tf.keras.applications.ResNet50

Aliases:

• tf.keras.applications.ResNet50
• tf.keras.applications.resnet50.ResNet50

tf.keras.applications.ResNet50(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000
)

Defined in tensorflow/python/keras/_impl/keras/applications/resnet50.py.

Instantiates the ResNet50 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.

Arguments:

• include_top: whether to include the fully-connected layer at the top of the network.
• weights: one of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded.
• 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 (224, 224, 3) (with channels_last data format) or (3, 224, 224) (with channels_first data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 197. E.g. (200, 200, 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.