“Función de activación de mish TensorFlow” Código de respuesta

Función de activación de mish 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))