ELU as a Neural Networks Activation Function Sefik Ilkin Serengil
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The 2080 features 2944 CUDA cores, a base/boost speed of 1515/1710 MHz, 8 GB of GDDR6 memory and a memory bandwidth of 448 GB/s. NVIDIA have also released the 2080 Ti which has marginally higher specs together with a jaw dropping price tag of $1200 for the Founders Edition. The RTX 2080 features Turing NVENC which is far more efficient than CPU.
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Scaled Exponential Linear Unit (SELU) [20] is a recent modification of ELU in which the function output is normalized without external normalization. In Table 2, λ and α are the fixed parameters ( λ ≈ 1.0507, α ≈ 1.6732) of SELU for scaling the output and shaping the function in the negative input region.
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ELU¶ class torch.nn. ELU (alpha = 1.0, inplace = False) [source] ¶ Applies the Exponential Linear Unit (ELU) function, element-wise, as described in the paper: Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). ELU is defined as:
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The Exponential Linear Unit (ELU) is an activation function for neural networks. In contrast to ReLUs, ELUs have negative values which allows them to push mean unit activations closer to zero like batch normalization but with lower computational complexity. Mean shifts toward zero speed up learning by bringing the normal gradient closer to the unit natural gradient because of a reduced bias.
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Exponential Linear Unit (ELU) is a popular activation function that speeds up learning and produces more accurate results. This article is an introduction to ELU and its position when compared to other popular activation functions. It also includes an interactive example and usage with PyTorch and Tensorflow.
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