Activation function is a non-linear transformation, usually applied in neural networks to the output of the linear or convolutional layer.
In LSTM Network (Understanding LSTMs), Why input gate and output gate use tanh? what is the intuition behind this? it …
machine-learning deep-learning lstm recurrent-neural-network activation-functionSuppose you need to make an activation function which is not possible using only pre-defined tensorflow building-blocks, what can you …
python tensorflow neural-network deep-learning activation-functionI have been experimenting with neural networks these days. I have come across a general question regarding the activation function …
neural-network regression activation-functionI am struggling to implement an activation function in TensorFlow in Python. The code is the following: def myfunc(x): …
tensorflow machine-learning deep-learning keras activation-functionMost examples of neural networks for classification tasks I've seen use the a softmax layer as output activation function. Normally, …
machine-learning neural-network classification softmax activation-functionHow would I implement the derivative of Leaky ReLU in Python without using Tensorflow? Is there a better way than …
python neural-network activation-functionRelu function as defined in keras/activation.py is: def relu(x, alpha=0., max_value=None): return K.relu(x, …
tensorflow keras activation-functionI am trying to implement leaky Relu, the problem is I have to do 4 for loops for a 4 dimensional array …
numpy activation-function relu