## Keras How to use max_value in Relu activation function

Relu function as defined in keras/activation.py is:

def relu(x, alpha=0., max_value=None): return K.relu(x, alpha=alpha, max_value=max_value)

It has a max_value which can be used to clip the value. Now how can this be used/called in the code? I have tried the following: (a)

model.add(Dense(512,input_dim=1)) model.add(Activation('relu',max_value=250)) assert kwarg in allowed_kwargs, 'Keyword argument not understood: ' + kwarg AssertionError: Keyword argument not understood: max_value

(b)

Rel = Activation('relu',max_value=250)

same error

(c)

from keras.layers import activations uu = activations.relu(??,max_value=250)

The problem with this is that it expects the input to be present in the first value. The error is 'relu() takes at least 1 argument (1 given)'

So how do I make this a layer?

model.add(activations.relu(max_value=250))

has the same issue 'relu() takes at least 1 argument (1 given)'

If this file cannot be used as layer, then there seems to be no way of specifying a clip value to Relu. This implies that the comment here https://github.com/fchollet/keras/issues/2119 closing a proposed change is wrong... Any thoughts? Thanks!

You can use the ReLU function of the Keras backend. Therefore, first import the backend:

from keras import backend as K

Then, you can pass your own function as activation using backend functionality. This would look like

def relu_advanced(x): return K.relu(x, max_value=250)

Then you can use it like

model.add(Dense(512, input_dim=1, activation=relu_advanced))

or

model.add(Activation(relu_advanced))

Unfortunately, you must hard code additional arguments. Therefore, it is better to use a function, that returns your function and passes your custom values:

def create_relu_advanced(max_value=1.): def relu_advanced(x): return K.relu(x, max_value=K.cast_to_floatx(max_value)) return relu_advanced

Then you can pass your arguments by either

model.add(Dense(512, input_dim=1, activation=create_relu_advanced(max_value=250)))

or

model.add(Activation(create_relu_advanced(max_value=250)))

**Layer activation functions,** ReLU(max_value=None, negative_slope=0, threshold=0, **kwargs). Rectified Linear Unit activation function. With default values, it returns element-wise max(x, ReLU stands for rectified linear unit, and is a type of activation function. Mathematically, it is defined as y = max(0, x). Visually, it looks like the following: ReLU is the most commonly used…

This is what I did using `Lambda`

layer to implement clip relu:
Step 1: define a function to do reluclip:

def reluclip(x, max_value = 20): return K.relu(x, max_value = max_value)

Step 2: add `Lambda`

layer into model:
`y = Lambda(function = reluclip)(y)`

**ReLU layer,** tf.keras.activations.relu Applies the rectified linear unit activation function. allows you to use non-zero thresholds, change the max value of the activation, You can use the ReLU function of the Keras backend. Therefore, first import the backend: from keras import backend as K Then, you can pass your own function as activation using backend functionality. This would look like. def relu_advanced(x): return K.relu(x, max_value=250) Then you can use it like

That is as easy as one lambda :

from keras.activations import relu clipped_relu = lambda x: relu(x, max_value=3.14)

Then use it like this:

model.add(Conv2D(64, (3, 3))) model.add(Activation(clipped_relu))

When reading a model saved in `hdf5`

use `custom_objects`

dictionary:

model = load_model(model_file, custom_objects={'<lambda>': clipped_relu})

**tf.keras.activations.relu,** Rectified Linear Unit activation function. tf.keras.layers.ReLU( max_value=None, negative_slope=0, threshold=0, **kwargs ) f(x) = max_value if x >= max_value Usage: layer = tf.keras.layers.ReLU() output = layer([-3.0, -1.0, 0.0, 2.0]) @brainnoise Thanks for your help. The following code works for my case, although I do expect a more user-friendly solution. import tensorflow as tf from tensorflow.python.keras import backend as K from tensorflow.python.keras.utils import CustomObjectScope def relu6(x): return K.relu(x, max_value=6) with CustomObjectScope({'relu6': relu6}): keras_mobilenet= tf.keras.applications.mobilenet

Tested below, it'd work:

import keras def clip_relu (x): return keras.activations.relu(x, max_value=1.) predictions=Dense(num_classes,activation=clip_relu,name='output')

**tf.keras.layers.ReLU,** Relu function as defined in keras/activation.py is: def relu(x, alpha=0., max_value=None): return K.relu(x, alpha=alpha, max_value=max_value). Activation functions. What is Activation function: It is a transfer function that is used to map the output of one layer to another. In daily life when we think every detailed decision is based on the results of small things. let’s assume the game of chess, every movement is based on 0 or 1. So in every move, we use the activation function.

**Keras How to use max_value in Relu activation function,** This page provides Python code examples for keras.backend.relu. The following are code examples for showing how to use keras.backend.relu(). They are from def relu6(x): return K.relu(x, max_value=6.0) # Custom activation function. Keras documentation Activation layers About Keras Getting started Developer guides Keras API reference Models API Layers API Callbacks API Data preprocessing Optimizers Metrics Losses Built-in small datasets Keras Applications Utilities Code examples Why choose Keras?

**keras.backend.relu Python Example,** This activation function is introduced in Hannun, Awni, et al. "Deep speech: One can just use max_value to do the clipping. Closing this and keras.activations.relu(x, alpha=0.0, max_value=None) While selecting and switching activation functions in deep learning frameworks is easy, you will find that managing multiple experiments and trying different activation functions on large test data sets can be challenging. It can be difficult to:

**Implement the clipped ReLU activation function · Issue #2119 · keras ,** LeakyReLU(X, alpha=0.0, max_value=None). We can directly use the activation function by setting the value of alpha with a small constant. There are many Combined L1 and L2 and/or L1, L2 regularization may also bias the activation function towards Leaky ReLU (Uthmān, 2017). With the constraint, or alpha_constraint, you can set fixed limits to the network parameters during training. You can use any of the Keras constraints for this purpose. He et al. (2015) do not use constraints to allow the