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model.py
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model.py
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import h5py
from keras.models import Model
from keras.layers import Input, Activation, Concatenate
from keras.layers import Flatten, Dropout
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers import AveragePooling2D
def SqueezeNet(nb_classes, inputs=(3, 224, 224)):
""" Keras Implementation of SqueezeNet(arXiv 1602.07360)
@param nb_classes: total number of final categories
Arguments:
inputs -- shape of the input images (channel, cols, rows)
"""
input_img = Input(shape=inputs)
conv1 = Convolution2D(
96, (7, 7), activation='relu', kernel_initializer='glorot_uniform',
strides=(2, 2), padding='same', name='conv1',
data_format="channels_first")(input_img)
maxpool1 = MaxPooling2D(
pool_size=(3, 3), strides=(2, 2), name='maxpool1',
data_format="channels_first")(conv1)
fire2_squeeze = Convolution2D(
16, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire2_squeeze',
data_format="channels_first")(maxpool1)
fire2_expand1 = Convolution2D(
64, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire2_expand1',
data_format="channels_first")(fire2_squeeze)
fire2_expand2 = Convolution2D(
64, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire2_expand2',
data_format="channels_first")(fire2_squeeze)
merge2 = Concatenate(axis=1)([fire2_expand1, fire2_expand2])
fire3_squeeze = Convolution2D(
16, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire3_squeeze',
data_format="channels_first")(merge2)
fire3_expand1 = Convolution2D(
64, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire3_expand1',
data_format="channels_first")(fire3_squeeze)
fire3_expand2 = Convolution2D(
64, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire3_expand2',
data_format="channels_first")(fire3_squeeze)
merge3 = Concatenate(axis=1)([fire3_expand1, fire3_expand2])
fire4_squeeze = Convolution2D(
32, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire4_squeeze',
data_format="channels_first")(merge3)
fire4_expand1 = Convolution2D(
128, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire4_expand1',
data_format="channels_first")(fire4_squeeze)
fire4_expand2 = Convolution2D(
128, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire4_expand2',
data_format="channels_first")(fire4_squeeze)
merge4 = Concatenate(axis=1)([fire4_expand1, fire4_expand2])
maxpool4 = MaxPooling2D(
pool_size=(3, 3), strides=(2, 2), name='maxpool4',
data_format="channels_first")(merge4)
fire5_squeeze = Convolution2D(
32, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire5_squeeze',
data_format="channels_first")(maxpool4)
fire5_expand1 = Convolution2D(
128, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire5_expand1',
data_format="channels_first")(fire5_squeeze)
fire5_expand2 = Convolution2D(
128, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire5_expand2',
data_format="channels_first")(fire5_squeeze)
merge5 = Concatenate(axis=1)([fire5_expand1, fire5_expand2])
fire6_squeeze = Convolution2D(
48, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire6_squeeze',
data_format="channels_first")(merge5)
fire6_expand1 = Convolution2D(
192, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire6_expand1',
data_format="channels_first")(fire6_squeeze)
fire6_expand2 = Convolution2D(
192, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire6_expand2',
data_format="channels_first")(fire6_squeeze)
merge6 = Concatenate(axis=1)([fire6_expand1, fire6_expand2])
fire7_squeeze = Convolution2D(
48, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire7_squeeze',
data_format="channels_first")(merge6)
fire7_expand1 = Convolution2D(
192, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire7_expand1',
data_format="channels_first")(fire7_squeeze)
fire7_expand2 = Convolution2D(
192, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire7_expand2',
data_format="channels_first")(fire7_squeeze)
merge7 = Concatenate(axis=1)([fire7_expand1, fire7_expand2])
fire8_squeeze = Convolution2D(
64, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire8_squeeze',
data_format="channels_first")(merge7)
fire8_expand1 = Convolution2D(
256, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire8_expand1',
data_format="channels_first")(fire8_squeeze)
fire8_expand2 = Convolution2D(
256, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire8_expand2',
data_format="channels_first")(fire8_squeeze)
merge8 = Concatenate(axis=1)([fire8_expand1, fire8_expand2])
maxpool8 = MaxPooling2D(
pool_size=(3, 3), strides=(2, 2), name='maxpool8',
data_format="channels_first")(merge8)
fire9_squeeze = Convolution2D(
64, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire9_squeeze',
data_format="channels_first")(maxpool8)
fire9_expand1 = Convolution2D(
256, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire9_expand1',
data_format="channels_first")(fire9_squeeze)
fire9_expand2 = Convolution2D(
256, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
padding='same', name='fire9_expand2',
data_format="channels_first")(fire9_squeeze)
merge9 = Concatenate(axis=1)([fire9_expand1, fire9_expand2])
fire9_dropout = Dropout(0.5, name='fire9_dropout')(merge9)
conv10 = Convolution2D(
nb_classes, (1, 1), kernel_initializer='glorot_uniform',
padding='valid', name='conv10',
data_format="channels_first")(fire9_dropout)
# The size should match the output of conv10
avgpool10 = AveragePooling2D(
(13, 13), name='avgpool10',
data_format="channels_first")(conv10)
flatten = Flatten(name='flatten')(avgpool10)
softmax = Activation("softmax", name='softmax')(flatten)
return Model(inputs=input_img, outputs=softmax)