def test_relu_after_conv2d_batchnorm(self):
input_layer = layers.Input(batch_shape=(None, 2, 2, 2))
conv_layer = layers.Conv2D(kernel=np.random.random(
(2, 2, 2, 2)).astype("float32"),
bias=np.random.random(
(2, )).astype("float32"),
conv_mxts_mode=ConvMxtsMode.Linear,
strides=(1, 1),
padding=PaddingMode.valid,
data_format="channels_last")
conv_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(conv_layer)
relu_after_bn = layers.ReLU(
nonlinear_mxts_mode=NonlinearMxtsMode.DeepLIFT_GenomicsDefault)
relu_after_bn.set_inputs(batch_norm)
relu_after_bn.build_fwd_pass_vars()
self.assertEqual(relu_after_bn.nonlinear_mxts_mode,
NonlinearMxtsMode.Rescale)
def prepare_batch_norm_deeplift_model(self, axis):
self.input_layer = layers.Input(batch_shape=(None, 2, 2, 2))
self.batch_norm_layer = layers.BatchNormalization(gamma=self.gamma,
beta=self.beta,
axis=axis,
mean=self.mean,
var=self.var,
epsilon=self.epsilon)
self.batch_norm_layer.set_inputs(self.input_layer)
self.flatten_layer = layers.Flatten()
self.flatten_layer.set_inputs(self.batch_norm_layer)
self.dense_layer = layers.Dense(kernel=np.ones(
(1, 8)).astype("float32").T,
bias=np.zeros(1).astype("float32"),
dense_mxts_mode=DenseMxtsMode.Linear)
self.dense_layer.set_inputs(self.flatten_layer)
self.dense_layer.build_fwd_pass_vars()
self.dense_layer.set_scoring_mode(layers.ScoringMode.OneAndZeros)
self.dense_layer.set_active()
self.dense_layer.update_task_index(0)
self.input_layer.update_mxts()
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)