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 test_relu_after_other_layer(self):
input_layer = layers.Input(batch_shape=(None,4))
relu_layer = layers.ReLU(
nonlinear_mxts_mode=
NonlinearMxtsMode.DeepLIFT_GenomicsDefault)
relu_layer.set_inputs(input_layer)
relu_layer.build_fwd_pass_vars()
def test_relu_after_dense(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)
relu_after_dense = layers.ReLU(
nonlinear_mxts_mode=NonlinearMxtsMode.DeepLIFT_GenomicsDefault)
relu_after_dense.set_inputs(dense_layer)
relu_after_dense.build_fwd_pass_vars()
self.assertEqual(relu_after_dense.nonlinear_mxts_mode,
NonlinearMxtsMode.RevealCancel)
def test_relu_after_conv1d(self):
input_layer = layers.Input(batch_shape=(None,2,2))
conv_layer = layers.Conv1D(
kernel=np.random.random((2,2,2)).astype("float32"),
bias=np.random.random((2,)).astype("float32"),
conv_mxts_mode=ConvMxtsMode.Linear,
stride=1,
padding=PaddingMode.valid)
conv_layer.set_inputs(input_layer)
relu_after_conv = layers.ReLU(nonlinear_mxts_mode=
NonlinearMxtsMode.DeepLIFT_GenomicsDefault)
relu_after_conv.set_inputs(conv_layer)
relu_after_conv.build_fwd_pass_vars()
self.assertEqual(relu_after_conv.nonlinear_mxts_mode,
NonlinearMxtsMode.Rescale)
def test_relu_gradient(self):
out_layer = layers.ReLU(
nonlinear_mxts_mode=layers.NonlinearMxtsMode.Gradient)
fprop_results, bprop_results_each_task =\
self.set_up_prediction_func_and_deeplift_func(out_layer)
np.testing.assert_almost_equal(
np.array(bprop_results_each_task[0]),
np.array([np.array(self.w1), np.array(self.w1)]),
decimal=5)
np.testing.assert_almost_equal(
np.array(bprop_results_each_task[1]),
np.array([np.zeros_like(self.w2),
np.zeros_like(self.w2)]),
decimal=5)
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 test_relu_rescale(self):
out_layer = layers.ReLU(
nonlinear_mxts_mode=layers.NonlinearMxtsMode.Rescale)
fprop_results, bprop_results_each_task =\
self.set_up_prediction_func_and_deeplift_func(out_layer)
self.assertListEqual(fprop_results, [[9.0, 0.0], [19.0, 0.0]])
#post-activation under default would be [0.0, 1.0, 0.0]
#post-activation diff from default = [9.0, -1.0, 4.0], [19.0, -1.0, -4.0]
#pre-activation under default would be [-1.0, 1.0]
#pre-activation diff-from-default is [10.0, -10.0], [20.0, -20.0]
#scale-factors: [[9.0/10.0, -1.0/-10.0], [19.0/20.0, -1.0/-20.0]]
print(bprop_results_each_task)
np.testing.assert_almost_equal(np.array(bprop_results_each_task[0]),
np.array([
(9.0 / 10.0) * np.array(self.w1),
(19.0 / 20.0) * np.array(self.w1)
]),
decimal=5)
np.testing.assert_almost_equal(np.array(bprop_results_each_task[1]),
np.array([
(-1.0 / -10.0) * np.array(self.w2),
(-1.0 / -20.0) * np.array(self.w2)
]),
decimal=5)
def test_running_of_different_activation_modes(self):
#just tests that things run, not a test for values
for mode in layers.NonlinearMxtsMode.vals:
out_layer = layers.ReLU(nonlinear_mxts_mode=mode)
fprop_results, bprop_results_each_task =\
self.set_up_prediction_func_and_deeplift_func(out_layer)