Function vgg19

tensorlayer/models/vgg.py:261–320

Pre-trained VGG19 model. Parameters ------------ pretrained : boolean Whether to load pretrained weights. Default False. end_with : str The end point of the model. Default ``fc3_relu`` i.e. the whole model. mode : str. Model building mode, 'dynamic' or 's

(pretrained=False, end_with='outputs', mode='dynamic', name=None)

def vgg19(pretrained=False, end_with='outputs', mode='dynamic', name=None):
    """Pre-trained VGG19 model.

    Parameters
    ------------
    pretrained : boolean
        Whether to load pretrained weights. Default False.
    end_with : str
        The end point of the model. Default ``fc3_relu`` i.e. the whole model.
    mode : str.
        Model building mode, 'dynamic' or 'static'. Default 'dynamic'.
    name : None or str
        A unique layer name.

    Examples
    ---------
    Classify ImageNet classes with VGG19, see `tutorial_models_vgg.py <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_models_vgg.py>`__
    With TensorLayer

    >>> # get the whole model, without pre-trained VGG parameters
    >>> vgg = tl.models.vgg19()
    >>> # get the whole model, restore pre-trained VGG parameters
    >>> vgg = tl.models.vgg19(pretrained=True)
    >>> # use for inferencing
    >>> output = vgg(img, is_train=False)
    >>> probs = tf.nn.softmax(output)[0].numpy()

    Extract features with VGG19 and Train a classifier with 100 classes

    >>> # get VGG without the last layer
    >>> cnn = tl.models.vgg19(end_with='fc2_relu', mode='static').as_layer()
    >>> # add one more layer and build a new model
    >>> ni = Input([None, 224, 224, 3], name="inputs")
    >>> nn = cnn(ni)
    >>> nn = tl.layers.Dense(n_units=100, name='out')(nn)
    >>> model = tl.models.Model(inputs=ni, outputs=nn)
    >>> # train your own classifier (only update the last layer)
    >>> train_params = model.get_layer('out').trainable_weights

    Reuse model

    >>> # in dynamic model, we can directly use the same model
    >>> # in static model
    >>> vgg_layer = tl.models.vgg19().as_layer()
    >>> ni_1 = tl.layers.Input([None, 224, 244, 3])
    >>> ni_2 = tl.layers.Input([None, 224, 244, 3])
    >>> a_1 = vgg_layer(ni_1)
    >>> a_2 = vgg_layer(ni_2)
    >>> M = Model(inputs=[ni_1, ni_2], outputs=[a_1, a_2])

    """
    if mode == 'dynamic':
        model = VGG(layer_type='vgg19', batch_norm=False, end_with=end_with, name=name)
    elif mode == 'static':
        model = VGG_static(layer_type='vgg19', batch_norm=False, end_with=end_with, name=name)
    else:
        raise Exception("No such mode %s" % mode)
    if pretrained: