CNN_train_UCF101.py 文件源码

def get_model(weights='imagenet'):
    # create the base pre-trained model
    base_model = InceptionV3(weights=weights, include_top=False)

    # add a global spatial average pooling layer
    x = base_model.output
    x = GlobalAveragePooling2D()(x)
    # let's add a fully-connected layer
    x = Dense(1024, activation='relu')(x)
    # and a logistic layer -- let's say we have 2 classes
    predictions = Dense(len(data.classes), activation='softmax')(x)

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

    for layer in base_model.layers:
        layer.trainable = False

    # compile the model (should be done *after* setting layers to non-trainable)
    model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

    return model