def test_relu_after_dense_batchnorm_noop_noop(self):
input_layer = layers.Input(batch_shape=(None, 4))
dense_layer = layers.Dense(kernel=np.random.random((4, 2)),
bias=np.random.random((2, )),
dense_mxts_mode=DenseMxtsMode.Linear)
dense_layer.set_inputs(input_layer)
batch_norm = layers.BatchNormalization(
gamma=np.array([1.0, 1.0]).astype("float32"),
beta=np.array([-0.5, 0.5]).astype("float32"),
axis=-1,
mean=np.array([-0.5, 0.5]).astype("float32"),
var=np.array([1.0, 1.0]).astype("float32"),
epsilon=0.001)
batch_norm.set_inputs(dense_layer)
noop_layer1 = layers.NoOp()
noop_layer1.set_inputs(batch_norm)
noop_layer2 = layers.NoOp()
noop_layer2.set_inputs(noop_layer1)
relu_after_bn = layers.ReLU(
nonlinear_mxts_mode=NonlinearMxtsMode.DeepLIFT_GenomicsDefault)
relu_after_bn.set_inputs(noop_layer2)
relu_after_bn.build_fwd_pass_vars()
self.assertEqual(relu_after_bn.nonlinear_mxts_mode,
NonlinearMxtsMode.RevealCancel)
def noop_conversion(name, **kwargs):
return [layers.NoOp(name=name)]