def get_vgg_twoeyes() 是我的模型的定义。 我已经加载了一个预训练模型，该模型在同一台计算机上训练，然后我想对模型进行微调。在重新训练模型之前，我设置了 model.trainable false，以确保模型的权重是固定的。在我训练之前，重量与节省的重量相同。我发现model.fit的输出与model.predict的输出不同。 正如我假设具有相同权重的 model.fit 与 model.predict 应该输出相同的结果，因为 model.trainable 是假的，这意味着 model.fit 的行为是 model.predict。
def get_vgg_twoeyes(optimizer='adam'， model_type='VGG16'，fc1_size=1024， fc2_size=512， fc3_size=256)：
kern_init = initializers.glorot_normal()
img_input = Input(shape=(36, 60, 3), name='img_input')
headpose_input = Input(shape=(2,), name='headpose_input')
# create the base pre-trained model
if model_type == 'VGG19':
base_model = VGG19(input_tensor=img_input, weights='imagenet', include_top=False)
elif model_type == 'VGG16':
base_model = VGG16(input_tensor=img_input, weights='imagenet', include_top=False)
else:
raise Exception('Unknown model type in get_vgg_twoeyes')
# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(fc1_size, kernel_initializer=kern_init)(x)
x = concatenate([x, headpose_input])
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dense(fc2_size, kernel_initializer=kern_init)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
gaze_predictions = Dense(2, kernel_initializer=kern_init, name='pred_gaze')(x)
# this is the model we will train
model = Model(inputs=[img_input, headpose_input], outputs=gaze_predictions)
model.compile(optimizer=optimizer, loss=angle_loss, metrics=['accuracy', accuracy_angle])
return model
# fine-tune the model
models=load_model(model_path + "15Fold" + prefix + ''+str(i) +
suffix + ".h5",custom_objects={'accuracy_angle':accuracy_angle,
'angle_loss': angle_loss}))
model.trainable=False
adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.95)
model.compile(optimizer=adam, loss=angle_loss, metrics= ['accuracy', accuracy_angle])
model.fit({'img_input':cal_images,'headpose_input':cal_headposes},
cal_gazes,shuffle=False,batch_size=32,epochs=1,callbacks=
[losshistory()])
predgaze=model.predict({'img_input': cal_images, 'headpose_input':
cal_headposes}, batch_size=2,verbose=1)
设置model.trainable=False后，您可能必须再次编译模型。否则，您可以像
for l in model.layers:
l.trainable=False