Sage-Tips

Why is ReLU not used in Lstm?

Traditionally, LSTMs use the tanh activation function for the activation of the cell state and the sigmoid activation function for the node output. Given their careful design, ReLU were thought to not be appropriate for Recurrent Neural Networks (RNNs) such as the Long Short-Term Memory Network (LSTM) by default.

Which issue is faced while working with ReLU activation function?

The drawback with ReLU function is their fragility, that is, when a large gradient is made to flow through ReLU neuron, it can render the neuron useless and make it unable to fire on any other datapoint again for the rest of the process. In order to address this problem, leaky ReLU was introduced.

Why dont we use ReLU in RNN?

RELU can only solve part of the gradient vanishing problem of RNN because the gradient vanishing problem is not only caused by activation function. see above function, the hidden state derivative will depend on both activation and Ws, if Ws’s max eigen value < 1, the long term dependency’s gradient will be vanished.

Can ReLU differentiate?

since ReLU doesn’t have a derivative. No, ReLU has derivative. I assumed you are using ReLU function f(x)=max(0,x) . It means if x<=0 then f(x)=0 , else f(x)=x .

Why is ReLU differentiable?

A function is differentiable at a particular point if there exist left derivatives and right derivatives and both the derivatives are equal at that point. ReLU is differentiable at all the point except 0. Hence it is acceptable for the minima of the cost function to correspond to points with undefined gradient.

Is ReLU continuous function?

By contrast RELU is continuous and only its first derivative is a discontinuous step function. Since the RELU function is continuous and well defined, gradient descent is well behaved and leads to a well behaved minimization. Further, RELU does not saturate for large values greater than zero.

Why ReLU performs better than sigmoid?

Efficiency: ReLu is faster to compute than the sigmoid function, and its derivative is faster to compute. This makes a significant difference to training and inference time for neural networks: only a constant factor, but constants can matter.

Do I need an activation function in my LSTM?

As for whether having an activation function would make much difference to the analysis, much of this depends on the data. Given that ReLUs can have quite large outputs, they have traditionally been regarded as inappropriate for use with LSTMs. Let’s consider the following example.

What is the rectified linear activation function (Relu)?

The rectified linear activation function or ReLU for short is a piecewise linear function that will output the input directly if it is positive, otherwise, it will output zero. It has become the default activation function for many types of neural networks because a model that uses it is easier to train and often achieves better performance.

Do stacked LSTMs need activation layers?

The first usage of stacked LSTMs (that I know of) was applied to speech recognition (Graves et. al), and the authors also do not mention the need for activation layers between the LSTM cells; only at the final output in conjunction with a fully-connected layer.

How does the LSTM network work?

The LSTM network takes a 2D array as input. One layer of LSTM has as many cells as the timesteps. Setting the return_sequences=True makes each cell per timestep emit a signal. This becomes clearer in Figure 2.4 which shows the difference between return_sequences as True (Fig. 2.4a) vs False (Fig. 2.4b).