Check one inference step.

Check that generator can take unknown batch dimension inputs.

Defines Mobilenet training scope. Usage: with tf.contrib.slim.arg_scope(mobilenet.training_scope()): logits, endpoints = mobilenet_v2.m

Defines MobilenetV2 training scope. Usage: with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()): logits, endpoints = mobilenet

Upsamples the given inputs. Args: net: A `Tensor` of size [batch_size, height, width, filters]. num_outputs: The number of output filters.

Oxford Net VGG 16-Layers version D Example. Note: All the fully_connected layers have been transformed to conv2d layers. To use in classifi

Oxford Net VGG 19-Layers version E Example. Note: All the fully_connected layers have been transformed to conv2d layers. To use in classifi

Oxford Net VGG 11-Layers version A Example. Note: All the fully_connected layers have been transformed to conv2d layers. To use in classifi

Defines the VGG arg scope. Args: weight_decay: The l2 regularization coefficient. Returns: An arg_scope.

Writes a file with the list of class names. Args: labels_to_class_names: A map of (integer) labels to class names. dataset_dir: The directo