def CreateResNetModel(self, input_shape, classes):
X_input = Input(name='the_input', shape=input_shape)
x = Conv2D(kernel_size=3,
filters=16,
strides=1,
padding='same',
kernel_regularizer=regularizers.l2(0.01))(X_input)
x = BatchNormalization()(x)
x = Activation(relu)(x)
x = block(16)(x)
x = block(16)(x)
x = BatchNormalization()(x)
x = Activation(relu)(x)
# 28x28x48 -> 1x48
x = GlobalAveragePooling2D()(x)
# dropout for more robust learning
x = Dropout(0.2)(x)
last = Dense(units=classes,
activation='softmax',
kernel_regularizer=regularizers.l2(0.01))(x)
model = Model(inputs=X_input, outputs=last)
optimizer = optimizers.Adadelta()
model.compile(optimizer=optimizer,
loss=[focal_loss(alpha=0.25, gamma=2)])
print('Original resnet neural network established. ')
return model
def CreateCustomizedResNetModel(self, input_shape, classes):
X_input = Input(name='the_input', shape=input_shape)
level_h1 = block(32)(X_input)
level_m1 = MaxPooling2D(pool_size=2, strides=None,
padding="valid")(level_h1) # 池化层
level_h2 = block(64)(level_m1)
level_m2 = MaxPooling2D(pool_size=2, strides=None,
padding="valid")(level_h2) # 池化层
level_h3 = block(128)(level_m2)
level_m3 = MaxPooling2D(pool_size=2, strides=None,
padding="valid")(level_h3) # 池化层
# level_h4 = block(64)(level_m3)
# level_m4 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(level_h4) # 池化层
flayer = Flatten()(level_m3)
fc = Dense(classes, use_bias=True,
kernel_initializer='he_normal')(flayer) # 全连接层
y_pred = Activation('softmax', name='Activation0')(fc)
model = Model(inputs=X_input, outputs=y_pred)
optimizer = optimizers.Adadelta()
# model.compile(optimizer=optimizer, loss='binary_crossentropy')# [focal_loss])
model.compile(optimizer=optimizer,
loss=[focal_loss(alpha=0.25, gamma=2)])
print('Customized resnet model estabished.')
self.modelname = 'residual'
return model
def __CreateCustomizedResNetModel__(self):
level_h1 = block(32)(self.model_input)
level_m1 = MaxPooling2D(pool_size=2, strides=None,
padding="valid")(level_h1) # 池化层
level_h2 = block(64)(level_m1)
level_m2 = MaxPooling2D(pool_size=2, strides=None,
padding="valid")(level_h2) # 池化层
level_h3 = block(128)(level_m2)
level_m3 = MaxPooling2D(pool_size=2, strides=None,
padding="valid")(level_h3) # 池化层
flayer = GlobalAveragePooling2D()(level_m3)
fc = Dense(self.classes, use_bias=True,
kernel_initializer='he_normal')(flayer) # 全连接层
y_pred = Activation('softmax')(fc)
model = Model(inputs=self.model_input, outputs=y_pred)
submodel = Model(inputs=self.model_input, outputs=flayer)
print('Customized resnet model estabished.')
modelname = 'residual'
return model, modelname, submodel