tf.keras.applications.resnet_rs.ResNetRS50

Instantiates the ResNetRS50 architecture.

View aliases

Main aliases

tf.keras.applications.ResNetRS50

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.applications.ResNetRS50, tf.compat.v1.keras.applications.resnet_rs.ResNetRS50

tf.keras.applications.resnet_rs.ResNetRS50(
    include_top=True,
    weights='imagenet',
    classes=1000,
    input_shape=None,
    input_tensor=None,
    pooling=None,
    classifier_activation='softmax',
    include_preprocessing=True
)

Reference:

Revisiting ResNets: Improved Training and Scaling Strategies

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 ResNetRs, by default input preprocessing is included as a part of the model (as a Rescaling layer), and thus tf.keras.applications.resnet_rs.preprocess_input is actually a pass-through function. In this use case, ResNetRS models expect their inputs to be float tensors of pixels with values in the [0-255] range. At the same time, preprocessing as a part of the model (i.e. Rescaling layer) can be disabled by setting include_preprocessing argument to False. With preprocessing disabled ResNetRS models expect their inputs to be float tensors of pixels with values in the [-1, 1] range.

Args
depth	Depth of ResNet network.
input_shape	optional shape tuple. 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.
bn_momentum	Momentum parameter for Batch Normalization layers.
bn_epsilon	Epsilon parameter for Batch Normalization layers.
activation	activation function.
se_ratio	Squeeze and Excitation layer ratio.
dropout_rate	dropout rate before final classifier layer.
drop_connect_rate	dropout rate at skip connections.
include_top	whether to include the fully-connected layer at the top of the network.
block_args	list of dicts, parameters to construct block modules.
model_name	name of 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.
weights	one of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. Note: one model can have multiple imagenet variants depending on input shape it was trained with. For input_shape 224x224 pass imagenet-i224 as argument. By default, highest input shape weights are downloaded.
input_tensor	optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
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.
include_preprocessing	Boolean, whether to include the preprocessing layer (Rescaling) at the bottom of the network. Defaults to True. Note: Input image is normalized by ImageNet mean and standard deviation.

Returns
A keras.Model instance.