def test_fprop(self):
conv_layer = layers.Conv2D(kernel=self.conv_W, bias=self.conv_b,
strides=(1,1),
padding=PaddingMode.valid,
data_format="channels_last",
conv_mxts_mode="Linear")
self.create_small_net_with_conv_layer(conv_layer,
outputs_per_channel=9)
func = compile_func([self.input_layer.get_activation_vars()],
self.conv_layer.get_activation_vars())
np.testing.assert_almost_equal(func(self.inp),
np.array(
[[[[439, 467, 495],
[551, 579, 607],
[663, 691, 719]],
[[-439, -467, -495],
[-551, -579, -607],
[-663, -691, -719]],],
[[[1335, 1363, 1391],
[1447, 1475, 1503],
[1559, 1587, 1615],],
[[-1335, -1363, -1391],
[-1447, -1475, -1503],
[-1559, -1587, -1615]]]]).transpose(0,2,3,1))
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_dense_backprop(self):
conv_layer = layers.Conv2D(kernel=self.conv_W,
bias=self.conv_b,
strides=(1, 1),
padding=PaddingMode.valid,
data_format="channels_last",
conv_mxts_mode="Linear")
self.create_small_net_with_conv_layer(conv_layer,
outputs_per_channel=9)
self.dense_layer.update_task_index(task_index=0)
func = compile_func([
self.input_layer.get_activation_vars(),
self.input_layer.get_reference_vars()
], self.input_layer.get_mxts())
np.testing.assert_almost_equal(
func([self.inp, np.zeros_like(self.inp)]),
np.array([[[[0, 2, 2, 2], [4, 12, 12, 8], [4, 12, 12, 8],
[4, 10, 10, 6]],
[[8, 18, 18, 10], [20, 44, 44, 24], [20, 44, 44, 24],
[12, 26, 26, 14]]],
[[[0, 2, 2, 2], [4, 12, 12, 8], [4, 12, 12, 8],
[4, 10, 10, 6]],
[[8, 18, 18, 10], [20, 44, 44, 24], [20, 44, 44, 24],
[12, 26, 26, 14]]]]).transpose(0, 2, 3, 1))
def test_relu_after_conv2d(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)
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)