tf.keras.applications.ResNet50( include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000 )
Instantiates the ResNet50 architecture.
Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set
image_data_format='channels_last' in your Keras config at ~/.keras/keras.json.
The model and the weights are compatible with both TensorFlow and Theano. The data format convention used by the model is the one specified in your Keras config file.
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_topis False (otherwise the input shape has to be
(224, 224, 3)(with
channels_lastdata format) or
(3, 224, 224)(with
channels_firstdata format). It should have exactly 3 inputs channels, and width and height should be no smaller than 197. E.g.
(200, 200, 3)would be one valid value.
pooling: Optional pooling mode for feature extraction when
Nonemeans that the output of the model will be the 4D tensor output of the last convolutional layer. -
avgmeans 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. -
maxmeans that global max pooling will be applied.
classes: optional number of classes to classify images into, only to be specified if
include_topis True, and if no
weightsargument is specified.
A Keras model instance.
ValueError: in case of invalid argument for
weights, or invalid input shape.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.