GitHub - digantamisra98/Mish: Mish: A Self Regularized Non-Monotonic Neural Activation Function
Mish/mish.py at master · digantamisra98/Mish · GitHub
python::1
from __future__ import print_function
from tensorflow.python import keras
from tensorflow.python.keras.datasets import mnist
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Dropout, Flatten
from tensorflow.python.keras.layers import Conv2D, MaxPooling2D
from tensorflow.python.keras import backend as K
"""Tensorflow-Keras Implementation of Mish"""
## Import Necessary Modules
import tensorflow as tf
from tensorflow.keras.layers import Activation
from tensorflow.keras.utils import get_custom_objects
class Mish(Activation):
'''
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 = Activation('Mish', name="conv1_act")(X_input)
'''
def __init__(self, activation, **kwargs):
super(Mish, self).__init__(activation, **kwargs)
self.__name__ = 'Mish'
def mish(inputs):
return inputs * tf.math.tanh(tf.math.softplus(inputs))
get_custom_objects().update({'Mish': Mish(mish)})
batch_size = 128
num_classes = 10
epochs = 12
# input image dimensions
img_rows, img_cols = 28, 28
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='Mish',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='Mish'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='Mish'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
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