def process_layer7(self, curr_inputs):
curr_layer = 'layer7'
pro_inp = np.concatenate((curr_inputs, curr_inputs), axis=1)
pro_inp = model_utils.reshape_4d(pro_inp, shape=(64, 8, 8))
assert np.shape(pro_inp)[1:4] == self.model_structure()[curr_layer]
return pro_inp
def process_layer4(self, curr_inputs):
curr_layer = 'layer4'
model_reduction = self.model_reduction_plan()
num_split, _, _, opt = model_reduction[curr_layer][0]
weights = self.reduction_weights[curr_layer][0]
pro_inp = base_model.reduce_inp(weights,
curr_inputs,
num_split=num_split,
pool_method=self.params.pooling,
opt=opt)
pro_inp = model_utils.reshape_4d(pro_inp, shape=(64, 28, 28))
assert np.shape(pro_inp)[1:4] == self.model_structure()[curr_layer]
return pro_inp
def process_layer6(self, curr_inputs):
curr_layer = 'layer6'
pro_inp = model_utils.reshape_4d(curr_inputs, shape=(64, 8, 8))
assert np.shape(pro_inp)[1:4] == self.model_structure()[curr_layer]
return pro_inp