This is why the Sigmoid activation function is not used in hidden layers. If the input values are too small or too high, then the neural network may stop learning, this problem is popularly known as the vanishing gradient problem.Sigmoid activation is computationally heavy to use and the neural network may not converge fast during training.Disadvantages of Sigmoid Activation Function Since its output ranges from 0 to 1, it is a good choice for the output layer to produce the result in probability for binary classification.The sigmoid activation function is both non-linear and differentiable which are good characteristics for activation function.Advantages of Sigmoid Activation Function The sigmoid activation function produces results in the range of 0 to 1 which is interpreted as the probability. The formula of Sigmoid function is as below – sigmoid(x) = 1/ (1 + exp(-x)) In the Sigmoid Activation layer of Keras, we apply the sigmoid function. Sigmoid Activation Layer Sigmoid Activation Layer in Keras Syntax of ReLu Activation Layer in Keras – tf.(x, alpha=0.0, max_value=None, threshold=0)Ģ. Whenever the inputs are negative, its derivative becomes zero, therefore backpropagation cannot be performed and learning may not take place for that neuron and it dies out. The major disadvantage of the ReLU layer is that it suffers from the problem of Dying Neurons.Disadvantages of ReLU Activation Function ReLU does not suffer from the issue of Vanishing Gradient issue like other activation functions and hence it is very effective in hidden layers of large neural networks.It is both non-linear and differentiable which are good characteristics for activation function.ReLu activation function is computationally efficient hence it enables neural networks to converge faster during the training phase.ReLu Layer in Keras is used for applying the rectified linear unit activation function. Relu Activation Layer ReLU Activation Layer in Keras
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