mish activation function tensorflow
import matplotlib.pyplot as plt
%matplotlib inline
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from keras.engine.base_layer import Layer
from keras.layers import Activation, Dense
from keras import backend as K
from sklearn.model_selection import train_test_split
from keras.datasets import mnist
from keras.optimizers import SGD
from keras.utils import np_utils
from __future__ import print_function
import keras
from keras.models import Sequential
from keras.layers.core import Flatten
from keras.layers import Dropout
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
import numpy as np
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
def mish(x):
return keras.layers.Lambda(lambda x: x*K.tanh(K.softplus(x)))(x)
###### Use in your model ##########
model.add(Dense(128,activation= mish))