Sets self._train_op, op to run for training.

Verify the values of dense feature extraction by atrous convolution. Make sure that dense feature extraction by stack_blocks_dense() followed by

Verify the values of dense feature extraction by atrous convolution. Make sure that dense feature extraction by stack_blocks_dense() followed by

Calculate the average gradient for each shared variable across all towers. Note that this function provides a synchronization point across all towe

Build a lookup from image file to bounding boxes. Args: bounding_box_file: string, path to file with bounding boxes annotations. Assumes

Build the core model within the graph.

Build lookup for synset to human-readable label. Args: imagenet_metadata_file: string, path to file containing mapping from synset to hum

Build training specific ops for the graph.

Wrapper for inserting a bytes FeatureList into a SequenceExample proto.

Performs central crops of the given image list. Args: image_list: a list of image tensors of the same dimension but possibly varying chan

Removes temporary files used to create the dataset. Args: dataset_dir: The directory where the temporary files are stored.

Removes temporary files used to create the dataset. Args: dataset_dir: The directory where the temporary files are stored.

Removes temporary files used to create the dataset. Args: dataset_dir: The directory where the temporary files are stored.

Compute delta for given log_moments and eps. Args: log_moments: the log moments of privacy loss, in the form of pairs of (moment_order, l

Compute delta for given log_moments and eps. Args: log_moments: the log moments of privacy loss, in the form of pairs of (moment_or

Compute epsilon for given log_moments and delta. Args: log_moments: the log moments of privacy loss, in the form of pairs of (moment_orde

Compute high moment of privacy loss. Args: sigma: the noise sigma, in the multiples of the sensitivity. q: the sampling ratio.

Configures the learning rate. Args: num_samples_per_epoch: The number of samples in each epoch of training. global_step: The global_step te

Configures the optimizer used for training. Args: learning_rate: A scalar or `Tensor` learning rate. Returns: An instance of an optimize

Build an Example proto for an example. Args: filename: string, path to an image file, e.g., '/path/to/example.JPG' image_buffer: string, JP

Build an Example proto for an example. Args: filename: string, path to an image file, e.g., '/path/to/example.JPG' image_buffer: string, JP

Counts the number of parameters in the inception model at top scope.

Counts the number of parameters in the model at top level scope.

Creates the vocabulary of word to word_id. The vocabulary is saved to disk in a text file of word counts. The id of each word in the file is its

L2 weight decay loss.

Downloads MNIST locally. Args: dataset_dir: The directory where the temporary files are stored.

Runs Eval once. Args: saver: Saver. summary_writer: Summary writer. top_1_op: Top 1 op. top_5_op: Top 5 op. summary_op: Summary

Get a loop_function that extracts the previous symbol and embeds it. Args: embedding: embedding tensor for symbols. output_projection: None

Extract the images into a numpy array. Args: filename: The path to an MNIST images file. num_images: The number of images in the file. R

Extract the labels into a vector of int64 label IDs. Args: filename: The path to an MNIST labels file. num_labels: The number of labels in

Build a list of human-readable labels. Args: synsets: list of strings; each string is a unique WordNet ID. synset_to_human: dict of synset

Find the bounding boxes for a given image file. Args: filenames: list of strings; each string is a path to an image file. image_to_bboxes:

Build a list of all images files and labels in the data set. Args: data_dir: string, path to the root directory of images. Assumes that

Build a list of all images files and labels in the data set. Args: data_dir: string, path to the root directory of images. Assumes that

FullyConnected layer for final output.

Returns a list of filenames and inferred class names. Args: dataset_dir: A directory containing a set of subdirectories representing clas

Returns a function run by the chief worker to warm-start the training. Note that the init_fn is only run when initializing the model during the ver

Returns a list of variables to train. Returns: A list of variables to train by the optimizer.

Wrapper for inserting an int64 FeatureList into a SequenceExample proto.

Determine if file contains a CMYK JPEG format image. Args: filename: string, path of the image file. Returns: boolean indicating if the

Determine if a file contains a PNG format image. Args: filename: string, path of the image file. Returns: boolean indicating if the imag

Determine if a file contains a PNG format image. Args: filename: string, path of the image file. Returns: boolean indicating if the imag

Randomly select a file and read it.

Read one file and convert to ids. Args: shard_name: file path. Returns: list of (id, char_id, global_word_id) tuples.

Compute losses and gradients for a single clone. Args: optimizer: A tf.Optimizer object. clone: A Clone namedtuple. num_clones: The nu

Processes a caption string into a list of tonenized words. Args: caption: A string caption. Returns: A list of strings; the tokenized ca

Process a single image file. Args: filename: string, path to an image file e.g., '/path/to/example.JPG'. coder: instance of ImageCoder to p

Process a single image file. Args: filename: string, path to an image file e.g., '/path/to/example.JPG'. coder: instance of ImageCoder to p

Process and save list of images as TFRecord of Example protos. Args: name: string, unique identifier specifying the data set filenames: lis

Process and save list of images as TFRecord of Example protos. Args: name: string, unique identifier specifying the data set filenames: lis

Crops the given list of images. The function applies the same crop to each image in the list. This can be effectively applied when there are mult

Define kernel size which is automatically reduced for small input. If the shape of the input images is unknown at graph construction time this fu

A plain ResNet without extra layers before or after the ResNet blocks.

A plain ResNet without extra layers before or after the ResNet blocks.

:param record: VideoRecord :return: list

Computes new shape with the smallest side equal to `smallest_side`. Computes new shape with the smallest side equal to `smallest_side` while pres

A simplified ResNet Block stacker without output stride control.

A simplified ResNet Block stacker without output stride control.

Calculate the sum gradient for each shared variable across all clones. This function assumes that the clone_grads has been scaled appropriately by