Files
file	NeuralNetworks.h

Data Structures
struct	ANeuralNetworksOperandType
	ANeuralNetworksOperandType describes the type of an operand. More...

Typedefs
typedef struct ANeuralNetworksMemory	ANeuralNetworksMemory
	ANeuralNetworksMemory is an opaque type that represents memory. More...

typedef struct ANeuralNetworksModel	ANeuralNetworksModel
	ANeuralNetworksModel is an opaque type that contains a description of the mathematical operations that constitute the model. More...

typedef struct ANeuralNetworksCompilation	ANeuralNetworksCompilation
	ANeuralNetworksCompilation is an opaque type that can be used to compile a machine learning model. More...

typedef struct ANeuralNetworksExecution	ANeuralNetworksExecution
	ANeuralNetworksExecution is an opaque type that can be used to apply a machine learning model to a set of inputs. More...

typedef struct ANeuralNetworksOperandType	ANeuralNetworksOperandType
	ANeuralNetworksOperandType describes the type of an operand. More...

typedef int32_t	ANeuralNetworksOperationType

typedef struct ANeuralNetworksEvent	ANeuralNetworksEvent
	ANeuralNetworksEvent is an opaque type that represents an event that will be signaled once an execution completes.

Enumerations
enum	OperandCode { ANEURALNETWORKS_FLOAT32 = 0, ANEURALNETWORKS_INT32 = 1, ANEURALNETWORKS_UINT32 = 2, ANEURALNETWORKS_TENSOR_FLOAT32 = 3, ANEURALNETWORKS_TENSOR_INT32 = 4, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM = 5 }
	Operand types. More...

enum	OperationCode { ANEURALNETWORKS_ADD = 0, ANEURALNETWORKS_AVERAGE_POOL_2D = 1, ANEURALNETWORKS_CONCATENATION = 2, ANEURALNETWORKS_CONV_2D = 3, ANEURALNETWORKS_DEPTHWISE_CONV_2D = 4, ANEURALNETWORKS_DEPTH_TO_SPACE = 5, ANEURALNETWORKS_DEQUANTIZE = 6, ANEURALNETWORKS_EMBEDDING_LOOKUP = 7, ANEURALNETWORKS_FLOOR = 8, ANEURALNETWORKS_FULLY_CONNECTED = 9, ANEURALNETWORKS_HASHTABLE_LOOKUP = 10, ANEURALNETWORKS_L2_NORMALIZATION = 11, ANEURALNETWORKS_L2_POOL_2D = 12, ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION = 13, ANEURALNETWORKS_LOGISTIC = 14, ANEURALNETWORKS_LSH_PROJECTION = 15, ANEURALNETWORKS_LSTM = 16, ANEURALNETWORKS_MAX_POOL_2D = 17, ANEURALNETWORKS_MUL = 18, ANEURALNETWORKS_RELU = 19, ANEURALNETWORKS_RELU1 = 20, ANEURALNETWORKS_RELU6 = 21, ANEURALNETWORKS_RESHAPE = 22, ANEURALNETWORKS_RESIZE_BILINEAR = 23, ANEURALNETWORKS_RNN = 24, ANEURALNETWORKS_SOFTMAX = 25, ANEURALNETWORKS_SPACE_TO_DEPTH = 26, ANEURALNETWORKS_SVDF = 27, ANEURALNETWORKS_TANH = 28 }
	Operation types. More...

enum	FuseCode { ANEURALNETWORKS_FUSED_NONE = 0, ANEURALNETWORKS_FUSED_RELU = 1, ANEURALNETWORKS_FUSED_RELU1 = 2, ANEURALNETWORKS_FUSED_RELU6 = 3 }
	Fused activation function types. More...

enum	PaddingCode { ANEURALNETWORKS_PADDING_SAME = 1, ANEURALNETWORKS_PADDING_VALID = 2 }
	Implicit padding algorithms. More...

enum	PreferenceCode { ANEURALNETWORKS_PREFER_LOW_POWER = 0, ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER = 1, ANEURALNETWORKS_PREFER_SUSTAINED_SPEED = 2 }
	Execution preferences. More...

enum	ResultCode { ANEURALNETWORKS_NO_ERROR = 0, ANEURALNETWORKS_OUT_OF_MEMORY = 1, ANEURALNETWORKS_INCOMPLETE = 2, ANEURALNETWORKS_UNEXPECTED_NULL = 3, ANEURALNETWORKS_BAD_DATA = 4, ANEURALNETWORKS_OP_FAILED = 5, ANEURALNETWORKS_UNMAPPABLE = 5, ANEURALNETWORKS_BAD_STATE = 6 }
	Result codes.

enum	{ ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES = 128 }
	For ANeuralNetworksModel_setOperandValue, values with a length smaller or equal to this will be immediately copied into the model. More...

Functions
int	ANeuralNetworksMemory_createFromFd (size_t size, int protect, int fd, size_t offset, ANeuralNetworksMemory **memory)
	Creates a shared memory object from a file descriptor. More...

void	ANeuralNetworksMemory_free (ANeuralNetworksMemory *memory)
	Delete a memory object. More...

int	ANeuralNetworksModel_create (ANeuralNetworksModel **model)
	Create an empty ANeuralNetworksModel. More...

void	ANeuralNetworksModel_free (ANeuralNetworksModel *model)
	Destroy a model. More...

int	ANeuralNetworksModel_finish (ANeuralNetworksModel *model)
	Indicate that we have finished modifying a model. More...

int	ANeuralNetworksModel_addOperand (ANeuralNetworksModel model, const ANeuralNetworksOperandType type)
	Add an operand to a model. More...

int	ANeuralNetworksModel_setOperandValue (ANeuralNetworksModel model, int32_t index, const void buffer, size_t length)
	Sets an operand to a constant value. More...

int	ANeuralNetworksModel_setOperandValueFromMemory (ANeuralNetworksModel model, int32_t index, const ANeuralNetworksMemory memory, size_t offset, size_t length)
	Sets an operand to a value stored in a memory object. More...

int	ANeuralNetworksModel_addOperation (ANeuralNetworksModel model, ANeuralNetworksOperationType type, uint32_t inputCount, const uint32_t inputs, uint32_t outputCount, const uint32_t *outputs)
	Add an operation to a model. More...

int	ANeuralNetworksModel_identifyInputsAndOutputs (ANeuralNetworksModel model, uint32_t inputCount, const uint32_t inputs, uint32_t outputCount, const uint32_t *outputs)
	Specfifies which operands will be the model's inputs and outputs. More...

int	ANeuralNetworksCompilation_create (ANeuralNetworksModel model, ANeuralNetworksCompilation *compilation)
	Create a ANeuralNetworksCompilation to compile the given model. More...

void	ANeuralNetworksCompilation_free (ANeuralNetworksCompilation *compilation)
	Destroy a compilation. More...

int	ANeuralNetworksCompilation_setPreference (ANeuralNetworksCompilation *compilation, int32_t preference)
	Sets the execution preference. More...

int	ANeuralNetworksCompilation_finish (ANeuralNetworksCompilation *compilation)
	Indicate that we have finished modifying a compilation. More...

int	ANeuralNetworksExecution_create (ANeuralNetworksCompilation compilation, ANeuralNetworksExecution *execution)
	Create a ANeuralNetworksExecution to apply the given compilation. More...

void	ANeuralNetworksExecution_free (ANeuralNetworksExecution *execution)
	Destroy an execution. More...

int	ANeuralNetworksExecution_setInput (ANeuralNetworksExecution execution, int32_t index, const ANeuralNetworksOperandType type, const void *buffer, size_t length)
	Associate a user buffer with an input of the model of the ANeuralNetworksExecution. More...

Variables
int32_t	ANeuralNetworksOperandType::type
	The data type, e.g ANEURALNETWORKS_INT8. More...

uint32_t	ANeuralNetworksOperandType::dimensionCount
	The number of dimensions. More...

const uint32_t *	ANeuralNetworksOperandType::dimensions
	The dimensions of the tensor. More...

float	ANeuralNetworksOperandType::scale
	These two fields are only used for quantized tensors. More...

int32_t	ANeuralNetworksOperandType::zeroPoint

int	ANeuralNetworksExecution_setInputFromMemory (ANeuralNetworksExecution execution, int32_t index, const ANeuralNetworksOperandType type, const ANeuralNetworksMemory *memory, size_t offset, size_t length)
	Associate part of a memory object with an input of the model of the ANeuralNetworksExecution. More...

int	ANeuralNetworksExecution_setOutput (ANeuralNetworksExecution execution, int32_t index, const ANeuralNetworksOperandType type, void *buffer, size_t length)
	Associate a user buffer with an output of the model of the ANeuralNetworksExecution. More...

int	ANeuralNetworksExecution_setOutputFromMemory (ANeuralNetworksExecution execution, int32_t index, const ANeuralNetworksOperandType type, const ANeuralNetworksMemory *memory, size_t offset, size_t length)
	Associate part of a memory object with an output of the model of the ANeuralNetworksExecution. More...

int	ANeuralNetworksExecution_startCompute (ANeuralNetworksExecution execution, ANeuralNetworksEvent *event)
	Schedule evaluation of the execution. More...

int	ANeuralNetworksEvent_wait (ANeuralNetworksEvent *event)
	Waits until the execution completes. More...

void	ANeuralNetworksEvent_free (ANeuralNetworksEvent *event)
	Destroys the event. More...

Detailed Description

Typedef Documentation

typedef struct ANeuralNetworksCompilation ANeuralNetworksCompilation

ANeuralNetworksCompilation is an opaque type that can be used to compile a machine learning model.

To use:

Create a new compilation instance by calling the ANeuralNetworksCompilation_create function.
Set any desired properties on the compilation (for example, ANeuralNetworksCompilation_setPreference).
Complete the compilation with ANeuralNetworksCompilation_finish.
Use the compilation as many times as needed with ANeuralNetworksExecution_create.
Destroy the compilation with ANeuralNetworksCompilation_free once all executions using the compilation have completed.

A compilation is completed by calling ANeuralNetworksCompilation_finish. A compilation is destroyed by calling ANeuralNetworksCompilation_free.

A compilation cannot be modified once ANeuralNetworksCompilation_finish has been called on it.

It is the application's responsibility to make sure that only one thread modifies a compilation at a given time. It is however safe for more than one thread to use the compilation once ANeuralNetworksCompilation_finish has returned.

It is also the application's responsibility to ensure that there are no other uses of the compilation after calling ANeuralNetworksCompilation_free. This includes any execution object created using the compilation.

Definition at line 1334 of file NeuralNetworks.h.

typedef struct ANeuralNetworksExecution ANeuralNetworksExecution

ANeuralNetworksExecution is an opaque type that can be used to apply a machine learning model to a set of inputs.

To use:

Create a new execution instance by calling the ANeuralNetworksExecution_create function.
Associate data to the model inputs with ANeuralNetworksExecution_setInput or ANeuralNetworksExecution_setInputFromMemory.
Associate output buffers to the model outputs with ANeuralNetworksExecution_setOutput or ANeuralNetworksExecution_setOutputFromMemory.
Apply the model with ANeuralNetworksExecution_startCompute.
Wait for the execution to complete with ANeuralNetworksEvent_wait.
Destroy the execution with ANeuralNetworksExecution_free.

An execution cannot be modified once ANeuralNetworksExecution_startCompute has been called on it.

An execution can be applied to a model with ANeuralNetworksExecution_startCompute only once. Create new executions to do new evaluations of the model.

It is the application's responsibility to make sure that only one thread modifies an execution at a given time. It is however safe for more than one thread to use ANeuralNetworksEvent_wait at the same time.

It is also the application's responsibility to ensure that there are no other uses of the request after calling ANeuralNetworksExecution_free.

Definition at line 1369 of file NeuralNetworks.h.

typedef struct ANeuralNetworksMemory ANeuralNetworksMemory

ANeuralNetworksMemory is an opaque type that represents memory.

This type is used to represent shared memory, memory mapped files, and similar memories.

By using shared memory, a program can efficiently communicate to the runtime and drivers the tensors that define a model. See ANeuralNetworksModel_setOperandValueFromMemory. An application should typically create one shared memory object that contains every tensor needed to define a model. ANeuralNetworksMemory_createFromFd can be used to create shared memory from a file handle. ANeuralNetworksMemory_createShared can be used to directly created shared memory.

Memory objects can also be used to specify the input and output arguments of an execution. See ANeuralNetworksExecution_setInputFromMemory and ANeuralNetworksExecution_setOutputFromMemory.

Definition at line 1276 of file NeuralNetworks.h.

typedef struct ANeuralNetworksModel ANeuralNetworksModel

ANeuralNetworksModel is an opaque type that contains a description of the mathematical operations that constitute the model.

The model will be built by calling

A model is completed by calling ANeuralNetworksModel_finish. A model is destroyed by calling ANeuralNetworksModel_free.

A model cannot be modified once ANeuralNetworksModel_finish has been called on it.

It is the application's responsibility to make sure that only one thread modifies a model at a given time. It is however safe for more than one thread to use the model once ANeuralNetworksModel_finish has returned.

It is also the application's responsibility to ensure that there are no other uses of the model after calling ANeuralNetworksModel_free. This includes any compilation or execution object created using the model.

Definition at line 1302 of file NeuralNetworks.h.

typedef struct ANeuralNetworksOperandType ANeuralNetworksOperandType

ANeuralNetworksOperandType describes the type of an operand.

This structure is used to describe both scalars and tensors.

Enumeration Type Documentation

anonymous enum

For ANeuralNetworksModel_setOperandValue, values with a length smaller or equal to this will be immediately copied into the model.

The size is in bytes.

Definition at line 1254 of file NeuralNetworks.h.

enum FuseCode

Fused activation function types.

Enumerator
ANEURALNETWORKS_FUSED_NONE	NO fused activation function.
ANEURALNETWORKS_FUSED_RELU	Fused ReLU activation function.
ANEURALNETWORKS_FUSED_RELU1	Fused ReLU1 activation function.
ANEURALNETWORKS_FUSED_RELU6	Fused ReLU6 activation function.

Definition at line 1171 of file NeuralNetworks.h.

enum OperandCode

Operand types.

The type of operands that can be added to a model.

Although we define many types, most operators accept just a few types. Most used are ANEURALNETWORKS_TENSOR_FLOAT32, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, and ANEURALNETWORKS_INT32.

Enumerator
ANEURALNETWORKS_FLOAT32	The following entries are used to declare scalars. A 32 bit floating point scalar value.
ANEURALNETWORKS_INT32	A signed 32 bit integer scalar value.
ANEURALNETWORKS_UINT32	An unsigned 32 bit integer scalar value.
ANEURALNETWORKS_TENSOR_FLOAT32	The following entries are used to declare tensors. A tensor of 32 bit floating point values.
ANEURALNETWORKS_TENSOR_INT32	A tensor of 32 bit integer values.
ANEURALNETWORKS_TENSOR_QUANT8_ASYMM	A tensor of 8 bit integers that represent real numbers. Attached to this tensor are two numbers that can be used to convert the 8 bit integer to the real value and vice versa. These two numbers are: scale: a 32 bit non-negative floating point value. zeroPoint: an 32 bit integer, in range [0, 255]. The formula is: real_value = (integer_value - zeroPoint) * scale.

Definition at line 63 of file NeuralNetworks.h.

enum OperationCode

Operation types.

The type of operations that can be added to a model.

Enumerator
ANEURALNETWORKS_ADD	Adds two tensors, element-wise. Takes two input tensors of identical type and compatible dimensions. The output is the sum of both input tensors, optionally modified by an activation function. Two dimensions are compatible when: they are equal, or one of them is 1 The size of the output is the maximum size along each dimension of the input operands. It starts with the trailing dimensions, and works its way forward. Example: input1.dimension = {4, 1, 2} input2.dimension = {5, 4, 3, 1} output.dimension = {5, 4, 3, 2} Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: up to 4 Inputs: 0: A tensor. 1: A tensor of the same type, and compatible dimensions as input0. 2: An INT32 value, and has to be one of the FuseCode values. Specifies the activation to invoke on the result of each addition. Outputs: 0: The sum, a tensor of the same type as input0.
ANEURALNETWORKS_AVERAGE_POOL_2D	Performs a 2-D average pooling operation. The output dimensions are functions of the filter dimensions, stride, and padding. The values in the output tensor are computed as: output[batch, row, col, channel] = sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1) Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, and Channels) data layout. Both explicit padding and implicit padding are supported. Inputs (explicit padding): 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension. 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension. 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension. 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension. 5: An INT32 value, specifying the stride when walking through input in the ‘width’ dimension. 6: An INT32 value, specifying the stride when walking through input in the ‘height’ dimension. 7: An INT32 value, specifying the filter width. 8: An INT32 value, specifying the filter height. 9: An INT32 value, and has to be one of the FuseCode values. Specifies the activation to invoke on the result of each addition. Inputs (implicit padding): 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. 1: An INT32 value, specifying the implicit padding scheme, has to be one of the PaddingCode values. 2: An INT32 value, specifying the stride when walking through input in the ‘width’ dimension. 3: An INT32 value, specifying the stride when walking through input in the ‘height’ dimension. 4: An INT32 value, specifying the filter width. 5: An INT32 value, specifying the filter height. 6: An INT32 value, and has to be one of the FuseCode values. Specifies the activation to invoke on the result of each addition. Outputs: 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
ANEURALNETWORKS_CONCATENATION	Concatenates the input tensors along the given dimension. The input tensors must have identical type and the same dimensions except the dimension along the concatenation axis. Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: up to 4 Inputs: 0 ~ n-1: The list of n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm]. For inputs of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM type, all input tensors must have the same scale and zeroPoint. n: An INT32 value, specifying the concatenation axis. Outputs: 0: The output, a tensor of the same type as the input tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
ANEURALNETWORKS_CONV_2D	Performs an 2-D convolution operation. The CONV_2D op sweeps a 2-D filter that can mix channels together over a batch of images, applying the filter to each window of each image of the appropriate size. The output dimensions are functions of the filter dimensions, stride, and padding. The values in the output tensor are computed as: output[batch, row, col, channel] = sum_{i, j} ( input[batch, row + i, col + j, k] * filter[channel, row + i, col + j, k] + bias[channel] ) Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: 4, with "NHWC" data layout. Both explicit padding and implicit padding are supported. Inputs (explicit padding): 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in], specifying the filter. 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT32 type, the bias should also be of ANEURALNETWORKS_TENSOR_FLOAT32. For input tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM type, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale == input_scale * filter_scale. 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension. 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension. 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension. 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension. 7: An INT32 value, specifying the stride when walking through input in the ‘width’ dimension. 8: An INT32 value, specifying the stride when walking through input in the ‘height’ dimension. 9: An INT32 value, and has to be one of the FuseCode values. Specifies the activation to invoke on the result of each addition. Inputs (implicit padding): 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in], specifying the filter. 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT32 type, the bias should also be of ANEURALNETWORKS_TENSOR_FLOAT32. For input tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM type, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale == input_scale * filter_scale. 3: An INT32 value, specifying the implicit padding scheme, has to be one of the PaddingCode values. 4: An INT32 value, specifying the stride when walking through input in the ‘width’ dimension. 5: An INT32 value, specifying the stride when walking through input in the ‘height’ dimension. 6: An INT32 value, and has to be one of the FuseCode values. Specifies the activation to invoke on the result of each addition. Outputs: 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out]. For output tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM type, the following condition must be satisfied: output_scale > input_scale * filter_scale.
ANEURALNETWORKS_DEPTHWISE_CONV_2D	Performs a depthwise 2-D convolution operation. Given an input tensor of shape [batches, height, width, depth_in] and a filter tensor of shape [1, filter_height, filter_width, depth_out] containing depth_out convolutional filters of depth 1, DEPTHWISE_CONV applies a different filter to each input channel (expanding from 1 channel to channel_multiplier channels for each), then concatenates the results together. The output has depth_out = depth_in * depth_multiplier channels. The output dimensions are functions of the filter dimensions, stride, and padding. The values in the output tensor are computed as: output[b, i, j, k * channel_multiplier + q] = sum_{di, dj} ( input[b, strides[1] * i + di, strides[2] * j + dj, k] * filter[1, di, dj, k * channel_multiplier + q] ) Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: 4, with "NHWC" data layout. Both explicit padding and implicit padding are supported. Inputs (explicit padding): 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], specifying the filter. 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT32 type, the bias should also be of ANEURALNETWORKS_TENSOR_FLOAT32. For input tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM type, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale == input_scale * filter_scale. 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension. 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension. 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension. 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension. 7: An INT32 value, specifying the stride when walking through input in the ‘width’ dimension. 8: An INT32 value, specifying the stride when walking through input in the ‘height’ dimension. 9: An INT32 value, specifying the depthwise multiplier. 10: An INT32 value, and has to be one of the FuseCode values. Specifies the activation to invoke on the result of each addition. Inputs (explicit padding): 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], specifying the filter. 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT32 type, the bias should also be of ANEURALNETWORKS_TENSOR_FLOAT32. For input tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM type, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale == input_scale * filter_scale. 3: An INT32 value, specifying the implicit padding scheme, has to be one of the PaddingCode values. 4: An INT32 value, specifying the stride when walking through input in the ‘width’ dimension. 5: An INT32 value, specifying the stride when walking through input in the ‘height’ dimension. 6: An INT32 value, specifying the depthwise multiplier. 7: An INT32 value, and has to be one of the FuseCode values. Specifies the activation to invoke on the result of each addition. Outputs: 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out]. For output tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM type, the following condition must be satisfied: output_scale > input_scale * filter_scale.
ANEURALNETWORKS_DEPTH_TO_SPACE	Rearranges data from depth into blocks of spatial data. More specifically, this op outputs a copy of the input tensor where values from the depth dimension are moved in spatial blocks to the height and width dimensions. The value block_size indicates the input block size and how the data is moved. Chunks of data of size block_size * block_size from depth are rearranged into non-overlapping blocks of size block_size x block_size. The width of the output tensor is input_depth * block_size, whereas the height is input_height * block_size. The depth of the input tensor must be divisible by block_size * block_size Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: 4, with "NHWC" data layout. Inputs: 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. 1: An INT32 value, specifying the block_size. block_size must be >=1 and block_size * block_size must be a divisor of the input depth. Outputs: 0: The output 4-D tensor, of shape [batch, heightblock_size, widthblock_size, depth/(block_size*block_size)].
ANEURALNETWORKS_DEQUANTIZE	Dequantizes the input tensor. The formula is: output = (input - zeroPoint) * scale. Supported tensor types: ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: up to 4 Inputs: 0: A tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM. Outputs: 0: The output tensor of same shape as input0, but with type ANEURALNETWORKS_TENSOR_FLOAT32.
ANEURALNETWORKS_EMBEDDING_LOOKUP	Looks up sub-tensors in the input tensor. This operator takes for input a tensor of values (Values) and a one-dimensional tensor of selection indices (Lookups). The output tensor is the concatenation of sub-tensors of Values as selected by Lookups. Think of Values as being sliced along its first dimension: The entries in Lookups select which slices are concatenated together to create the output tensor. For example, if Values has shape of [40, 200, 300] and Lookups has shape of [3], we would expect all three values found in Lookups to be between 0 and 39. The resulting tensor will have shape of [3, 200, 300]. If a value in Lookups is out of bounds, the operation will fail and an error will be reported. Inputs: 0: Lookups. A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32 type. The values are indices into the first dimension of Values. 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are extracted. Output: 0: A n-D tensor with the same rank and shape as the Values tensor, except for the first dimension which has the same size as Lookups' only dimension.
ANEURALNETWORKS_FLOOR	Computes element-wise floor() on the input tensor. Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 Supported tensor rank: up to 4 Inputs: 0: A tensor. Outputs: 0: The output tensor, of the same type and dimensions as the input tensor.
ANEURALNETWORKS_FULLY_CONNECTED	Denotes a fully (densely) connected layer, which connects all elements in the input tensor with each element in the output tensor. This layer implements the operation: outputs = activation(inputs * weights’ + bias) Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: up to 4. Inputs: 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape [batch_size, input_size], where “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. 1: A 2-D tensor, specifying the weights, of shape [num_units, input_size], where "num_units" corresponds to the number of output nodes. 2: A 1-D tensor, of shape [num_units], specifying the bias. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT32 type, the bias should also be of ANEURALNETWORKS_TENSOR_FLOAT32. For input tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM type, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale == input_scale * filter_scale. 3: An INT32 value, and has to be one of the FuseCode values. Specifies the activation to invoke on the result of each addition. Outputs: 0: The output tensor, of shape [batch_size, num_units]. For output tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM type, the following condition must be satisfied: output_scale > input_scale * filter_scale.
ANEURALNETWORKS_HASHTABLE_LOOKUP	Looks up sub-tensors in the input tensor using a key-value map. This operator takes for input a tensor of values (Values), a one-dimensional tensor of selection values (Lookups) and a one-dimensional tensor that maps these values to Values indexes. The output tensor is the concatenation of sub-tensors of Values as selected by Lookups via Keys. Think of Values as being sliced along its outer-most dimension. The output is a concatenation of selected slices, with one slice for each entry of Lookups. The slice selected is the one at the same index as the Maps entry that matches the value in Lookups. For a hit, the corresponding sub-tensor of Values is included in the Output tensor. For a miss, the corresponding sub-tensor in Output will have zero values. For example, if Values has shape of [40, 200, 300], Keys should have a shape of [40]. If Lookups tensor has shape of [3], we're concatenating three slices, so the resulting tensor will have the shape of [3, 200, 300]. If the first entry in Lookups has the value 123456, we'll look for that value in Keys tensor. If the sixth entry of Keys contains 123456, we'll select the sixth slice of Values. If no entry in Keys has 123456, a slice of zeroes will be concatenated. Inputs: 0: Lookups. A 1-D ANEURALNETWORKS_TENSOR_INT32 tensor with shape [ k ]. 1: Keys. A 1-D ANEURALNETWORKS_TENSOR_INT32 tensor with shape [ n ]; Keys and Values pair represent a map, i.e., the ith element in Keys (Keys[i]) is the key to select the ith sub-tensor in Values (Values[i]), where 0 <= i <= n-1. Keys tensor MUST be sorted in ascending order. 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension must be n. Outputs: 0: Output. A tensor with shape [ k …]. 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup hits (True) or not (False). Stored as ANEURALNETWORKS_TENSOR_QUANT8_ASYMM with offset 0 and scale 1.0f. A non-zero byte represents True, a hit. A zero indicates otherwise.
ANEURALNETWORKS_L2_NORMALIZATION	Applies L2 normalization along the depth dimension. The values in the output tensor are computed as: output[batch, row, col, channel] = input[batch, row, col, channel] / sqrt(sum_{c} pow(input[batch, row, col, c], 2)) For input tensor with more dimensions, independently normalizes each 1-D slice along dimension dim. Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, Height, Width, and Channels). Inputs: 0: A 4-D tensor, of shape [batches, height, width, depth]. Outputs: 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
ANEURALNETWORKS_L2_POOL_2D	Performs an 2-D L2 pooling operation. The output dimensions are functions of the filter dimensions, stride, and padding. The values in the output tensor are computed as: output[batch, row, col, channel] = sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / sum(1)) Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 Supported tensor rank: 4, with "NHWC" data layout. Both explicit padding and implicit padding are supported. Inputs (explicit padding): 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension. 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension. 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension. 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension. 5: An INT32 value, specifying the stride when walking through input in the ‘width’ dimension. 6: An INT32 value, specifying the stride when walking through input in the ‘height’ dimension. 7: An INT32 value, specifying the filter width. 8: An INT32 value, specifying the filter height. 9: An INT32 value, and has to be one of the FuseCode values. Specifies the activation to invoke on the result of each addition. Inputs (implicit padding): 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. 1: An INT32 value, specifying the implicit padding scheme, has to be one of the PaddingCode values. 2: An INT32 value, specifying the stride when walking through input in the ‘width’ dimension. 3: An INT32 value, specifying the stride when walking through input in the ‘height’ dimension. 4: An INT32 value, specifying the filter width. 5: An INT32 value, specifying the filter height. 6: An INT32 value, and has to be one of the FuseCode values. Specifies the activation to invoke on the result of each addition. Outputs: 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION	Applies Local Response Normalization along the depth dimension. The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the last dimension), and each vector is normalized independently. Within a given vector, each component is divided by the weighted, squared sum of inputs within depth_radius. The output is calculated using this formula: sqr_sum[a, b, c, d] = sum(pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2) output = input / pow((bias + alpha * sqr_sum), beta) Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 Supported tensor rank: 4, with "NHWC" data layout. Inputs: 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. 1: An INT32 value, specifying the radius of the normalization window. 2: A FLOAT32 value, specifying the bias, must not be zero. 3: A FLOAT32 value, specifying the scale factor, alpha. 4: A FLOAT32 value, specifying the exponent, beta. Outputs: 0: The output tensor of same shape as input0.
ANEURALNETWORKS_LOGISTIC	Computes sigmoid activation on the input tensor element-wise. The output is calculated using this formula: output = 1 / (1 + exp(-input)) Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: up to 4. Inputs: 0: A tensor, specifying the input. Outputs: 0: The output tensor of same shape as input0. For ANEURALNETWORKS_TENSOR_QUANT8_ASYMM type, the scale must be 1.f / 256 and the zeroPoint must be 0.
ANEURALNETWORKS_LSH_PROJECTION	Projects an input to a bit vector via locality senstive hashing. Inputs: 0: Hash functions. Dim.size == 2, DataType: Float. Tensor[0].Dim[0]: Number of hash functions. Tensor[0].Dim[1]: Number of seeds per hash functions. Tensor[0].Dim[1] <= 32 in sparse case. 1: Input. Dim.size >= 1, no restriction on DataType. 2: Weight. Optional. Dim.size == 1, DataType: Float. If not set, each input element is considered to have the same weight of 1.0. Tensor[1].Dim[0] == Tensor[2].Dim[0] 3: Type: Sparse: Value LSHProjectionType_SPARSE(=1). Computed bit vector is considered to be sparse. Each output element is an int32 made up of multiple bits computed from hash functions. Dense: Value LSHProjectionType_DENSE(=2). Computed bit vector is considered to be dense. Each output element represents a bit and can take the value of either 0 or 1. Outputs: 0: If the projection type is sparse: Output.Dim == { Tensor[0].Dim[0] } A tensor of int32 that represents hash signatures. If the projection type is Dense: Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] } A flattened tensor that represents projected bit vectors.
ANEURALNETWORKS_LSTM	Long short-term memory unit (LSTM) recurrent network layer. The default non-peephole implementation is based on: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997. The peephole implementation is based on: https://research.google.com/pubs/archive/43905.pdf Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory recurrent neural network architectures for large scale acoustic modeling." INTERSPEECH, 2014. The coupling of input and forget gate (CIFG) is based on: http://arxiv.org/pdf/1503.04069.pdf Greff et al. "LSTM: A Search Space Odyssey" The class has the following independently optional inputs: If input gate (if CIFG): “input_to_forget_weights”, “recurrent_to_input_weights”, “cell_to_input_weights”, “input_gate_bias”. If no peephole connections: “cell_to_input_weights”, “cell_to_forget_weights”, “cell_to_output_weights”. If no projection layer: “projection_weights” and “projection_bias”. If no projection bias: “projection_bias”. Supported tensor types (type T): ANEURALNETWORKS_TENSOR_FLOAT32 Inputs: 0: Input. A 2-D tensor of type T, of shape [batch_size, input_size], where “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. 1: input_to_input_weights. A 2-D tensor of type T, of shape [num_units, input_size], where “num_units” corresponds to the number of cell units. 2: input_to_forget_weights. A 2-D tensor of type T, of shape [num_units, input_size]. 3: input_to_cell_weights. A 2-D tensor of type T, of shape [num_units, input_size]. 4: input_to_output_weights. A 2-D tensor of type T, of shape [num_units, input_size]. 5: recurrent_to_input_weights. A 2-D tensor of type T, of shape [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., “num_units”), or the second dimension of the “projection_weights”, if defined. 6: recurrent_to_forget_weights. A 2-D tensor of type T, of shape [num_units, output_size]. 7: recurrent_to_cell_weights. A 2-D tensor of type T, of shape [num_units, output_size]. 8: recurrent_to_output_weights. A 2-D tensor of type T, of shape [num_units, output_size]. 9: cell_to_input_weights. A 1-D tensor of type T, of shape [num_units]. 10:cell_to_forget_weights. A 1-D tensor of type T, of shape [num_units]. 11:cell_to_output_weights. A 1-D tensor of type T, of shape [num_units]. 12:input_gate_bias. A 1-D tensor of type T, of shape [num_units]. 13:forget_gate_bias. A 1-D tensor of type T, of shape [num_units]. 14:cell_bias. A 1-D tensor of type T, of shape [num_units]. 15:output_gate_bias. A 1-D tensor of type T, of shape [num_units]. 16:projection_weights. A 2-D tensor of type T, of shape [output_size, num_units]. 17:projection_bias. A 1-D tensor of type T, of shape [output_size]. 18: output_state (in). A 2-D tensor of type T, of shape [batch_size, output_size]. 19: cell_state (in). A 2-D tensor of type T, of shape [batch_size, num_units]. 20:fused_activation_function. An optional FuseCode value indicating the activation function. If “NONE” is specified then it results in a linear activation. 21:cell_clip. A clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip]. If set to 0.0 then clipping is disabled. 22:proj_clip. A clipping threshold for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. Outputs: 0: scratch_buffer. A 3-D tensor of type T, of shape [batch_size, num_cell, 4]. 1: output_state (out). A 2-D tensor of type T, of shape [batch_size, output_size]. 2: cell_state (out). A 2-D tensor of type T, of shape [batch_size, num_units]. 3: output. A 2-D tensor of type T, of shape [batch_size, output_size]. This is effectively the same as the current “output_state” value.
ANEURALNETWORKS_MAX_POOL_2D	Performs an 2-D max pooling operation. The output dimensions are functions of the filter dimensions, stride, and padding. The values in the output tensor are computed as: output[batch, row, col, channel] = max_{i, j} (input[batch, row + i, col + j, channel]) Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: 4, with "NHWC" data layout. Both explicit padding and implicit padding are supported. Inputs (explicit padding): 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension. 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension. 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension. 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension. 5: An INT32 value, specifying the stride when walking through input in the ‘width’ dimension. 6: An INT32 value, specifying the stride when walking through input in the ‘height’ dimension. 7: An INT32 value, specifying the filter width. 8: An INT32 value, specifying the filter height. 9: An INT32 value, and has to be one of the FuseCode values. Specifies the activation to invoke on the result of each addition. Inputs (implicit padding): 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. 1: An INT32 value, specifying the implicit padding scheme, has to be one of the PaddingCode values. 2: An INT32 value, specifying the stride when walking through input in the ‘width’ dimension. 3: An INT32 value, specifying the stride when walking through input in the ‘height’ dimension. 4: An INT32 value, specifying the filter width. 5: An INT32 value, specifying the filter height. 6: An INT32 value, and has to be one of the FuseCode values. Specifies the activation to invoke on the result of each addition. Outputs: 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
ANEURALNETWORKS_MUL	Multiplies two tensors, element-wise. Takes two input tensors of identical type and compatible dimensions. The output is the product of both input tensors, optionally modified by an activation function. Two dimensions are compatible when: they are equal, or one of them is 1 The size of the resulting output is the maximum size along each dimension of the input operands. It starts with the trailing dimensions, and works its way forward. Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: up to 4 Inputs: 0: A tensor. 1: A tensor of the same type, and compatible dimensions as input0. 2: An INT32 value, and has to be one of the FuseCode values. Specifies the activation to invoke on the result of each addition. Outputs: 0: The product, a tensor of the same type as input0. For output tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM type, the following condition must be satisfied: output_scale > input1_scale * input2_scale.
ANEURALNETWORKS_RELU	Computes rectified linear activation on the input tensor element-wise. The output is calculated using this formula: output = max(0, input) Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: up to 4. Inputs: 0: A tensor, specifying the input. Outputs: 0: The output tensor of same shape as input0.
ANEURALNETWORKS_RELU1	Computes rectified linear 1 activation on the input tensor element-wise. The output is calculated using this formula: output = min(1.f, max(-1.f, input)) Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: up to 4. Inputs: 0: A tensor, specifying the input. Outputs: 0: The output tensor of same shape as input0.
ANEURALNETWORKS_RELU6	Computes rectified linear 6 activation on the input tensor element-wise. The output is calculated using this formula: output = min(6, max(0, input)) Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: up to 4. Inputs: 0: A tensor, specifying the input. Outputs: 0: The output tensor of same shape as input0.
ANEURALNETWORKS_RESHAPE	Reshapes a tensor. Given tensor, this operation returns a tensor that has the same values as tensor, but with a newly specified shape. Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: up to 4. Inputs: 0: A tensor, specifying the tensor to be reshaped. 1: A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32, defining the shape of the output tensor. The number of elements implied by shape must be the same as the number of elements in the input tensor. Outputs: 0: The output tensor, of shape specified by the input shape.
ANEURALNETWORKS_RESIZE_BILINEAR	Resizes images to given size using the bilinear interpretation. Resized images will be distorted if their output aspect ratio is not the same as input aspect ratio. Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 Supported tensor rank: 4, with "NHWC" data layout. Inputs: 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. 1: An INT32 value, specifying the output height of the output tensor. 2: An INT32 value, specifying the output width of the output tensor. Outputs: 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
ANEURALNETWORKS_RNN	A basic recurrent neural network layer. This layer implements the operation: outputs = state = activation(inputs * input_weights + state * recurrent_weights + bias) Where: “input_weights” is a weight matrix that multiplies the inputs; “recurrent_weights” is a weight matrix that multiplies the current “state” which itself is the output from the previous time step computation; “bias” is a bias vector (added to each output vector in the batch); “activation” is the function passed as the “fused_activation_function” argument (if not “NONE”). Supported tensor types (Type T): ANEURALNETWORKS_TENSOR_FLOAT32 Inputs: 0: input. A 2-D tensor of type T, of shape [batch_size, input_size], where “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. 1: weights. A 2-D tensor of type T, of shape [num_units, input_size], where “num_units” corresponds to the number of units. 2: recurrent_weights. A 2-D tensor of type T, of shape [num_units, num_units], with columns corresponding to the weights from each unit. 3: bias. A 1-D tensor of type T, of shape [num_units]. 4: hidden state (in). A 2-D tensor of type T, of shape [batch_size, num_units]. 5: fused_activation_function. An optional FuseCode value indicating the activation function. If “NONE” is specified then it results in a linear activation. Outputs: 0: hidden state (out). A 2-D tensor of type T, of shape [batch_size, num_units]. 1: output. A 2-D tensor of type T, of shape [batch_size, num_units]. This is effectively the same as the current state value.
ANEURALNETWORKS_SOFTMAX	Computes the softmax activation on the input tensor element-wise, per batch, by normalizing the input vector so the maximum coefficient is zero. The output is calculated using this formula: output[batch, i] = exp((input[batch, i] - max(input[batch, :])) * beta) / sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)} Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: 2 or 4. Inputs: 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped. 1: A FLOAT32 value, specifying the positive scaling factor for the exponent, beta. Outputs: 0: The output tensor of same shape as input0. For ANEURALNETWORKS_TENSOR_QUANT8_ASYMM type, the scale must be 1.f / 256 and the zeroPoint must be 0.
ANEURALNETWORKS_SPACE_TO_DEPTH	Rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension. The value block_size indicates the input block size and how the data is moved. Chunks of data of size block_size * block_size from depth are rearranged into non-overlapping blocks of size block_size x block_size. The depth of the output tensor is input_depth * block_size * block_size. The input tensor's height and width must be divisible by block_size. Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM Supported tensor rank: 4, with "NHWC" data layout. Inputs: 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. 1: An INT32 value, specifying the block_size. block_size must be >=1 and block_size must be a divisor of both the input height and width. Outputs: 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size, depthblock_sizeblock_size].
ANEURALNETWORKS_SVDF	SVDF op is a kind of stateful layer derived from the notion that a densely connected layer that's processing a sequence of input frames can be approximated by using a singular value decomposition of each of its nodes. The implementation is based on: https://research.google.com/pubs/archive/43813.pdf P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada. “Compressing Deep Neural Networks using a Rank-Constrained Topology”. INTERSPEECH, 2015. It processes the incoming input using a 2-stage filtering mechanism: stage 1 performs filtering on the "features" dimension, whose outputs get pushed into a memory of fixed-size memory_size. stage 2 performs filtering on the "time" dimension of the memory_size memoized outputs of stage 1. Specifically, for rank 1, this layer implements the operation: memory = push(conv1d(inputs, weights_feature, feature_dim, "ANEURALNETWORKS_PADDING_VALID")); outputs = activation(memory * weights_time + bias); Where: “weights_feature” is a weights matrix that processes the inputs (by convolving the input with every “feature filter”), and whose outputs get pushed, stacked in order, into the fixed-size “memory” (the oldest entry gets dropped); “weights_time” is a weights matrix that processes the “memory” (by a batched matrix multiplication on the num_units); “bias” is an optional bias vector (added to each output vector in the batch); and “activation” is the function passed as the “fused_activation_function” argument (if not “NONE”). Each rank adds a dimension to the weights matrices by means of stacking the filters. Supported tensor types (type T): ANEURALNETWORKS_TENSOR_FLOAT32 Inputs: 0: input. A 2-D tensor of type T, of shape [batch_size, input_size], where “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. 1: weights_feature. A 2-D tensor of type T, of shape [num_units, input_size], where “num_units” corresponds to the number of units. 2: weights_time. A 2-D tensor of type T, of shape [num_units, memory_size], where “memory_size” corresponds to the fixed-size of the memory. 3: bias. An optional 1-D tensor of type T, of shape [num_units]. 4: state (in). A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank]. 5: rank. The rank of the SVD approximation. 6: fused_activation_function. An optional FuseCode value indicating the activation function. If “NONE” is specified then it results in a linear activation. Outputs: 0: state (out). A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank]. 1: output. A 2-D tensor of type T, of shape [batch_size, num_units].
ANEURALNETWORKS_TANH	Computes hyperbolic tangent of input tensor element-wise. The output is calculated using this formula: output = tanh(input) Supported tensor types: ANEURALNETWORKS_TENSOR_FLOAT32 Supported tensor rank: up to 4. Inputs: 0: A tensor, specifying the input. Outputs: 0: The output tensor of same shape as input0.

Definition at line 97 of file NeuralNetworks.h.

enum PaddingCode

Implicit padding algorithms.

Enumerator

ANEURALNETWORKS_PADDING_SAME

SAME padding.

Padding on both ends are the "same": padding_to_beginning = total_padding / 2 padding_to_end = (total_padding + 1)/2. i.e., for even number of padding, padding to both ends are exactly the same; for odd number of padding, padding to the ending is bigger than the padding to the beginning by 1.

total_padding is a function of input, stride and filter size. It could be computed as follows: out_size = (input + stride - 1) / stride; needed_input = (out_size - 1) * stride + filter_size total_padding = max(0, needed_input - output_size) The computation is the same for the horizontal and vertical directions.

ANEURALNETWORKS_PADDING_VALID

VALID padding.

No padding. When the input size is not evenly divisible by the filter size, the input at the end that could not fill the whole filter tile will simply be ignored.

Definition at line 1186 of file NeuralNetworks.h.

enum PreferenceCode

Execution preferences.

Enumerator

ANEURALNETWORKS_PREFER_LOW_POWER

Prefer executing in a way that minimizes battery drain.

This is desirable for compilations that will be executed often.

ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER

Prefer returning a single answer as fast as possible, even if this causes more power consumption.

ANEURALNETWORKS_PREFER_SUSTAINED_SPEED

Prefer maximizing the throughput of successive frames, for example when processing successive frames coming from the camera.

Definition at line 1217 of file NeuralNetworks.h.

Function Documentation

int ANeuralNetworksCompilation_create	(	ANeuralNetworksModel *	model,
		ANeuralNetworksCompilation **	compilation
	)

Create a ANeuralNetworksCompilation to compile the given model.

This only creates the object. Compilation is only performed once ANeuralNetworksCompilation_finish is invoked.

ANeuralNetworksCompilation_finish should be called once all desired properties have been set on the compilation.

ANeuralNetworksModel_free should be called once the compilation is no longer needed.

The provided model must outlive the compilation.

The model must already have been finished by a call to ANeuralNetworksModel_finish.

See ANeuralNetworksCompilation for information on multithreaded usage.

Parameters

model	The ANeuralNetworksModel to be compiled.
compilation	The newly created object or NULL if unsuccessful.

Returns: ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the model is invalid.

int ANeuralNetworksCompilation_finish ( ANeuralNetworksCompilation * compilation )

Indicate that we have finished modifying a compilation.

Required before calling ANeuralNetworksExecution_create.

An application is responsible to make sure that no other thread uses the compilation at the same time.

This function must only be called once for a given compilation.

See ANeuralNetworksCompilation for information on multithreaded usage.

Parameters

compilation The compilation to be finished.

Returns: ANEURALNETWORKS_NO_ERROR if successful.

void ANeuralNetworksCompilation_free ( ANeuralNetworksCompilation * compilation )

Destroy a compilation.

The compilation need not have been finished by a call to ANeuralNetworksModel_finish.

See ANeuralNetworksCompilation for information on multithreaded usage.

Parameters

compilation The compilation to be destroyed. Passing NULL is acceptable and results in no operation.

int ANeuralNetworksCompilation_setPreference	(	ANeuralNetworksCompilation *	compilation,
		int32_t	preference
	)

Sets the execution preference.

Provides guidance to the runtime when trade-offs are possible.

See ANeuralNetworksCompilation for information on multithreaded usage.

Parameters

compilation	The compilation to be modified.
preference	Either PREFER_LOW_POWER, PREFER_SINGLE_FAST_ANSWER, or PREFER_SUSTAINED_SPEED.

Returns: ANEURALNETWORKS_NO_ERROR if successful.

void ANeuralNetworksEvent_free ( ANeuralNetworksEvent * event )

Destroys the event.

See ANeuralNetworksExecution for information on multithreaded usage.

int ANeuralNetworksEvent_wait ( ANeuralNetworksEvent * event )

Waits until the execution completes.

More than one thread can wait on an event. When the execution completes, all threads will be released.

See ANeuralNetworksExecution for information on multithreaded usage.

Returns: ANEURALNETWORKS_NO_ERROR if the execution completed normally.

int ANeuralNetworksExecution_create	(	ANeuralNetworksCompilation *	compilation,
		ANeuralNetworksExecution **	execution
	)

Create a ANeuralNetworksExecution to apply the given compilation.

This only creates the object. Computation is only performed once ANeuralNetworksExecution_startCompute is invoked.

The provided compilation must outlive the execution.

See ANeuralNetworksExecution for information on multithreaded usage.

Parameters

compilation	The ANeuralNetworksCompilation to be evaluated.
execution	The newly created object or NULL if unsuccessful.

Returns: ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the compilation is invalid.

void ANeuralNetworksExecution_free ( ANeuralNetworksExecution * execution )

Destroy an execution.

If called on an execution for which ANeuralNetworksExecution_startCompute has been called, the function will return immediately but will mark the execution to be deleted once the computation completes. The related ANeuralNetworksEvent will be signaled and the ANeuralNetworksEvent_wait will return ANEURALNETWORKS_ERROR_DELETED.

See ANeuralNetworksExecution for information on multithreaded usage.

Parameters

execution The execution to be destroyed. Passing NULL is acceptable and results in no operation.

int ANeuralNetworksExecution_setInput	(	ANeuralNetworksExecution *	execution,
		int32_t	index,
		const ANeuralNetworksOperandType *	type,
		const void *	buffer,
		size_t	length
	)

Associate a user buffer with an input of the model of the ANeuralNetworksExecution.

The provided buffer must outlive the execution.

If the input is optional, you can indicate that it is omitted by passing nullptr for buffer and 0 for length.

See ANeuralNetworksExecution for information on multithreaded usage.

Parameters

execution	The execution to be modified.
index	The index of the input argument we are setting. It is an index into the lists passed to ANeuralNetworksModel_identifyInputsAndOutputs. It is not the index associated with ANeuralNetworksModel_addOperand.
type	The type of the operand. This should be used to specify the dimensions that were set to 0 when the operand was added to the model. All other properties of the type must be the same as specified in the model. If the type is the same as specified when the model was built, NULL can be passed.
buffer	The buffer containing the data.
length	The length in bytes of the buffer.

Returns: ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the name is not recognized or the buffer is too small for the input.

int ANeuralNetworksExecution_setInputFromMemory	(	ANeuralNetworksExecution *	execution,
		int32_t	index,
		const ANeuralNetworksOperandType *	type,
		const ANeuralNetworksMemory *	memory,
		size_t	offset,
		size_t	length
	)

Associate part of a memory object with an input of the model of the ANeuralNetworksExecution.

The provided memory must outlive the execution.

If the input is optional, you can indicate that it is omitted by usingLink ANeuralNetworks_setInput} instead, passing nullptr for buffer and 0 for length.

See ANeuralNetworksExecution for information on multithreaded usage.

Parameters

execution	The execution to be modified.
index	The index of the input argument we are setting. It is an index into the lists passed to ANeuralNetworksModel_identifyInputsAndOutputs. It is not the index associated with ANeuralNetworksModel_addOperand.
type	The type of the operand. This can be used to specify the dimensions that were set to 0 when the operand was added to the model. All other values must be the same as specified in the model. If the type is the same as specified when the model was built, NULL can be passed.
memory	The memory containing the data.
offset	This specifies the location of the data whithin the memory. The offset is in bytes from the start of memory.
length	The size in bytes of the data value.

Returns: ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the name is not recognized or the buffer is too small for the input.

int ANeuralNetworksExecution_setOutput	(	ANeuralNetworksExecution *	execution,
		int32_t	index,
		const ANeuralNetworksOperandType *	type,
		void *	buffer,
		size_t	length
	)

Associate a user buffer with an output of the model of the ANeuralNetworksExecution.

If the output is optional, you can indicate that it is omitted by passing nullptr for buffer and 0 for length.

The provided buffer must outlive the execution.

See ANeuralNetworksExecution for information on multithreaded usage.

Parameters

execution	The execution to be modified.
index	The index of the output argument we are setting. It is an index into the lists passed to ANeuralNetworksModel_identifyInputsAndOutputs. It is not the index associated with ANeuralNetworksModel_addOperand.
type	The type of the operand. This can be used to specify the dimensions that were set to 0 when the operand was added to the model. All other values must be the same as specified in the model. If the type is the same as specified when the model was built, NULL can be passed.
buffer	The buffer where the data is to be written.
length	The length in bytes of the buffer.

Returns: ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the name is not recognized or the buffer is too small for the output.

int ANeuralNetworksExecution_setOutputFromMemory	(	ANeuralNetworksExecution *	execution,
		int32_t	index,
		const ANeuralNetworksOperandType *	type,
		const ANeuralNetworksMemory *	memory,
		size_t	offset,
		size_t	length
	)

Associate part of a memory object with an output of the model of the ANeuralNetworksExecution.

If the output is optional, you can indicate that it is omitted by usingLink ANeuralNetworks_setOutput} instead, passing nullptr for buffer and 0 for length.

The provided memory must outlive the execution.

See ANeuralNetworksExecution for information on multithreaded usage.

Parameters

execution	The execution to be modified.
index	The index of the output argument we are setting. It is an index into the lists passed to ANeuralNetworksModel_identifyInputsAndOutputs. It is not the index associated with ANeuralNetworksModel_addOperand.
type	The type of the operand. This can be used to specify the dimensions that were set to 0 when the operand was added to the model. All other values must be the same as specified in the model. If the type is the same as specified when the model was built, NULL can be passed.
memory	The memory where the data is to be stored.
offset	This specifies the location of the data whithin the memory. The offset is in bytes from the start of memory.
length	The length in bytes of the data value.

Returns: ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the name is not recognized or the buffer is too small for the output.

int ANeuralNetworksExecution_startCompute	(	ANeuralNetworksExecution *	execution,
		ANeuralNetworksEvent **	event
	)

Schedule evaluation of the execution.

Schedules evaluation of the execution. Once the model has been applied and the outputs are ready to be consumed, the returned event will be signaled. Use ANeuralNetworksEvent_wait to wait for that event.

Multiple executions can be scheduled and evaluated concurrently. The runtime makes no guarantee on the ordering of completion of executions. If it's important to the application, the application should enforce the ordering by using ANeuralNetworksEvent_wait.

ANeuralNetworksEvent_wait must be called to recuperate the resources used by the execution.

See ANeuralNetworksExecution for information on multithreaded usage.

Parameters

execution	The execution to be scheduled and executed.
event	The event that will be signaled on completion. event is set to NULL if there's an error.

Returns: ANEURALNETWORKS_NO_ERROR if successful.

int ANeuralNetworksMemory_createFromFd	(	size_t	size,
		int	protect,
		int	fd,
		size_t	offset,
		ANeuralNetworksMemory **	memory
	)

Creates a shared memory object from a file descriptor.

The shared memory is backed by a file descriptor via mmap. See ANeuralNetworksMemory for a description on how to use this shared memory.

Parameters

size	The requested size in bytes. Must not be larger than the file size.
prot	The desired memory protection for the mapping. It is either PROT_NONE or the bitwise OR of one or more of the following flags: PROT_READ, PROT_WRITE.
fd	The requested file descriptor. The file descriptor has to be mmap-able. The file descriptor will be duplicated.
offset	The offset to the beginning of the file of the area to map. The offset has to be aligned to a page size.
memory	The memory object to be created. Set to NULL if unsuccessful.

Returns: ANEURALNETWORKS_NO_ERROR if the request completed normally.

void ANeuralNetworksMemory_free ( ANeuralNetworksMemory * memory )

Delete a memory object.

Destroys the object used by the run time to keep track of the memory. This will free the underlying actual memory if no other code has open handles to this memory.

Parameters

memory The memory object to be freed.

int ANeuralNetworksModel_addOperand	(	ANeuralNetworksModel *	model,
		const ANeuralNetworksOperandType *	type
	)

Add an operand to a model.

The order in which the operands are added is important. The first one added to a model will have the index value 0, the second 1, etc. These indexes are used as operand identifiers in ANeuralNetworksModel_addOperation, ANeuralNetworksExecution_setInput, ANeuralNetworksExecution_setInputFromMemory, ANeuralNetworksExecution_setOutput, ANeuralNetworksExecution_setOutputFromMemory and ANeuralNetworksExecution_setOperandValue.

To build a model that can accomodate inputs of various sizes, as you may want to do for a CNN, set the size of the dimensions that will vary at run time to 0. If you do so, provide the full dimensions when calling ANeuralNetworksExecution_setInput or ANeuralNetworksExecution_setInputFromMemory.

Attempting to modify a model once ANeuralNetworksModel_finish has been called will return an error.

See ANeuralNetworksModel for information on multithreaded usage.

Parameters

model	The model to be modified.
type	The ANeuralNetworksOperandType that describes the shape of the operand.

Returns: ANEURALNETWORKS_NO_ERROR if successful.

int ANeuralNetworksModel_addOperation	(	ANeuralNetworksModel *	model,
		ANeuralNetworksOperationType	type,
		uint32_t	inputCount,
		const uint32_t *	inputs,
		uint32_t	outputCount,
		const uint32_t *	outputs
	)

Add an operation to a model.

Parameters

model	The model to be modified.
type	The type of the operation.
inputCount	The number of entries in the inputs array.
inputs	An array of indexes identifying each operand.
outputCount	The number of entries in the outputs array.
outputs	An array of indexes identifying each operand.

The operands specified by inputs and outputs must have been previously added by calls to ANeuralNetworksModel_addOperand.

Attempting to modify a model once ANeuralNetworksModel_finish has been called will return an error.

See ANeuralNetworksModel for information on multithreaded usage.

Returns: ANEURALNETWORKS_NO_ERROR if successful.

int ANeuralNetworksModel_create ( ANeuralNetworksModel ** model )

Create an empty ANeuralNetworksModel.

This only creates the object. Computation is performed once ANeuralNetworksExecution_startCompute is invoked.

The model should be constructed with calls to ANeuralNetworksModel_addOperation and ANeuralNetworksModel_addOperand

ANeuralNetworksModel_finish should be called once the model has been fully constructed.

ANeuralNetworksModel_free should be called once the model is no longer needed.

Parameters

model The ANeuralNetworksModel to be created. Set to NULL if unsuccessful.

Returns: ANEURALNETWORKS_NO_ERROR if successful.

int ANeuralNetworksModel_finish ( ANeuralNetworksModel * model )

Indicate that we have finished modifying a model.

Required before calling ANeuralNetworksCompilation_create.

An application is responsible to make sure that no other thread uses the model at the same time.

This function must only be called once for a given model.

See ANeuralNetworksModel for information on multithreaded usage.

Parameters

model The model to be finished.

Returns: ANEURALNETWORKS_NO_ERROR if successful.

void ANeuralNetworksModel_free ( ANeuralNetworksModel * model )

Destroy a model.

The model need not have been finished by a call to ANeuralNetworksModel_finish.

See ANeuralNetworksModel for information on multithreaded usage.

Parameters

model The model to be destroyed. Passing NULL is acceptable and results in no operation.

int ANeuralNetworksModel_identifyInputsAndOutputs	(	ANeuralNetworksModel *	model,
		uint32_t	inputCount,
		const uint32_t *	inputs,
		uint32_t	outputCount,
		const uint32_t *	outputs
	)

Specfifies which operands will be the model's inputs and outputs.

An operand cannot be used for both input and output. Doing so will return an error.

Parameters

model	The model to be modified.
inputCount	The number of entries in the inputs array.
inputs	An array of indexes identifying the input operands.
outputCount	The number of entries in the outputs array.
outputs	An array of indexes identifying the output operands.

The operands specified by inputs and outputs must have been previously added by calls to ANeuralNetworksModel_addOperand.

Attempting to modify a model once ANeuralNetworksModel_finish has been called will return an error.

See ANeuralNetworksModel for information on multithreaded usage.

int ANeuralNetworksModel_setOperandValue	(	ANeuralNetworksModel *	model,
		int32_t	index,
		const void *	buffer,
		size_t	length
	)

Sets an operand to a constant value.

Values of length smaller or equal to ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES are immediately copied into the model.

For values of length greater than ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES, a pointer to the buffer is stored within the model. The application is responsible for not changing the content of this region until all executions using this model have completed. As the data may be copied during processing, modifying the data after this call yields undefined results.

For large tensors, using ANeuralNetworksModel_setOperandValueFromMemory is likely to be more efficient.

To indicate that an optional operand should be considered missing, pass nullptr for buffer and 0 for length.

Attempting to modify a model once ANeuralNetworksModel_finish has been called will return an error.

See ANeuralNetworksModel for information on multithreaded usage.

Parameters

model	The model to be modified.
index	The index of the model operand we're setting.
buffer	A pointer to the data to use.
length	The size in bytes of the data value.

Returns: ANEURALNETWORKS_NO_ERROR if successful.

int ANeuralNetworksModel_setOperandValueFromMemory	(	ANeuralNetworksModel *	model,
		int32_t	index,
		const ANeuralNetworksMemory *	memory,
		size_t	offset,
		size_t	length
	)

Sets an operand to a value stored in a memory object.

The content of the memory is not copied. A reference to that memory is stored inside the model. The application is responsible for not changing the content of the memory region until all executions using this model have completed. As the data may be copied during processing, modifying the data after this call yields undefined results.

To indicate that an optional operand should be considered missing, use ANeuralNetworksModel_setOperandValue instead, passing nullptr for buffer.

Attempting to modify a model once ANeuralNetworksModel_finish has been called will return an error.

See ANeuralNetworksModel for information on multithreaded usage.

Parameters

model	The model to be modified.
index	The index of the model operand we're setting.
buffer	A pointer to the data to use.
memory	The memory containing the data.
offset	This specifies the location of the data within the memory. The offset is in bytes from the start of memory.
length	The size in bytes of the data value.

Returns: ANEURALNETWORKS_NO_ERROR if successful.

Variable Documentation

uint32_t ANeuralNetworksOperandType::dimensionCount

The number of dimensions.

It should be 0 for scalars.

Definition at line 1379 of file NeuralNetworks.h.

const uint32_t* ANeuralNetworksOperandType::dimensions

The dimensions of the tensor.

It should be nullptr for scalars.

Definition at line 1381 of file NeuralNetworks.h.

float ANeuralNetworksOperandType::scale

These two fields are only used for quantized tensors.

They should be zero for scalars and non-fixed point tensors. The dequantized value of each entry is (value - zeroPoint) * scale.

Definition at line 1386 of file NeuralNetworks.h.

int32_t ANeuralNetworksOperandType::type

The data type, e.g ANEURALNETWORKS_INT8.

Definition at line 1377 of file NeuralNetworks.h.

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