tf.keras.applications.EfficientNetB5
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Instantiates the EfficientNetB5 architecture.
tf.keras.applications.EfficientNetB5(
include_top=True, weights='imagenet', input_tensor=None, input_shape=None,
pooling=None, classes=1000, classifier_activation='softmax', **kwargs
)
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.
If you have never configured it, it defaults to "channels_last".
Arguments |
|---|
include_top
|
Whether to include the fully-connected
layer at the top of the network. Defaults to True.
|
weights
|
One of None (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded. Defaults to 'imagenet'.
|
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.
It should have exactly 3 inputs channels.
|
pooling
|
Optional pooling mode for feature extraction
when include_top is False. Defaults to None.
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. Defaults to 1000 (number of
ImageNet classes).
|
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.
Defaults to 'softmax'.
|
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Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.keras.applications.EfficientNetB5\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.3.0/tensorflow/python/keras/applications/efficientnet.py#L647-L670) |\n\nInstantiates the EfficientNetB5 architecture.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.keras.applications.efficientnet.EfficientNetB5`](/api_docs/python/tf/keras/applications/EfficientNetB5)\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.keras.applications.EfficientNetB5`](/api_docs/python/tf/keras/applications/EfficientNetB5), [`tf.compat.v1.keras.applications.efficientnet.EfficientNetB5`](/api_docs/python/tf/keras/applications/EfficientNetB5)\n\n\u003cbr /\u003e\n\n tf.keras.applications.EfficientNetB5(\n include_top=True, weights='imagenet', input_tensor=None, input_shape=None,\n pooling=None, classes=1000, classifier_activation='softmax', **kwargs\n )\n\n#### Reference:\n\n- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) (ICML 2019)\n\nOptionally loads weights pre-trained on ImageNet.\nNote that the data format convention used by the model is\nthe one specified in your Keras config at `~/.keras/keras.json`.\nIf you have never configured it, it defaults to `\"channels_last\"`.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Arguments --------- ||\n|-------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `include_top` | Whether to include the fully-connected layer at the top of the network. Defaults to True. |\n| `weights` | One of `None` (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to 'imagenet'. |\n| `input_tensor` | Optional Keras tensor (i.e. output of [`layers.Input()`](../../../tf/keras/Input)) to use as image input for the model. |\n| `input_shape` | Optional shape tuple, only to be specified if `include_top` is False. It should have exactly 3 inputs channels. |\n| `pooling` | Optional pooling mode for feature extraction when `include_top` is `False`. Defaults to None. \u003cbr /\u003e - `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. |\n| `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. Defaults to 1000 (number of ImageNet classes). |\n| `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. Defaults to 'softmax'. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A [`keras.Model`](../../../tf/keras/Model) instance. ||\n\n\u003cbr /\u003e"]]
View source on GitHub