Mish Class Definition in Keras

mish_keras.py

# Keras Implementation of Mish Activation Function.


# Import Necessary Modules.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from keras.engine.base_layer import Layer
from keras import backend as K

class Mish(Layer):
    '''
    Mish Activation Function.
    .. math::
        mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + e^{x}))
    Shape:
        - Input: Arbitrary. Use the keyword argument `input_shape`
        (tuple of integers, does not include the samples axis)
        when using this layer as the first layer in a model.
        - Output: Same shape as the input.
    Examples:
        >>> X_input = Input(input_shape)
        >>> X = Mish()(X_input)
    '''

    def __init__(self, **kwargs):
        super(Mish, self).__init__(**kwargs)
        self.supports_masking = True

    def call(self, inputs):
        return inputs * K.tanh(K.softplus(inputs))

    def get_config(self):
        base_config = super(Mish, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))

    def compute_output_shape(self, input_shape):
        return input_shape

@rupshali You can define mish as a function instead rather than as a Layer and use it within any keras layers supporting activations with the function name.
For example:
Defining Mish as a function -

## Mish Activation Function
def mish(x):
	return tf.keras.layers.Lambda(lambda x: x*tf.tanh(tf.log(1+tf.exp(x))))(x)

Defining a network with Mish activations:

##LeNet Architecture
model = Sequential()
model.add(Conv2D(20, 5, padding="same",input_shape=inputShape, activation = mish))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))

model.add(Conv2D(50, 5, padding="same",activation = mish ))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))

model.add(Flatten())
model.add(Dense(500, activation = mish))


model.add(Dense(numClasses))

model.add(Activation("softmax"))
model.summary()

Hope this helps.

Thanks a lot @digantamisra98