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)]