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pytensor
提交 f7e2eec4 authored 作者: James Bergstra's avatar James Bergstra
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modified dtype of random functions to upcast from their arguments rather than to always use floatX

上级 5ba30909
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正在显示 包含 33 行增加 和 20 行删除
theano/tensor/raw_random.py
浏览文件 @ f7e2eec4
......@@ -332,6 +332,7 @@ def uniform(random_state, size=None, low=0.0, high=1.0, ndim=None, dtype=theano.
low = tensor.as_tensor_variable(low)
high = tensor.as_tensor_variable(high)
ndim, size = _infer_ndim(ndim, size, low, high)
dtype = tensor.scal.upcast(dtype, low.dtype, high.dtype)
op = RandomFunction('uniform',
tensor.TensorType(dtype = dtype, broadcastable = (False,)*ndim) )
return op(random_state, size, low, high)
......@@ -368,6 +369,7 @@ def normal(random_state, size=None, avg=0.0, std=1.0, ndim=None, dtype=theano.co
avg = tensor.as_tensor_variable(avg)
std = tensor.as_tensor_variable(std)
ndim, size = _infer_ndim(ndim, size, avg, std)
dtype = tensor.scal.upcast(dtype, avg.dtype, std.dtype)
op = RandomFunction('normal',
tensor.TensorType(dtype = dtype, broadcastable = (False,)*ndim) )
return op(random_state, size, avg, std)
......
theano/tensor/tests/test_raw_random.py
浏览文件 @ f7e2eec4
......@@ -430,8 +430,8 @@ class T_random_function(unittest.TestCase):
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
post0, val0 = f(rng_state0)
post1, val1 = f(post0)
numpy_val0 = numpy_rng.uniform()
numpy_val1 = numpy_rng.uniform()
numpy_val0 = numpy.asarray(numpy_rng.uniform(), dtype=theano.config.floatX)
numpy_val1 = numpy.asarray(numpy_rng.uniform(), dtype=theano.config.floatX)
assert numpy.all(val0 == numpy_val0)
assert numpy.all(val1 == numpy_val1)
......@@ -439,7 +439,8 @@ class T_random_function(unittest.TestCase):
post_r, out = multinomial(rng_R)
g = compile.function([rng_R], [post_r, out], accept_inplace=True)
post2, val2 = g(post1)
numpy_val2 = numpy_rng.multinomial(n=1, pvals=[.5, .5])
numpy_val2 = numpy.asarray(numpy_rng.multinomial(n=1, pvals=[.5, .5]),
dtype=theano.config.floatX)
assert numpy.all(val2 == numpy_val2)
......@@ -450,12 +451,15 @@ class T_random_function(unittest.TestCase):
assert out.ndim == 1
f = compile.function([rng_R, low], [post_r, out], accept_inplace=True)
def as_floatX(thing):
return numpy.asarray(thing, dtype=theano.config.floatX)
rng_state0 = numpy.random.RandomState(utt.fetch_seed())
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
post0, val0 = f(rng_state0, [-5, .5, 0, 1])
post1, val1 = f(post0, [.9])
numpy_val0 = numpy_rng.uniform(low=[-5, .5, 0, 1], high=1)
numpy_val1 = numpy_rng.uniform(low=[.9], high=1)
numpy_val0 = as_floatX(numpy_rng.uniform(low=[-5, .5, 0, 1], high=1))
numpy_val1 = as_floatX(numpy_rng.uniform(low=as_floatX([.9]), high=1))
assert numpy.all(val0 == numpy_val0)
assert numpy.all(val1 == numpy_val1)
......@@ -467,8 +471,8 @@ class T_random_function(unittest.TestCase):
post0b, val0b = fb(post1, [-4., -2], [-1, 0])
post1b, val1b = fb(post0b, [-4.], [-1])
numpy_val0b = numpy_rng.uniform(low=[-4., -2], high=[-1, 0])
numpy_val1b = numpy_rng.uniform(low=[-4.], high=[-1])
numpy_val0b = as_floatX(numpy_rng.uniform(low=[-4., -2], high=[-1, 0]))
numpy_val1b = as_floatX(numpy_rng.uniform(low=[-4.], high=[-1]))
assert numpy.all(val0b == numpy_val0b)
assert numpy.all(val1b == numpy_val1b)
self.assertRaises(ValueError, fb, post1b, [-4., -2], [-1, 0, 1])
......@@ -480,8 +484,8 @@ class T_random_function(unittest.TestCase):
fc = compile.function([rng_R, low, high, size], [post_rc, outc], accept_inplace=True)
post0c, val0c = fc(post1b, [-4., -2], [-1, 0], [2])
post1c, val1c = fc(post0c, [-4.], [-1], [1])
numpy_val0c = numpy_rng.uniform(low=[-4., -2], high=[-1, 0])
numpy_val1c = numpy_rng.uniform(low=[-4.], high=[-1])
numpy_val0c = as_floatX(numpy_rng.uniform(low=[-4., -2], high=[-1, 0]))
numpy_val1c = as_floatX(numpy_rng.uniform(low=[-4.], high=[-1]))
assert numpy.all(val0c == numpy_val0c)
assert numpy.all(val1c == numpy_val1c)
self.assertRaises(ValueError, fc, post1c, [-4., -2], [-1, 0], [1])
......@@ -493,8 +497,8 @@ class T_random_function(unittest.TestCase):
def test_broadcast_arguments(self):
rng_R = random_state_type()
low = tensor.vector()
high = tensor.col()
low = tensor.dvector()
high = tensor.dcol()
post_r, out = uniform(rng_R, low=low, high=high)
assert out.ndim == 2
f = compile.function([rng_R, low, high], [post_r, out], accept_inplace=True)
......@@ -509,7 +513,7 @@ class T_random_function(unittest.TestCase):
numpy_val1 = numpy_rng.uniform(low=[.9], high=[[1.], [1.1], [1.5]])
numpy_val2 = numpy_rng.uniform(low=[-5, .5, 0, 1], high=[[1.], [1.1], [1.5]])
assert numpy.all(val0 == numpy_val0)
assert numpy.all(val0 == numpy_val0), (val0, numpy_val0)
assert numpy.all(val1 == numpy_val1)
assert numpy.all(val2 == numpy_val2)
......@@ -521,19 +525,21 @@ class T_random_function(unittest.TestCase):
assert out.ndim == 1
f = compile.function([rng_R, low, high], [post_r, out], accept_inplace=True)
low_val = [.1, .2, .3]
high_val = [1.1, 2.2, 3.3]
def as_floatX(thing):
return numpy.asarray(thing, dtype=theano.config.floatX)
low_val = as_floatX([.1, .2, .3])
high_val = as_floatX([1.1, 2.2, 3.3])
rng = numpy.random.RandomState(utt.fetch_seed())
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
# Arguments of size (3,)
rng0, val0 = f(rng, low_val, high_val)
numpy_val0 = numpy_rng.uniform(low=low_val, high=high_val)
numpy_val0 = as_floatX(numpy_rng.uniform(low=low_val, high=high_val))
assert numpy.all(val0 == numpy_val0)
# arguments of size (2,)
rng1, val1 = f(rng0, low_val[:-1], high_val[:-1])
numpy_val1 = numpy_rng.uniform(low=low_val[:-1], high=high_val[:-1])
numpy_val1 = as_floatX(numpy_rng.uniform(low=low_val[:-1], high=high_val[:-1]))
assert numpy.all(val1 == numpy_val1)
# Specifying the size explicitly
......@@ -541,7 +547,7 @@ class T_random_function(unittest.TestCase):
uniform(rng_R, low=low, high=high, size=(3,)),
accept_inplace=True)
rng2, val2 = g(rng1, low_val, high_val)
numpy_val2 = numpy_rng.uniform(low=low_val, high=high_val, size=(3,))
numpy_val2 = as_floatX(numpy_rng.uniform(low=low_val, high=high_val, size=(3,)))
assert numpy.all(val2 == numpy_val2)
self.assertRaises(ValueError, g, rng2, low_val[:-1], high_val[:-1])
......@@ -591,13 +597,17 @@ class T_random_function(unittest.TestCase):
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
# Arguments of size (3,)
def as_floatX(thing):
return numpy.asarray(thing, dtype=theano.config.floatX)
rng0, val0 = f(rng, avg_val, std_val)
numpy_val0 = numpy_rng.normal(loc=avg_val, scale=std_val)
numpy_val0 = as_floatX(numpy_rng.normal(loc=as_floatX(avg_val),
scale=as_floatX(std_val)))
assert numpy.all(val0 == numpy_val0)
# arguments of size (2,)
rng1, val1 = f(rng0, avg_val[:-1], std_val[:-1])
numpy_val1 = numpy_rng.normal(loc=avg_val[:-1], scale=std_val[:-1])
numpy_val1 = numpy.asarray(numpy_rng.normal(loc=avg_val[:-1], scale=std_val[:-1]),
dtype=theano.config.floatX)
assert numpy.all(val1 == numpy_val1)
# Specifying the size explicitly
......@@ -605,7 +615,8 @@ class T_random_function(unittest.TestCase):
normal(rng_R, avg=avg, std=std, size=(3,)),
accept_inplace=True)
rng2, val2 = g(rng1, avg_val, std_val)
numpy_val2 = numpy_rng.normal(loc=avg_val, scale=std_val, size=(3,))
numpy_val2 = numpy.asarray(numpy_rng.normal(loc=avg_val, scale=std_val, size=(3,)),
dtype=theano.config.floatX)
assert numpy.all(val2 == numpy_val2)
self.assertRaises(ValueError, g, rng2, avg_val[:-1], std_val[:-1])
......
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