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include_top: whether to include the fully-connected layer at the top of the network. |
weights: one of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. |
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 (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. |
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 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. |
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. |
ResNet101 function |
tf.keras.applications.ResNet101( |
include_top=True, |
weights="imagenet", |
input_tensor=None, |
input_shape=None, |
pooling=None, |
classes=1000, |
**kwargs |
) |
Instantiates the ResNet101 architecture. |
Reference |
Deep Residual Learning for Image Recognition (CVPR 2015) |
For image classification use cases, see this page for detailed examples. |
For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning. |
Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call tf.keras.applications.resnet.preprocess_input on your inputs before passing them to the model. resnet.preprocess_input will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling. |
Arguments |
include_top: whether to include the fully-connected layer at the top of the network. |
weights: one of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. |
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 (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. |
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 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. |
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. |
ResNet152 function |
tf.keras.applications.ResNet152( |
include_top=True, |
weights="imagenet", |
input_tensor=None, |
input_shape=None, |
pooling=None, |
classes=1000, |
**kwargs |
) |
Instantiates the ResNet152 architecture. |
Reference |
Deep Residual Learning for Image Recognition (CVPR 2015) |
For image classification use cases, see this page for detailed examples. |
For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning. |
Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call tf.keras.applications.resnet.preprocess_input on your inputs before passing them to the model. resnet.preprocess_input will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling. |
Arguments |
include_top: whether to include the fully-connected layer at the top of the network. |
weights: one of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. |
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 (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. |
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 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. |
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. |
ResNet50V2 function |
tf.keras.applications.ResNet50V2( |
include_top=True, |
weights="imagenet", |
input_tensor=None, |
input_shape=None, |
pooling=None, |
classes=1000, |
classifier_activation="softmax", |
) |
Instantiates the ResNet50V2 architecture. |