def create_small_net_with_conv_layer(self, conv_layer,
outputs_per_channel):
self.conv_layer = conv_layer
self.conv_layer.set_inputs(self.input_layer)
self.flatten_layer = layers.Flatten()
self.flatten_layer.set_inputs(self.conv_layer)
self.dense_layer = layers.Dense(
kernel=(np.array([
list(itertools.chain(*[[1.0,-1.0]
for i in range(outputs_per_channel)]))
]).T)
.astype("float32"),
bias=np.array([1]).astype("float32"),
dense_mxts_mode=DenseMxtsMode.Linear)
print(outputs_per_channel)
print(self.dense_layer.kernel)
self.dense_layer.set_inputs(self.flatten_layer)
self.dense_layer.build_fwd_pass_vars()
self.input_layer.reset_mxts_updated()
self.dense_layer.set_scoring_mode(layers.ScoringMode.OneAndZeros)
self.dense_layer.set_active()
self.input_layer.update_mxts()
self.inp = ((np.arange(16).reshape((2,2,4))
.astype("float32"))-8.0).transpose((0,2,1))
def setUp(self):
self.input_layer = layers.Input(batch_shape=(None, 4))
self.w1 = [1.0, 2.0, 3.0, 4.0]
self.w2 = [-1.0, -2.0, -3.0, -4.0]
W = np.array([self.w1, self.w2]).T
b = np.array([-1.0, 1.0])
self.dense_layer = layers.Dense(kernel=W,
bias=b,
dense_mxts_mode=DenseMxtsMode.Linear)
self.dense_layer.set_inputs(self.input_layer)
self.inp = [[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 2.0]]
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 setUp(self):
self.input_layer = layers.Input(batch_shape=(None,4))
self.w1 = [1.0, 2.0, 3.0, 4.0]
self.w2 = [-1.0, -2.0, -3.0, -4.0]
W = np.array([self.w1, self.w2]).T
b = np.array([-1.0, 1.0])
self.dense_layer = layers.Dense(kernel=W, bias=b,
dense_mxts_mode=DenseMxtsMode.Linear)
self.dense_layer.set_inputs(self.input_layer)
self.dense_layer.build_fwd_pass_vars()
self.dense_layer.set_scoring_mode(layers.ScoringMode.OneAndZeros)
self.dense_layer.set_active()
self.input_layer.update_mxts()
self.inp = [[1.0, 1.0, 1.0, 1.0],
[2.0, 2.0, 2.0, 2.0]]
def test_running_of_different_dense_modes(self):
for mode in DenseMxtsMode.vals:
input_layer = layers.Input(batch_shape=(None,4))
W = np.array([self.w1, self.w2]).T
b = np.array([-1.0, 1.0])
dense_layer = layers.Dense(kernel=W, bias=b,
dense_mxts_mode=mode)
dense_layer.set_inputs(input_layer)
dense_layer.build_fwd_pass_vars()
dense_layer.set_scoring_mode(layers.ScoringMode.OneAndZeros)
dense_layer.set_active()
input_layer.update_mxts()
func = compile_func([input_layer.get_activation_vars(),
input_layer.get_reference_vars()],
input_layer.get_mxts())
dense_layer.update_task_index(task_index=0)
func([self.inp, np.zeros_like(self.inp)])
def create_small_net_with_pool_layer(self, pool_layer,
outputs_per_channel):
self.pool_layer = pool_layer
self.pool_layer.set_inputs(self.input_layer)
self.flatten_layer = layers.Flatten()
self.flatten_layer.set_inputs(self.pool_layer)
self.dense_layer = layers.Dense(kernel=(np.array([
list(itertools.chain(*[[2, 3]
for i in range(outputs_per_channel)]))
])).astype("float32").T,
bias=np.array([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.input_layer.update_mxts()
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 setUp(self):
self.input_layer1 = layers.Input(batch_shape=(None, 1, 1, 1))
self.input_layer2 = layers.Input(batch_shape=(None, 1, 1, 1))
self.concat_layer = layers.Concat(axis=1)
self.concat_layer.set_inputs([self.input_layer1, self.input_layer2])
self.flatten_layer = layers.Flatten()
self.flatten_layer.set_inputs(self.concat_layer)
self.dense_layer = layers.Dense(kernel=np.array([([1, 2])]).T,
bias=[1],
dense_mxts_mode=DenseMxtsMode.Linear)
self.dense_layer.set_inputs(self.flatten_layer)
self.dense_layer.build_fwd_pass_vars()
self.input_layer1.reset_mxts_updated()
self.input_layer2.reset_mxts_updated()
self.dense_layer.set_scoring_mode(layers.ScoringMode.OneAndZeros)
self.dense_layer.set_active()
self.input_layer1.update_mxts()
self.input_layer2.update_mxts()
self.inp1 = np.arange(2).reshape((2, 1, 1, 1)) + 1
self.inp2 = np.arange(2).reshape((2, 1, 1, 1)) + 1
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)