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train.py
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import keras
from keras.models import Model
from keras.layers import Input, Activation, merge
from keras.layers import Flatten, Dropout
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers import AveragePooling2D
from keras.optimizers import Adam
import keras.backend as K
from dataHandler import dataHandler
from consts import *
K.set_image_dim_ordering('tf')
def SqueezeNet(nb_classes, inputs=(224, 224, 3)):
""" 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', init='glorot_uniform',
subsample=(2, 2), border_mode='same', name='conv1')(input_img)
maxpool1 = MaxPooling2D(
pool_size=(3, 3), strides=(2, 2), name='maxpool1')(conv1)
fire2_squeeze = Convolution2D(
16, 1, 1, activation='relu', init='glorot_uniform',
border_mode='same', name='fire2_squeeze')(maxpool1)
fire2_expand1 = Convolution2D(
64, 1, 1, activation='relu', init='glorot_uniform',
border_mode='same', name='fire2_expand1')(fire2_squeeze)
fire2_expand2 = Convolution2D(
64, 3, 3, activation='relu', init='glorot_uniform',
border_mode='same', name='fire2_expand2')(fire2_squeeze)
merge2 = merge(
[fire2_expand1, fire2_expand2], mode='concat', concat_axis=-1)
fire3_squeeze = Convolution2D(
16, 1, 1, activation='relu', init='glorot_uniform',
border_mode='same', name='fire3_squeeze')(merge2)
fire3_expand1 = Convolution2D(
64, 1, 1, activation='relu', init='glorot_uniform',
border_mode='same', name='fire3_expand1')(fire3_squeeze)
fire3_expand2 = Convolution2D(
64, 3, 3, activation='relu', init='glorot_uniform',
border_mode='same', name='fire3_expand2')(fire3_squeeze)
merge3 = merge(
[fire3_expand1, fire3_expand2], mode='concat', concat_axis=-1)
fire4_squeeze = Convolution2D(
32, 1, 1, activation='relu', init='glorot_uniform',
border_mode='same', name='fire4_squeeze')(merge3)
fire4_expand1 = Convolution2D(
128, 1, 1, activation='relu', init='glorot_uniform',
border_mode='same', name='fire4_expand1')(fire4_squeeze)
fire4_expand2 = Convolution2D(
128, 3, 3, activation='relu', init='glorot_uniform',
border_mode='same', name='fire4_expand2')(fire4_squeeze)
merge4 = merge(
[fire4_expand1, fire4_expand2], mode='concat', concat_axis=-1)
maxpool4 = MaxPooling2D(
pool_size=(3, 3), strides=(2, 2), name='maxpool4')(merge4)
fire5_squeeze = Convolution2D(
32, 1, 1, activation='relu', init='glorot_uniform',
border_mode='same', name='fire5_squeeze')(maxpool4)
fire5_expand1 = Convolution2D(
128, 1, 1, activation='relu', init='glorot_uniform',
border_mode='same', name='fire5_expand1')(fire5_squeeze)
fire5_expand2 = Convolution2D(
128, 3, 3, activation='relu', init='glorot_uniform',
border_mode='same', name='fire5_expand2')(fire5_squeeze)
merge5 = merge(
[fire5_expand1, fire5_expand2], mode='concat', concat_axis=-1)
fire6_squeeze = Convolution2D(
48, 1, 1, activation='relu', init='glorot_uniform',
border_mode='same', name='fire6_squeeze')(merge5)
fire6_expand1 = Convolution2D(
192, 1, 1, activation='relu', init='glorot_uniform',
border_mode='same', name='fire6_expand1')(fire6_squeeze)
fire6_expand2 = Convolution2D(
192, 3, 3, activation='relu', init='glorot_uniform',
border_mode='same', name='fire6_expand2')(fire6_squeeze)
merge6 = merge(
[fire6_expand1, fire6_expand2], mode='concat', concat_axis=-1)
fire7_squeeze = Convolution2D(
48, 1, 1, activation='relu', init='glorot_uniform',
border_mode='same', name='fire7_squeeze')(merge6)
fire7_expand1 = Convolution2D(
192, 1, 1, activation='relu', init='glorot_uniform',
border_mode='same', name='fire7_expand1')(fire7_squeeze)
fire7_expand2 = Convolution2D(
192, 3, 3, activation='relu', init='glorot_uniform',
border_mode='same', name='fire7_expand2')(fire7_squeeze)
merge7 = merge(
[fire7_expand1, fire7_expand2], mode='concat', concat_axis=-1)
fire8_squeeze = Convolution2D(
64, 1, 1, activation='relu', init='glorot_uniform',
border_mode='same', name='fire8_squeeze')(merge7)
fire8_expand1 = Convolution2D(
256, 1, 1, activation='relu', init='glorot_uniform',
border_mode='same', name='fire8_expand1')(fire8_squeeze)
fire8_expand2 = Convolution2D(
256, 3, 3, activation='relu', init='glorot_uniform',
border_mode='same', name='fire8_expand2')(fire8_squeeze)
merge8 = merge(
[fire8_expand1, fire8_expand2], mode='concat', concat_axis=-1)
maxpool8 = MaxPooling2D(
pool_size=(3, 3), strides=(2, 2), name='maxpool8')(merge8)
fire9_squeeze = Convolution2D(
64, 1, 1, activation='relu', init='glorot_uniform',
border_mode='same', name='fire9_squeeze')(maxpool8)
fire9_expand1 = Convolution2D(
256, 1, 1, activation='relu', init='glorot_uniform',
border_mode='same', name='fire9_expand1')(fire9_squeeze)
fire9_expand2 = Convolution2D(
256, 3, 3, activation='relu', init='glorot_uniform',
border_mode='same', name='fire9_expand2')(fire9_squeeze)
merge9 = merge(
[fire9_expand1, fire9_expand2], mode='concat', concat_axis=-1)
fire9_dropout = Dropout(0.5, name='fire9_dropout')(merge9)
conv10 = Convolution2D(
nb_classes, 1, 1, init='glorot_uniform',
border_mode='valid', name='conv10')(fire9_dropout)
# The size should match the output of conv10
avgpool10 = AveragePooling2D((13, 13), name='avgpool10')(conv10)
flatten = Flatten(name='flatten')(avgpool10)
softmax = Activation("softmax", name='softmax')(flatten)
return Model(input=input_img, output=softmax)
if __name__ == '__main__':
model = SqueezeNet(3, (IMAGE_HEIGHT, IMAGE_WIDTH, 3))
dHandler = dataHandler()
train_generator, validation_generator = dHandler.getGenerators()
adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, decay=0.01)
model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])
tensorBoard = keras.callbacks.TensorBoard(log_dir='./logs', histogram_freq=1, write_graph=True, write_images=True)
earlyStopping = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=12, verbose=0, mode='auto')
modelCheckpoints = keras.callbacks.ModelCheckpoint(MODELS_CHECKPOINTS_DIR +
"/squeezeNet_" + str(IMAGE_WIDTH) + "_" +
str(IMAGE_HEIGHT) +
".{epoch:02d}-{val_loss:.2f}-{val_acc:.2f}.hdf5",
monitor='val_loss', verbose=0, save_best_only=False,
save_weights_only=False, mode='auto')
hists = model.fit_generator(train_generator, TRAIN_IMAGES_PER_EPOCH, TRAINING_EPOCHS,
validation_data=validation_generator,
nb_val_samples=VALIDATE_IMAGES_PER_EPOCH,
callbacks=[modelCheckpoints, tensorBoard, earlyStopping])
model.save_weights("challenge1.model")