erdetn / tf

V language binding for TensorFlow

TensorFlow V Binding

V language binding for TensorFlow

This README provides an overview of how to use the TensorFlow V binding to perform a simple addition operation. The example code demonstrates creating tensors, defining a computation graph, and executing a session to obtain the result.

Prerequisites

Ensure you have the following installed:

• V programming language
• TensorFlow library. Check this LINK

Code overview

The following code exanmple initializes TensorFlow components, creates tensors, defines operations, and executes a session to perform the addition of two integers.

1. Initialize Status and Graph:

status := tf.Status.new()
graph := tf.Graph.new()

2. Create Tensor Values:

a_val := &TensorI32{
    value: 10
}
b_val := &TensorI32{
    value: 20
}

3. Create Tensors from Values:

a := tf.Tensor.new(a_val)
b := tf.Tensor.new(b_val)

4. Print Tensor Values:

ai := *(&i32(a.ptr()))
bi := *(&i32(b.ptr()))
println('${ai} ${bi}')

5. Define Placeholders in the Graph:

desc_a := graph.new_operation('Placeholder', 'a')
desc_a.set_type(.int32)
op_a := desc_a.finish_operation(status)

desc_b := graph.new_operation('Placeholder', 'b')
desc_b.set_type(.int32)
op_b := desc_b.finish_operation(status)

6. Define Addition Operation:

desc_add := graph.new_operation('Add', 'd')
desc_add.set_type(.int32)
desc_add.add_input(in_a)
desc_add.add_input(in_b)
op_add := desc_add.finish_operation(status)
output := *op_add.output(0)

7.Create and Run the Session:

sess_opts := tf.SessionOptions.new()
session := graph.new_session(sess_opts, status)

output_value := []&tf.Tensor{len: 1, init: &tf.Tensor(unsafe { nil })}

session.run(tf.null, inputs, input_tensors[..], [output], output_value, [], tf.null, status)

8.Print the Result:

if !tf.is_null(output_value[0]) {
    result := *(&i32(output_value[0].ptr()))
    print('Result: ${result}\n')
} else {
    print('Output tensor is null.\n')
}

9. Clean Up Resources:

a.delete()
b.delete()
output_value[0].delete()
graph.delete()
session.delete(status)
sess_opts.delete()
status.delete()

10. Running the Code

To run the code, compile and execute it using the V compiler:

v run main.v

This will output the result of the addition operation, demonstrating the basic usage of TensorFlow with the V language.

Check example ex4.v by running

v run examples/ex4.v

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The TensorI32 struct is an implementation of the ITensor interface, designed to encapsulate a 32-bit integer tensor. This documentation provides an overview of its fields and methods, detailing how to interact with and use the TensorI32 struct in the V programming language.

Struct definition

struct TensorI32 {
mut:
    value i32
}

• value (i32): The integer value that the tensor encapsulates. This field is mutable, allowing it to be modified after initialization.

Method data() voidptr

fn (t &TensorI32) data() voidptr {
    return unsafe {
        voidptr(&(t.value))
    }
}

• Returns: A pointer to the tensor's data.
• Description: This method provides access to the raw data of the tensor. It returns a void pointer to the memory location of the tensor's value.

Method len() usize

fn (t &TensorI32) len() usize {
    return sizeof(t.value)
}

• Returns: The size of the tensor's data in bytes.
• Description: This method returns the size of the tensor's data, which is equivalent to the size of a 32-bit integer.

Method dtype() tf.DataType

fn (t &TensorI32) dtype() tf.DataType {
    return .int32
}

• Returns: The data type of the tensor.
• Description: This method returns the data type of the tensor, which is tf.DataType.int32 for TensorI32.

Method shape() tf.Shape

fn (t &TensorI32) shape() tf.Shape {
    return tf.shape(1)
}

• Returns: The shape of the tensor.
• Description: This method returns the shape of the tensor. For TensorI32, it returns a shape with a single dimension of size 1.

method str() string

fn (t &TensorI32) str() string {
    val := *(&i32(t.data()))
    mut str := '{'
    str += 'value: ${t.value}, '
    str += 'data: ${t.data()} [${val}], '
    str += 'len: ${t.len()}, '
    str += 'dtype: ${t.dtype()}, '
    str += 'shape: ${t.shape()}}'
    return str
}

• Returns: A string representation of the tensor.
• Description: This method returns a string representation of the tensor, including its value, data pointer, length, data type, and shape. It provides a comprehensive view of the tensor's properties for debugging and logging purposes.

Example of using TensorI32

fn main() {
    tensor := &TensorI32{
        value: 10
    }

    println('Tensor Data Pointer: ${tensor.data()}')
    println('Tensor Length: ${tensor.len()}')
    println('Tensor Data Type: ${tensor.dtype()}')
    println('Tensor Shape: ${tensor.shape()}')
    println('Tensor String Representation: ${tensor.str()}')
}

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WARNING: The API defined in this package is not stable and can change without notice. The API is subject to change and may break at any time.

Note: This is test using TF version "2.15.0"