diff options
Diffstat (limited to 'examples/neon_cnn.cpp')
| -rw-r--r-- | examples/neon_cnn.cpp | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/examples/neon_cnn.cpp b/examples/neon_cnn.cpp index 85f8792b9c..339c8c1a81 100644 --- a/examples/neon_cnn.cpp +++ b/examples/neon_cnn.cpp @@ -53,10 +53,10 @@ public: // The weights and biases tensors should be initialized with the values inferred with the training // Set memory manager where allowed to manage internal memory requirements - conv0 = arm_compute::support::cpp14::make_unique<NEConvolutionLayer>(mm_layers); - conv1 = arm_compute::support::cpp14::make_unique<NEConvolutionLayer>(mm_layers); - fc0 = arm_compute::support::cpp14::make_unique<NEFullyConnectedLayer>(mm_layers); - softmax = arm_compute::support::cpp14::make_unique<NESoftmaxLayer>(mm_layers); + conv0 = std::make_unique<NEConvolutionLayer>(mm_layers); + conv1 = std::make_unique<NEConvolutionLayer>(mm_layers); + fc0 = std::make_unique<NEFullyConnectedLayer>(mm_layers); + softmax = std::make_unique<NESoftmaxLayer>(mm_layers); /* [Initialize tensors] */ @@ -170,8 +170,8 @@ public: // We need 2 memory groups for handling the input and output // We call explicitly allocate after manage() in order to avoid overlapping lifetimes - memory_group0 = arm_compute::support::cpp14::make_unique<MemoryGroup>(mm_transitions); - memory_group1 = arm_compute::support::cpp14::make_unique<MemoryGroup>(mm_transitions); + memory_group0 = std::make_unique<MemoryGroup>(mm_transitions); + memory_group1 = std::make_unique<MemoryGroup>(mm_transitions); memory_group0->manage(&out_conv0); out_conv0.allocator()->allocate(); |