Nevertheless, it does not mean that it cannot be improved. By consequence, ReLU is the de facto standard activation function in the deep learning community today. Computing this function - often by simply maximizing between (0, x) - takes substantially fewer resources than computing e.g. What's more, it makes your model sparser, since all gradients which turn to 0 effectively mean that a particular neuron is zeroed out.įinally, it is computationally faster. ReLU does not have this problem - its derivative is 0 when x < 0 and is 1 otherwise. Consequently, when using Tanh and Sigmoid, you risk having a suboptimal model that might possibly not converge due to vanishing gradients. When you chain values that are smaller than one, such as 0.2 * 0.15 * 0.3, you get really small numbers (in this case 0.009). This problem primarily occurs with the Sigmoid and Tanh activation functions, whose derivatives produce outputs of 0 < x' < 1, except for Tanh which produces x' = 1 at x = 0. In the first, the backpropagation algorithm, which chains the gradients together when computing the error backwards, will find really small gradients towards the left side of the network (i.e., farthest from where error computation started). Now, the deep learning community often deals with two types of problems during training - the vanishing gradients problem and the exploding gradients problem. We can explain the benefits and disbenefits by visualizing the derivatives of those three activation functions below. The derivative of any function at x is simply another function whose input is mapped to another numeric value. That is, the gradient is computed with respect to the neural weights, after which the weights are altered based on this gradient and the learning rate. This primarily has to do with how neural networks are optimized - i.e., through gradient descent. Those activation functions all have their own benefits and disbenefits. The shape of this function is really similar, but one noticeable difference is that the (-∞, ∞) domain is mapped to the (0, 1) range instead of the (-1, 1) range.įinally, the most prominently activation function used today is called Rectified Linear Unit or ReLU. Second, there is the sigmoid or softstep activation function. Clearly, one can see that the entire domain (-∞, ∞) is mapped to a range of (-1, 1). In the machine learning community, three major activation functions are used today.įirst, there is the tanh activation function. Update February 2020 - Added links to other MachineCurve blogs and processed small spelling improvements. Does it perform better and if so, why is that the case? In this blog, we'll study today's commonly used activation functions and inspect a relatively new player. In many applications, the results have been impressive. Since activation functions can be non-linear, neural networks have acquired the capability of handling non-linear data. To extend neural network behavior to non-linear data, smart minds have invented the activation function - a function which takes the scalar as its input and maps it to another numerical value. In fact, it would only be able to produce linear decision boundaries between the classes you're training the model for. If we would pass the scalar value, the model would behave as if it is a linear one. Mathematically, a neuron is nothing but the dot product between the weights vector w and the input vector x, yielding a scalar value that is passed on to the next layer. Neural networks are composed of various layers of neurons.
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