MobileNet
functiontf.keras.applications.MobileNet(
input_shape=None,
alpha=1.0,
depth_multiplier=1,
dropout=0.001,
include_top=True,
weights="imagenet",
input_tensor=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
**kwargs
)
Instantiates the MobileNet architecture.
Reference
Optionally loads weights pre-trained on ImageNet.
Note that the data format convention used by the model is
the one specified in the tf.keras.backend.image_data_format()
.
Note: each Keras Application expects a specific kind of input preprocessing.
For MobileNet, call tf.keras.applications.mobilenet.preprocess_input
on your inputs before passing them to the model.
Arguments
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 32. E.g. (200, 200, 3)
would be one
valid value. Default to None
.
input_shape
will be ignored if the input_tensor
is provided.alpha
< 1.0, proportionally
decreases the number of filters in each layer. - If alpha
> 1.0,
proportionally increases the number of filters in each layer. - If
alpha
= 1, default number of filters from the paper are used at each
layer. Default to 1.0.True
.None
(random initialization), 'imagenet' (pre-training
on ImageNet), or the path to the weights file to be loaded. Default to
imagenet
.layers.Input()
) to
use as image input for the model. input_tensor
is useful for sharing
inputs between multiple different networks. Default to None.include_top
is False
.None
(default) means that the output of the model will be
the 4D tensor output of the last convolutional block.avg
means that global average pooling
will be applied to the output of the
last convolutional block, and thus
the output of the model will be a 2D tensor.max
means that global max pooling will be applied.include_top
is True, and if no weights
argument is
specified. Defaults to 1000.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.Returns
A keras.Model
instance.
Raises
weights
,
or invalid input shape.classifier_activation
is not softmax
or None
when
using a pretrained top layer.MobileNetV2
functiontf.keras.applications.MobileNetV2(
input_shape=None,
alpha=1.0,
include_top=True,
weights="imagenet",
input_tensor=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
**kwargs
)
Instantiates the MobileNetV2 architecture.
Reference
Optionally loads weights pre-trained on ImageNet.
Note: each Keras Application expects a specific kind of input preprocessing.
For MobileNetV2, call tf.keras.applications.mobilenet_v2.preprocess_input
on your inputs before passing them to the model.
Arguments
(160, 160, 3)
would be one valid value.applications.MobileNetV1
model in Keras.alpha
< 1.0, proportionally decreases the number
of filters in each layer.alpha
> 1.0, proportionally increases the number
of filters in each layer.alpha
= 1, default number of filters from the paper
are used at each layer.True
.None
(random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.layers.Input()
)
to use as image input for the model.include_top
is False
.None
means that the output of the model
will be the 4D tensor output of the
last convolutional block.avg
means that global average pooling
will be applied to the output of the
last convolutional block, and thus
the output of the model will be a
2D tensor.max
means that global max pooling will
be applied.include_top
is True, and
if no weights
argument is specified.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.Returns
A keras.Model
instance.
Raises
weights
,
or invalid input shape or invalid alpha, rows when
weights='imagenet'classifier_activation
is not softmax
or None
when
using a pretrained top layer.