tf.keras.applications.NASNetLarge
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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,
classifier_activation='softmax'
)
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
.
Args |
---|
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. |
classifier_activation | A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True . Set classifier_activation=None to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be None or "softmax" . |
Returns |
---|
A Keras model instance. |
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Last updated 2024-06-07 UTC.
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