def test_layer_mode_setting(input_placeholder):
"""
Make sure that Layer.inference_mode sets the correct mode for the graph
"""
layer = SimpleLayer()
with Layer.inference_mode_on():
layer(input_placeholder)
assert "inference" in layer.modes
# Make sure all ops in subgraph are in mode
for op in layer:
assert op in layer.modes["inference"].ops
def make_network(scope1=None, scope2=None):
# 2 layer network, each layer has its own scope
x = ng.placeholder(axes) # inputs
t = ng.placeholder(ng.make_axes([ng.make_axis(length=1),
N])) # targets
with Layer.variable_scope(scope1):
layer1 = Affine(ConstantInit(val=Wlin1),
nout=nout1,
bias_init=ConstantInit(val=Wbias1),
activation=Rectlin(),
batch_norm=False)
with Layer.variable_scope(scope2):
layer2 = Affine(ConstantInit(val=Wlin2),
nout=1,
bias_init=ConstantInit(val=Wbias2),
activation=Logistic(),
batch_norm=False)
seq = Sequential([layer1, layer2])
p_t = seq(x)
t_cast = ng.cast_axes(t, p_t.axes) # TODO: how can this be avoided?
loss = ng.cross_entropy_binary(p_t, t_cast)
return seq, x, t, loss
def test_dropout_inference(nin, batch_size, transformer_factory):
# set inputs
N = ng.make_axis(batch_size, name='N')
F = ng.make_axis(nin, name='F')
inp = ng.placeholder([F, N])
layer = Dropout(keep=0.5)
with Layer.inference_mode_on():
fprop = layer(inp)
# create data
x = np.random.uniform(size=(nin, batch_size))
# evaluate
with ExecutorFactory() as ex:
comp = ex.executor(fprop, inp)
out = comp(x)
numpy_out = x * 0.5
ng.testing.assert_allclose(out, numpy_out, atol=atol, rtol=rtol)
out1 = out.copy()
out2 = comp(x)
ng.testing.assert_allclose(out1, out2, atol=atol, rtol=rtol)