Write a message to the log file :param msg: the message string to be written to the log file

Log values in an existent log file :param log_name: The name of the log file :param key: The key attribute for logging the

Compute the accuracy from the confusion matrix :param cm: confusion matrix :return: the accuracy score

1x1 convolution

3x3 convolution with padding

Create a csv for logging information :param log_name: The name of the log. The log filename will be <log_name>.csv. :param

Get data loaders

Save the arguments in the specified directory as - a text file called 'args.txt' - a pickle file called 'args.pickle' :pa

r"""Densenet-121 model from `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ Args: pretrained (bo

r"""Densenet-161 model from `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ Args: pretrained (bo

r"""Densenet-169 model from `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ Args: pretrained (bo

r"""Densenet-201 model from `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ Args: pretrained (bo

Perform convolution over the input using the squared L2 distance for all prototypes in the layer :param xs: A batch of input images

Load the proper dataset based on the parsed arguments :param args: The arguments in which is specified which dataset should be used :r

Construct the optimizer as dictated by the parsed arguments :param tree: The tree that should be optimized :param args: Parsed argumen

the number of conv layers in the network

the number of conv layers in the network, not counting the number of bypass layers

Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a ResNet-152 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a ResNet-34 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

VGG 11-layer model (configuration "A") with batch normalization Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

VGG 11-layer model (configuration "A") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

VGG 13-layer model (configuration "B") with batch normalization Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

VGG 13-layer model (configuration "B") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

VGG 16-layer model (configuration "D") with batch normalization Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

VGG 16-layer model (configuration "D") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

VGG 19-layer model (configuration 'E') with batch normalization Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

VGG 19-layer model (configuration "E") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet