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
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.