tf.keras.applications.ResNet152

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Instantiates the ResNet152 architecture.

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Main aliases

tf.keras.applications.resnet.ResNet152

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.applications.ResNet152, tf.compat.v1.keras.applications.resnet.ResNet152

tf.keras.applications.ResNet152(
    include_top=True, weights='imagenet', input_tensor=None, input_shape=None,
    pooling=None, classes=1000, **kwargs
)

Reference paper:

• Deep Residual Learning for Image Recognition (CVPR 2015)

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
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.

Returns
A Keras model instance.