def test_fprop_pos_and_neg_contribs(self):
conv_layer = blobs.Conv1D(W=self.conv_W,
b=self.conv_b,
stride=1,
border_mode=PaddingMode.valid,
channels_come_last=False,
conv_mxts_mode=ConvMxtsMode.Linear)
self.create_small_net_with_conv_layer(conv_layer,
outputs_per_channel=3)
pos_contribs, neg_contribs = self.conv_layer.get_pos_and_neg_contribs()
func_pos = compile_func([
self.input_layer.get_activation_vars(),
self.input_layer.get_reference_vars()
], pos_contribs)
func_neg = compile_func([
self.input_layer.get_activation_vars(),
self.input_layer.get_reference_vars()
], neg_contribs)
#diff from ref:
# [[[-9,-8,-7,-6],
# [-5,-4,-3,-2]],
# [[-1, 0, 1, 2],
# [ 3, 4, 5, 6]]]
# W:
# [-2,-1
# 0, 1]
# 18+8 = 26, -4 = -4
# 0+-1+0+5 = 4 - bias (1.0) = 3
np.testing.assert_almost_equal(
func_pos(self.inp, np.ones_like(self.inp)),
np.array([[[26, 23, 20], [4, 3, 2]], [[6, 5, 6], [0, 1, 4]]]))
np.testing.assert_almost_equal(
func_neg(self.inp, np.ones_like(self.inp)),
np.array([[[-4, -3, -2], [-26, -23, -20]],
[[0, -1, -4], [-6, -5, -6]]]))
def test_fprop(self):
conv_layer = blobs.Conv1D(W=self.conv_W,
b=self.conv_b,
stride=1,
border_mode=PaddingMode.valid,
channels_come_last=False,
conv_mxts_mode=ConvMxtsMode.Linear)
self.create_small_net_with_conv_layer(conv_layer,
outputs_per_channel=3)
func = compile_func([self.input_layer.get_activation_vars()],
self.conv_layer.get_activation_vars())
#input:
# [[[-8,-7,-6,-5],
# [-4,-3,-2,-1]],
# [[ 0, 1, 2, 3],
# [ 4, 5, 6, 7]]]
# W:
# [-2,-1
# 0, 1]
# 16+7+0+-3 = 20 - bias (1.0) = 19
# 0+-1+0+5 = 4 - bias (1.0) = 3
np.testing.assert_almost_equal(
func(self.inp),
np.array([[[19, 17, 15], [-19, -17, -15]], [[3, 1, -1],
[-3, -1, 1]]]))
def test_fprop_stride(self):
conv_layer = blobs.Conv1D(W=self.conv_W,
b=self.conv_b,
stride=2,
border_mode=PaddingMode.valid,
channels_come_last=False)
self.create_small_net_with_conv_layer(conv_layer,
outputs_per_channel=3)
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([[[23, 35], [-23, -35]], [[71, 83], [-71, -83]]]))
def test_relu_after_conv1d(self):
input_layer = blobs.Input(num_dims=None, shape=(None, 2, 2))
conv_layer = blobs.Conv1D(W=np.random.random((2, 2, 2)),
b=np.random.random((2, )),
conv_mxts_mode=ConvMxtsMode.Linear,
stride=1,
border_mode=PaddingMode.valid,
channels_come_last=True)
conv_layer.set_inputs(input_layer)
relu_after_conv = blobs.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_dense_backprop_stride(self):
conv_layer = blobs.Conv1D(W=self.conv_W,
b=self.conv_b,
stride=2,
border_mode=PaddingMode.valid,
channels_come_last=False)
self.create_small_net_with_conv_layer(conv_layer,
outputs_per_channel=2)
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, 0, 2], [4, 6, 4, 6]], [[0, 2, 0, 2],
[4, 6, 4, 6]]]))
def conv1d_conversion(layer, name, verbose, nonlinear_mxts_mode,
conv_mxts_mode, **kwargs):
#nonlinear_mxts_mode only used for activation
print(layer.get_config())
converted_activation = activation_conversion(
layer, name, verbose, nonlinear_mxts_mode=nonlinear_mxts_mode)
print(layer.get_weights()[0].shape)
to_return = [
blobs.Conv1D(
name=("preact_" if len(converted_activation) > 0 else "") + name,
# W=layer.get_weights()[0].squeeze(1),
W=layer.get_weights()[0],
b=layer.get_weights()[1],
stride=layer.get_config()[KerasKeys.strides][0],
padding_mode=layer.get_config()[KerasKeys.padding].upper(),
conv_mxts_mode=conv_mxts_mode)
]
to_return.extend(converted_activation)
return to_return
def conv1d_conversion(layer, name, verbose, nonlinear_mxts_mode, **kwargs):
#nonlinear_mxts_mode only used for activation
converted_activation = activation_conversion(
layer, name, verbose, nonlinear_mxts_mode=nonlinear_mxts_mode)
W = layer.get_weights()[0]
if (W.shape[-1] != 1): #is NHWC and not NCHW - need to transpose
W = W.transpose(3, 2, 0, 1)
to_return = [
blobs.Conv1D(
name=("preact_" if len(converted_activation) > 0 else "") + name,
W=np.squeeze(W, 3),
b=layer.get_weights()[1],
stride=layer.get_config()[KerasKeys.subsample_length],
border_mode=layer.get_config()[KerasKeys.border_mode],
#for conv1d implementations, channels always seem to come last
channels_come_last=True)
]
to_return.extend(converted_activation)
return to_return
def test_relu_after_conv1d_batchnorm(self):
input_layer = blobs.Input(num_dims=None, shape=(None, 2, 2))
conv_layer = blobs.Conv1D(W=np.random.random((2, 2, 2)),
b=np.random.random((2, )),
conv_mxts_mode=ConvMxtsMode.Linear,
stride=1,
border_mode=PaddingMode.valid,
channels_come_last=True)
conv_layer.set_inputs(input_layer)
batch_norm = blobs.BatchNormalization(gamma=np.array([1.0, 1.0]),
beta=np.array([-0.5, 0.5]),
axis=-1,
mean=np.array([-0.5, 0.5]),
std=np.array([1.0, 1.0]),
epsilon=0.001)
batch_norm.set_inputs(conv_layer)
relu_after_bn = blobs.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)