Create test input tensor. Args: batch_size: The number of images per batch or `None` if unknown. height: The height of each image or `None`

Create test input tensor. Args: batch_size: The number of images per batch or `None` if unknown. height: The height of each image or `None`

Helper function for creating a resnet_v1 bottleneck block. Args: scope: The scope of the block. base_depth: The depth of the bottleneck lay

Helper function for creating a resnet_v2 bottleneck block. Args: scope: The scope of the block. base_depth: The depth of the bottleneck lay

A shallow and thin ResNet v1 for faster tests.

A shallow and thin ResNet v2 for faster tests.

Generator for v1 ResNet models. This function generates a family of ResNet v1 models. See the resnet_v1_*() methods for specific model instantiat

Generator for v2 (preactivation) ResNet models. This function generates a family of ResNet v2 models. See the resnet_v2_*() methods for specific

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

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

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.

A simplified ResNet Block stacker without output stride control.

A simplified ResNet Block stacker without output stride control.

Builds the 8x8 resnet block.

Builds Inception-A block for Inception v4 network.

Builds Inception-B block for Inception v4 network.

Builds Inception-C block for Inception v4 network.

Builds Reduction-A block for Inception v4 network.

Builds Reduction-B block for Inception v4 network.

Check if directory exists otherwise create it.

Check if directory exists otherwise create it.

Check if directory exists otherwise create it.

Check if directory exists otherwise create it.

Check if directory exists otherwise create it.

Check if directory exists otherwise create it.

Check if directory exists otherwise create it.

Check if directory exists otherwise create it.

Returns a 2D Gaussian kernel array.

Returns a 2D Gaussian kernel array.

Returns a 2D Gaussian kernel array.

Returns a 2D Gaussian kernel array.

Returns a 2D Gaussian kernel array.

Returns a 2D Gaussian kernel array.

Returns a 2D Gaussian kernel array.

Returns a 2D Gaussian kernel array.

Inception model from http://arxiv.org/abs/1602.07261. Constructs an Inception Resnet v2 network from inputs to the given final endpoint. This me

Defines the Inception V1 base architecture. This architecture is defined in: Going deeper with convolutions Christian Szegedy, Wei Liu, Yan

Inception v2 (6a2). Constructs an Inception v2 network from inputs to the given final endpoint. This method can construct the network up to the l

Inception model from http://arxiv.org/abs/1512.00567. Constructs an Inception v3 network from inputs to the given final endpoint. This method can

Creates the Inception V4 network up to the given final endpoint. Args: inputs: a 4-D tensor of size [batch_size, height, width, 3]. final_e

Read png images from input directory in batches. Args: input_dir: input directory batch_shape: shape of minibatch array, i.e. [batch_s

Read png images from input directory in batches. Args: input_dir: input directory batch_shape: shape of minibatch array, i.e. [batch_s

Read png images from input directory in batches. Args: input_dir: input directory batch_shape: shape of minibatch array, i.e. [batch_s

Read png images from input directory in batches. Args: input_dir: input directory batch_shape: shape of minibatch array, i.e. [batch_s

Read png images from input directory in batches. Args: input_dir: input directory batch_shape: shape of minibatch array, i.e. [batch_s

Read png images from input directory in batches. Args: input_dir: input directory batch_shape: shape of minibatch array, i.e. [batch_s

Read png images from input directory in batches. Args: input_dir: input directory batch_shape: shape of minibatch array, i.e. [batch_s

Read png images from input directory in batches. Args: input_dir: input directory batch_shape: shape of minibatch array, i.e. [batch_s

Read png images from input directory in batches. Args: input_dir: input directory batch_shape: shape of minibatch array, i.e. [batch_s

Mobilenet v1. Constructs a Mobilenet v1 network from inputs to the given final endpoint. Args: inputs: a tensor of shape [batch_size, height

Saves images to the output directory. Args: images: array with minibatch of images filenames: list of filenames without path

Saves images to the output directory. Args: images: array with minibatch of images filenames: list of filenames without path

Saves images to the output directory. Args: images: array with minibatch of images filenames: list of filenames without path

Saves images to the output directory. Args: images: array with minibatch of images filenames: list of filenames without path

Saves images to the output directory. Args: images: array with minibatch of images filenames: list of filenames without path

Saves images to the output directory. Args: images: array with minibatch of images filenames: list of filenames without path

Saves images to the output directory. Args: images: array with minibatch of images filenames: list of filenames without path

Saves images to the output directory. Args: images: array with minibatch of images filenames: list of filenames without path

AlexNet version 2. Described in: http://arxiv.org/pdf/1404.5997v2.pdf Parameters from: github.com/akrizhevsky/cuda-convnet2/blob/master/layers/

Builds the 17x17 resnet block.

Builds the 35x35 resnet block.

Bottleneck residual unit variant with BN after convolutions. This is the original residual unit proposed in [1]. See Fig. 1(a) of [2] for its def

Bottleneck residual unit variant with BN before convolutions. This is the full preactivation residual unit variant proposed in [2]. See Fig. 1(b)

Creates a variant of the CifarNet model. Note that since the output is a set of 'logits', the values fall in the interval of (-infinity, infinity

Defines the default cifarnet argument scope. Args: weight_decay: The weight decay to use for regularizing the model. Returns: An `arg_sc

Strided 2-D convolution with 'SAME' padding. When stride > 1, then we do explicit zero-padding, followed by conv2d with 'VALID' padding. Note

Returns a network_fn such as `logits, end_points = network_fn(images)`. Args: name: The name of the network. num_classes: The number of cla