在 keras 中多次拟合模型

Question

I am using model.fit() several times, each time is responsible for training a block of layers where other layers are freezed

CODE

 # create the base pre-trained model
    base_model = efn.EfficientNetB0(input_tensor=input_tensor,weights='imagenet', include_top=False)

    # add a global spatial average pooling layer
    x = base_model.output
    x = GlobalAveragePooling2D()(x)    

    # add a fully-connected layer
    x = Dense(x.shape[1], activation='relu',name='first_dense')(x)
    x=Dropout(0.5)(x)
    x = Dense(x.shape[1], activation='relu',name='output')(x)
    x=Dropout(0.5)(x)

    no_classes=10
    predictions = Dense(no_classes, activation='softmax')(x)

    # this is the model we will train
    model = Model(inputs=base_model.input, outputs=predictions)

    # first: train only the top layers (which were randomly initialized)
    # i.e. freeze all convolutional layers
    for layer in base_model.layers:
        layer.trainable = False

    #FIRST COMPILE
    model.compile(optimizer='Adam', loss=loss_function,
                 metrics=['accuracy'])

    #FIRST FIT
    model.fit(features[train], labels[train],
              batch_size=batch_size,
              epochs=top_epoch,
              verbose=verbosity,
              validation_split=validation_split)

    # Generate generalization metrics
    scores = model.evaluate(features[test], labels[test], verbose=1)  
  
    print(scores)

     #Let all layers be trainable        
    for layer in model.layers:
        layer.trainable = True    

    from tensorflow.keras.optimizers import SGD
#FIRST COMPILE
    model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss=loss_function,
                 metrics=['accuracy'])

    #SECOND FIT
    model.fit(features[train], labels[train],
              batch_size=batch_size,
              epochs=no_epochs,
              verbose=verbosity,
              validation_split=validation_split)
    

What is weird is that in the second fit , accuracy resulted from first epoch is much lower that the accuracy of the last epoch of the first fit .

RESULT

Epoch 40/40 6286/6286 [==============================] - 14s 2ms/sample - loss: 0.2370 - accuracy: 0.9211 - val_loss: 1.3579 - val_accuracy: 0.6762 874/874 [==============================] - 2s 2ms/sample - loss: 0.4122 - accuracy: 0.8764

Train on 6286 samples, validate on 1572 samples Epoch 1/40 6286/6286 [==============================] - 60s 9ms/sample - loss: 5.9343 - accuracy: 0.5655 - val_loss: 2.4981 - val_accuracy: 0.5115

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I think the weights of the second fit are not taken from the first fit

Thanks in advance!!!

Answer 1

I think this is the result of using a different optimizer. You used Adam in the first fit and SGD in the second fit. Try using Adam in the second fit and see if it works correctly

Answer 2

我通过删除第二个编译器解决了这个问题。