tf.keras.applications.NASNetLarge
Instantiates a NASNet model in ImageNet mode.
tf.keras.applications.NASNetLarge(
input_shape=None, include_top=True, weights='imagenet',
input_tensor=None, pooling=None, classes=1000
)
Reference:
Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json.
Note: each Keras Application expects a specific kind of input preprocessing. For NASNet, call tf.keras.applications.nasnet.preprocess_input on your inputs before passing them to the model.
| Arguments |
|---|
input_shape | Optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (331, 331, 3) for NASNetLarge. It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (224, 224, 3) would be one valid value. |
include_top | Whether to include the fully-connected layer at the top of the network. |
weights | None (random initialization) or imagenet (ImageNet weights) For loading imagenet weights, input_shape should be (331, 331, 3) |
input_tensor | Optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model. |
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. |
RuntimeError | If attempting to run this model with a backend that does not support separable convolutions. |