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 = blobs.Flatten()
self.flatten_layer.set_inputs(self.conv_layer)
self.dense_layer = blobs.Dense(
W=(np.array(
[([1.0]*outputs_per_channel)+
([-1.0]*outputs_per_channel)]).T
.astype("float32")),
b=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.input_layer.reset_mxts_updated()
self.dense_layer.set_scoring_mode(blobs.ScoringMode.OneAndZeros)
self.dense_layer.set_active()
self.input_layer.update_mxts()
self.inp = (np.arange(16).reshape((2,2,4))
.astype("float32"))
def prepare_batch_norm_deeplift_model(self, axis):
self.input_layer = blobs.Input(num_dims=None, shape=(None,2,2,2))
if (self.keras_version <= 0.3):
std = self.std
epsilon = self.epsilon
else:
std = np.sqrt(self.std+self.epsilon)
epsilon = 0
self.batch_norm_layer = blobs.BatchNormalization(
gamma=self.gamma,
beta=self.beta,
axis=axis,
mean=self.mean,
std=std,
epsilon=epsilon)
self.batch_norm_layer.set_inputs(self.input_layer)
self.flatten_layer = blobs.Flatten()
self.flatten_layer.set_inputs(self.batch_norm_layer)
self.dense_layer = blobs.Dense(
W=np.ones((1,8)).T,
b=np.zeros(1),
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(blobs.ScoringMode.OneAndZeros)
self.dense_layer.set_active()
self.dense_layer.update_task_index(0)
self.input_layer.update_mxts()
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 = blobs.Flatten()
self.flatten_layer.set_inputs(self.pool_layer)
self.dense_layer = blobs.Dense(W=np.array([
([2] * outputs_per_channel) + ([3] * outputs_per_channel)
]).astype("float32").T,
b=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(blobs.ScoringMode.OneAndZeros)
self.dense_layer.set_active()
self.input_layer.update_mxts()
def setUp(self):
self.input_layer1 = blobs.Input(num_dims=None, shape=(None,1,1,1))
self.input_layer2 = blobs.Input(num_dims=None, shape=(None,1,1,1))
self.concat_layer = blobs.Concat(axis=1)
self.concat_layer.set_inputs([self.input_layer1, self.input_layer2])
self.flatten_layer = blobs.Flatten()
self.flatten_layer.set_inputs(self.concat_layer)
self.dense_layer = blobs.Dense(
W=np.array([([1,2])]).T, b=[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(blobs.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 flatten_conversion(layer, name, verbose, **kwargs):
return [blobs.Flatten(name=name, verbose=verbose)]
def flatten_conversion(layer, name, mxts_mode):
#mxts_mode not used
return [blobs.Flatten(name=name)]
