Instantiates a Mobile NASNet model in ImageNet mode.
tf.keras.applications.NASNetMobile(
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.
Arguments |
|---|
input_shape
|
Optional shape tuple, only to be specified
if include_top is False (otherwise the input shape
has to be (224, 224, 3) for NASNetMobile
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 (224, 224, 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.
|
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