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from keras.layers import Activation, Convolution2D, Dropout, Conv2D
from keras.layers import AveragePooling2D, BatchNormalization
from keras.layers import GlobalAveragePooling2D
from keras.models import Sequential
from keras.layers import Flatten
from keras.models import Model
from keras.layers import Input
from keras.layers import MaxPooling2D
from keras.layers import SeparableConv2D
from keras import layers
from keras.regularizers import l2
def simple_CNN(input_shape, num_classes):
model = Sequential()
model.add(Convolution2D(filters=16, kernel_size=(7, 7), padding='same',
name='image_array', input_shape=input_shape))
model.add(BatchNormalization())
model.add(Convolution2D(filters=16, kernel_size=(7, 7), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(AveragePooling2D(pool_size=(2, 2), padding='same'))
model.add(Dropout(.5))
model.add(Convolution2D(filters=32, kernel_size=(5, 5), padding='same'))
model.add(BatchNormalization())
model.add(Convolution2D(filters=32, kernel_size=(5, 5), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(AveragePooling2D(pool_size=(2, 2), padding='same'))
model.add(Dropout(.5))
model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same'))
model.add(BatchNormalization())
model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(AveragePooling2D(pool_size=(2, 2), padding='same'))
model.add(Dropout(.5))
model.add(Convolution2D(filters=128, kernel_size=(3, 3), padding='same'))
model.add(BatchNormalization())
model.add(Convolution2D(filters=128, kernel_size=(3, 3), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(AveragePooling2D(pool_size=(2, 2), padding='same'))
model.add(Dropout(.5))
model.add(Convolution2D(filters=256, kernel_size=(3, 3), padding='same'))
model.add(BatchNormalization())
model.add(Convolution2D(filters=num_classes, kernel_size=(3, 3), padding='same'))
model.add(GlobalAveragePooling2D())
model.add(Activation('softmax',name='predictions'))
return model
def simpler_CNN(input_shape, num_classes):
model = Sequential()
model.add(Convolution2D(filters=16, kernel_size=(5, 5), padding='same',
name='image_array', input_shape=input_shape))
model.add(BatchNormalization())
model.add(Convolution2D(filters=16, kernel_size=(5, 5),
strides=(2, 2), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(.25))
model.add(Convolution2D(filters=32, kernel_size=(5, 5), padding='same'))
model.add(BatchNormalization())
model.add(Convolution2D(filters=32, kernel_size=(5, 5),
strides=(2, 2), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(.25))
model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same'))
model.add(BatchNormalization())
model.add(Convolution2D(filters=64, kernel_size=(3, 3),
strides=(2, 2), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(.25))
model.add(Convolution2D(filters=64, kernel_size=(1, 1), padding='same'))
model.add(BatchNormalization())
model.add(Convolution2D(filters=128, kernel_size=(3, 3),
strides=(2, 2), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(.25))
model.add(Convolution2D(filters=256, kernel_size=(1, 1), padding='same'))
model.add(BatchNormalization())
model.add(Convolution2D(filters=128, kernel_size=(3, 3),
strides=(2, 2), padding='same'))
model.add(Convolution2D(filters=256, kernel_size=(1, 1), padding='same'))
model.add(BatchNormalization())
model.add(Convolution2D(filters=num_classes, kernel_size=(3, 3),
strides=(2, 2), padding='same'))
model.add(Flatten())
#model.add(GlobalAveragePooling2D())
model.add(Activation('softmax',name='predictions'))
return model
def tiny_XCEPTION(input_shape, num_classes, l2_regularization=0.01):
regularization = l2(l2_regularization)
# base
img_input = Input(input_shape)
x = Conv2D(5, (3, 3), strides=(1, 1), kernel_regularizer=regularization,
use_bias=False)(img_input)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(5, (3, 3), strides=(1, 1), kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
# module 1
residual = Conv2D(8, (1, 1), strides=(2, 2),
padding='same', use_bias=False)(x)
residual = BatchNormalization()(residual)
x = SeparableConv2D(8, (3, 3), padding='same',
kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(8, (3, 3), padding='same',
kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = layers.add([x, residual])
# module 2
residual = Conv2D(16, (1, 1), strides=(2, 2),
padding='same', use_bias=False)(x)
residual = BatchNormalization()(residual)
x = SeparableConv2D(16, (3, 3), padding='same',
kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(16, (3, 3), padding='same',
kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = layers.add([x, residual])
# module 3
residual = Conv2D(32, (1, 1), strides=(2, 2),
padding='same', use_bias=False)(x)
residual = BatchNormalization()(residual)
x = SeparableConv2D(32, (3, 3), padding='same',
kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(32, (3, 3), padding='same',
kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = layers.add([x, residual])
# module 4
residual = Conv2D(64, (1, 1), strides=(2, 2),
padding='same', use_bias=False)(x)
residual = BatchNormalization()(residual)
x = SeparableConv2D(64, (3, 3), padding='same',
kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(64, (3, 3), padding='same',
kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = layers.add([x, residual])
x = Conv2D(num_classes, (3, 3),
#kernel_regularizer=regularization,
padding='same')(x)
x = GlobalAveragePooling2D()(x)
output = Activation('softmax',name='predictions')(x)
model = Model(img_input, output)
return model
def mini_XCEPTION(input_shape, num_classes, l2_regularization=0.01):
regularization = l2(l2_regularization)
# base
img_input = Input(input_shape)
x = Conv2D(8, (3, 3), strides=(1, 1), kernel_regularizer=regularization,
use_bias=False)(img_input)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(8, (3, 3), strides=(1, 1), kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
# module 1
residual = Conv2D(16, (1, 1), strides=(2, 2),
padding='same', use_bias=False)(x)
residual = BatchNormalization()(residual)
x = SeparableConv2D(16, (3, 3), padding='same',
kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(16, (3, 3), padding='same',
kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = layers.add([x, residual])
# module 2
residual = Conv2D(32, (1, 1), strides=(2, 2),
padding='same', use_bias=False)(x)
residual = BatchNormalization()(residual)
x = SeparableConv2D(32, (3, 3), padding='same',
kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(32, (3, 3), padding='same',
kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = layers.add([x, residual])
# module 3
residual = Conv2D(64, (1, 1), strides=(2, 2),
padding='same', use_bias=False)(x)
residual = BatchNormalization()(residual)
x = SeparableConv2D(64, (3, 3), padding='same',
kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(64, (3, 3), padding='same',
kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = layers.add([x, residual])
# module 4
residual = Conv2D(128, (1, 1), strides=(2, 2),
padding='same', use_bias=False)(x)
residual = BatchNormalization()(residual)
x = SeparableConv2D(128, (3, 3), padding='same',
kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(128, (3, 3), padding='same',
kernel_regularizer=regularization,
use_bias=False)(x)
x = BatchNormalization()(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = layers.add([x, residual])
x = Conv2D(num_classes, (3, 3),
#kernel_regularizer=regularization,
padding='same')(x)
x = GlobalAveragePooling2D()(x)
output = Activation('softmax',name='predictions')(x)
model = Model(img_input, output)
return model
def big_XCEPTION(input_shape, num_classes):
img_input = Input(input_shape)
x = Conv2D(32, (3, 3), strides=(2, 2), use_bias=False)(img_input)
x = BatchNormalization(name='block1_conv1_bn')(x)
x = Activation('relu', name='block1_conv1_act')(x)
x = Conv2D(64, (3, 3), use_bias=False)(x)
x = BatchNormalization(name='block1_conv2_bn')(x)
x = Activation('relu', name='block1_conv2_act')(x)
residual = Conv2D(128, (1, 1), strides=(2, 2),
padding='same', use_bias=False)(x)
residual = BatchNormalization()(residual)
x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False)(x)
x = BatchNormalization(name='block2_sepconv1_bn')(x)
x = Activation('relu', name='block2_sepconv2_act')(x)
x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False)(x)
x = BatchNormalization(name='block2_sepconv2_bn')(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = layers.add([x, residual])
residual = Conv2D(256, (1, 1), strides=(2, 2),
padding='same', use_bias=False)(x)
residual = BatchNormalization()(residual)
x = Activation('relu', name='block3_sepconv1_act')(x)
x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False)(x)
x = BatchNormalization(name='block3_sepconv1_bn')(x)
x = Activation('relu', name='block3_sepconv2_act')(x)
x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False)(x)
x = BatchNormalization(name='block3_sepconv2_bn')(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = layers.add([x, residual])
x = Conv2D(num_classes, (3, 3),
#kernel_regularizer=regularization,
padding='same')(x)
x = GlobalAveragePooling2D()(x)
output = Activation('softmax',name='predictions')(x)
model = Model(img_input, output)
return model
if __name__ == "__main__":
input_shape = (64, 64, 1)
num_classes = 7
#model = tiny_XCEPTION(input_shape, num_classes)
#model.summary()
#model = mini_XCEPTION(input_shape, num_classes)
#model.summary()
#model = big_XCEPTION(input_shape, num_classes)
#model.summary()
model = simple_CNN((48, 48, 1), num_classes)
model.summary()
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