contrib.keras.applications.InceptionV3

tf.contrib.keras.applications.InceptionV3

tf.contrib.keras.applications.InceptionV3

tf.contrib.keras.applications.inception_v3.InceptionV3

InceptionV3(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000
)

Defined in tensorflow/contrib/keras/python/keras/applications/inception_v3.py.

Instantiates the Inception v3 architecture.

Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set image_data_format="channels_last" in your Keras config at ~/.keras/keras.json. The model and the weights are compatible with both TensorFlow and Theano. The data format convention used by the model is the one specified in your Keras config file. Note that the default input image size for this model is 299x299.

Arguments:

include_top: whether to include the fully-connected
    layer at the top of the network.
weights: one of `None` (random initialization)
    or "imagenet" (pre-training on ImageNet).
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
    to use as image input for the model.
input_shape: optional shape tuple, only to be specified
    if `include_top` is False (otherwise the input shape
    has to be `(299, 299, 3)` (with `channels_last` data format)
    or `(3, 299, 299)` (with `channels_first` data format).
    It should have exactly 3 inputs channels,
    and width and height should be no smaller than 139.
    E.g. `(150, 150, 3)` would be one valid value.
pooling: Optional pooling mode for feature extraction
    when `include_top` is `False`.
    - `None` means that the output of the model will be
        the 4D tensor output of the
        last convolutional layer.
    - `avg` means that global average pooling
        will be applied to the output of the
        last convolutional layer, and thus
        the output of the model will be a 2D tensor.
    - `max` means that global max pooling will
        be applied.
classes: optional number of classes to classify images
    into, only to be specified if `include_top` is True, and
    if no `weights` argument is specified.

Returns:

A Keras model instance.

Raises:

ValueError: in case of invalid argument for `weights`,
    or invalid input shape.