*param_shape).astype(theano.config.floatX)
Bias = np.random.randn(*param_shape).astype(theano.config.floatX)
Mean = np.random.randn(*param_shape).astype(theano.config.floatX)
Var = np.random.rand(*param_shape).astype(theano.config.floatX)
outputs = f(X, Scale, Bias, Mean, Var, Dy)
# compare outputs
utt.assert_allclose(outputs[0], outputs[1]) # out
......
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