keras-layer

Sometimes the default standard activations like ReLU, tanh, softmax, ... and the advanced activations like LeakyReLU aren't enough. And it might also not be in keras-contrib . How do you create your own activation function? Credits to this Github issue comment by Ritchie Ng . # Creating a model from keras.models import Sequential from keras.layers import Dense # Custom activation function from keras.layers import Activation from keras import backend as K from keras.utils.generic_utils import get_custom_objects def custom_activation(x): return (K.sigmoid(x) * 5) - 1 get_custom_objects().update(

For any Keras layer ( Layer class), can someone explain how to understand the difference between input_shape , units , dim , etc.? For example the doc says units specify the output shape of a layer. In the image of the neural net below hidden layer1 has 4 units. Does this directly translate to the units attribute of the Layer object? Or does units in Keras equal the shape of every weight in the hidden layer times the number of units? In short how does one understand/visualize the attributes of the model - in particular the layers - with the image below? Units: The amount of "neurons", or

This is my test code: from keras import layers input1 = layers.Input((2,3)) output = layers.Dense(4)(input1) print(output) The output is: <tf.Tensor 'dense_2/add:0' shape=(?, 2, 4) dtype=float32> But What Happend? The documentation says: Note: if the input to the layer has a rank greater than 2, then it is flattened prior to the initial dot product with kernel. While the output is reshaped? Currently, contrary to what has been stated in documentation, the Dense layer is applied on the last axis of input tensor : Contrary to the documentation, we don't actually flatten it. It's applied on the