def test_linear_ones(basic_linargs, transformer_factory):
# basic sanity check with all ones on the inputs
# and weights, check that each row in output
# is the sum of the weights for that output
# this check will confirm that the correct number
# of operations is being run
nin, nout, batch_size = basic_linargs
# set inputs
N = ng.make_axis(batch_size, name="N", batch=True)
F = ng.make_axis(nin, name="F")
inp = ng.placeholder([F, N])
layer = Linear(nout=nout, init=UniformInit(1.0, 1.0))
fprop = layer.train_outputs(inp)
# create data
x = np.ones((nin, batch_size))
# evaluate
ngt.make_transformer()
out, w = executor([fprop, layer.W], inp)(x)
sums = np.sum(w, 1).reshape((nout, 1)) * np.ones((1, batch_size))
assert np.allclose(sums, out, atol=0.0,
rtol=0.0), '%e' % np.max(np.abs(out - sums))
def test_linear_zeros(basic_linargs, transformer_factory):
# basic sanity check with 0 weights random inputs
nin, nout, batch_size = basic_linargs
# set inputs
N = ng.make_axis(batch_size, name="N", batch=True)
F = ng.make_axis(nin, name="F")
inp = ng.placeholder([F, N])
layer = Linear(nout=nout, init=UniformInit(0.0, 0.0))
fprop = layer.train_outputs(inp)
# create data
x = np.random.random((nin, batch_size))
# evaluate
ngt.make_transformer()
out = executor(fprop, inp)(x)
assert np.min(out) == 0.0 and np.max(out) == 0.0