def cmp(a_shp, b_shp):
a = numpy.random.randn(* a_shp).astype(numpy.float32)
b = numpy.random.randn(* b_shp).astype(numpy.float32)
x = tensor.ftensor3()
y = tensor.ftensor3()
f = theano.function([x, y],
batched_dot(x, y),
mode=mode_with_gpu)
z0 = numpy.asarray(f(a, b))
ga = cuda_ndarray.CudaNdarray(a)
gb = cuda_ndarray.CudaNdarray(b)
z1 = numpy.asarray(f(ga, gb))
z_test = numpy.sum(
a[:, :, :, None] * b[:, None, :, :], axis=-2)
z1 = numpy.asarray(f(ga, gb))
z_test = numpy.sum(
a[:, :, :, None] * b[:, None, :, :], axis=-2)
unittest_tools.assert_allclose(z0, z_test)
unittest_tools.assert_allclose(z1, z_test)
def fail(a_shp, b_shp):
a = numpy.random.randn(*a_shp).astype(numpy.float32)
b = numpy.random.randn(*b_shp).astype(numpy.float32)
x = tensor.ftensor3()
y = tensor.ftensor3()
f = theano.function([x, y], batched_dot(x, y), mode=mode_with_gpu)
z = f(a, b)
def fail(a_shp, b_shp):
a=numpy.random.randn(*a_shp).astype(numpy.float32)
b=numpy.random.randn(*b_shp).astype(numpy.float32)
x=tensor.ftensor3()
y=tensor.ftensor3()
f=theano.function([x,y], batched_dot(x,y), mode=mode_with_gpu)
z = f(a,b)
def get_output(self, train):
X = self.get_input(train)
output = self.activation(batched_dot(self.W, X.dimshuffle(1, 2, 0)).dimshuffle(2, 0, 1) +
self.b)
return output
