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Why the GRU unit resolves the problem of the vanishing gradient?
As mentioned above, GRUs are improved version of standard recurrent neural network. To solve the vanishing gradient problem of a standard RNN, GRU uses, so-called, update gate and reset gate. Basically, these are two vectors which decide what information should be passed to the output.
What solves the vanishing gradient problem?
Solutions: The simplest solution is to use other activation functions, such as ReLU, which doesn’t cause a small derivative. Residual networks are another solution, as they provide residual connections straight to earlier layers.
How does ReLU solve vanishing gradient problem?
This involves first calculating the prediction error made by the model and using the error to estimate a gradient used to update each weight in the network so that less error is made next time. This error gradient is propagated backward through the network from the output layer to the input layer.
How does a GRU solve the vanishing gradient problem?
To solve the vanishing gradient problem of a standard RNN, GRU uses, so called, update gate and reset gate. Basically, these are two vectors which decide what information should be passed to the output.
Which is the best rnn for vanishing gradients?
The problem of Vanishing Gradients and Exploding Gradients are common with basic RNNs. Gated Recurrent Units (GRU) are simple, fast and solve vanishing gradient problem easily. Long Short-Term Memory (LSTM) units are slightly more complex, more powerful, more effective in solving the vanishing gradient problem.
What is the problem of vanishing gradients in neural networks?
This problem makes it hard to learn and tune the parameters of the earlier layers in the network. The vanishing gradients problem is one example of unstable behaviour that you may encounter when training a deep neural network.
What makes GRU networks so special and effective?
As mentioned above, GRUs are improved version of standard recurrent neural network. But what makes them so special and effective? To solve the vanishing gradient problem of a standard RNN, GRU uses, so-called, update gate and reset gate. Basically, these are two vectors which decide what information should be passed to the output.