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🌸 PetalFlow

PetalFlow logo

A pure C light-weight library for machine learning

🚧 PetalFlow is under heavy development

Please use with caution and wait for the proper release

Currently, PetalFlow has only been tested on Linux and Windows with x86_64 architecture

In the future, it is planned to extend its usability even to AVR / ARM / ESP (such as Arduino / STM / ESP boards) In the future it is planned to use it even on AVR (like Arduino)

📝 TODO for the near future

  • Add convolution layers
  • Python bindings
  • Test on ARM and AVR
  • More tests and examples

📚 Building shared library

It's possible to build shared library using cmake

cmake -B build -DTESTS=OFF -DLOGGER_LEVEL=1
cmake --build build --config Release

Shared library will be located inside build directory

🏗️ Getting started

Below is an example of an extremely simple classifier capable of just comparing two numbers

Alt text

  1. Include base libraries

    #include <stdint.h>
#include <stdio.h>
#include <stdlib.h>

#include "flower.h"
#include "random.h"

  2. Add functions to print array and create demo dataset

    Click to show code 
    /**
 * @brief Prints 1D array as 1D or multidimensional array
 *
 * @param array pointer to array
 * @param rows number of rows (height)
 * @param cols number of columns (width)
 * @param depth number of channel (depth)
 */
void print_array(float *array, uint32_t rows, uint32_t cols, uint32_t depth) {
    for (uint32_t row = 0; row < rows; ++row) {
        for (uint32_t col = 0; col < cols; ++col) {
            if (depth > 1)
                printf("(");
            for (uint32_t channel = 0; channel < depth; ++channel) {
                printf("%.4f", array[(row * cols + col) * depth + channel]);

                if (channel < depth - 1)
                    printf(", ");
            }
            if (depth > 1)
                printf(")");
            printf("\t");
        }
        printf("\n");
    }
}

/**
 * @brief Generates 2D array of random floats from -10.0 to 10.0
 *
 * @param rows number of rows (outer array length)
 * @param cols number of elements in each internal array
 * @return float** 2D array of random floats
 */
float **dense_generate_input_data(uint32_t rows, uint32_t cols) {
    // Allocate memory for the outer array (rows)
    float **array = (float **) malloc(rows * sizeof(float *));
    if (!array) {
        printf("Error allocating array for dense_generate_input_data!\n");
        return NULL;
    }

    for (uint32_t row = 0; row < rows; ++row) {
        // Allocate memory for each internal array (columns)
        array[row] = (float *) malloc(cols * sizeof(float));
        if (!array[row]) {
            printf("Error allocating array[row] for dense_generate_input_data!\n");
            return NULL;
        }

        // Populate the internal array with random float values in (-10, 10] interval
        for (uint32_t col = 0; col < cols; ++col) {
            array[row][col] = rk_float_() * 20.f - 10.f;
        }
    }
    return array;
}

/**
 * @brief Generates 2D array of expected outputs by comparing 1st and 2nd
 * elements of input_data array
 *
 * @param input_data 2D array of random floats
 * @param rows number of rows (outer array length)
 * @return float** true outputs (cols = 2)
 */
float **dense_generate_output_data(float **input_data, uint32_t rows) {
    // Allocate memory for the outer array (rows)
    float **array = (float **) malloc(rows * sizeof(float *));
    if (!array) {
        printf("Error allocating array for dense_generate_output_data!\n");
        return NULL;
    }

    for (uint32_t row = 0; row < rows; ++row) {
        // Allocate memory for each internal array (columns)
        array[row] = (float *) calloc(2U, sizeof(float));
        if (!array[row]) {
            printf("Error allocating array[row] for "
                    "dense_generate_output_data!\n");
            return NULL;
        }

        // 1 > 2
        if (input_data[row][0] > input_data[row][1])
            array[row][0] = 1.f;

        // 1 <= 2
        else
            array[row][1] = 1.f;
    }
    return array;
}

  3. Initialize datasets

    Click to show code 
    // Set random seed
// rk_seed_(time(NULL) & 0xFFFFFFFFUL);
rk_seed_(0);

// 1000 numbers from -10 to 10: 80% train, 20% validation
uint32_t train_dataset_length = 800;
uint32_t validation_dataset_length = 200;

// Generate validation datasets
float **train_dataset_inputs = dense_generate_input_data(train_dataset_length, 2U);
float **validation_dataset_inputs = dense_generate_input_data(validation_dataset_length, 2U);
if (!train_dataset_inputs || !validation_dataset_inputs) {
    printf("train_dataset_inputs or validation_dataset_inputs allocation failed\n");
    return 1U;
}

// Generate outputs
float **train_dataset_outputs = dense_generate_output_data(train_dataset_inputs, train_dataset_length);
float **validation_dataset_outputs =
    dense_generate_output_data(validation_dataset_inputs, validation_dataset_length);
if (!train_dataset_outputs || !validation_dataset_outputs) {
    printf("train_dataset_outputs or est_dataset_outputs allocation failed\n");
    return 1U;
}

  4. Initialize petals

    Initializes petal's struct and petal's weights if needed Sets petal->error_code in case of error.

    Parameters

    • petal_type: type of the petal (PETAL_TYPE_...)
    • first: true if it's the first petal (output_left is input data) to prevent error_on_input calculation
    • input_shape: pointer to petal_shape_s struct:
      • rows: height of input data
      • cols: width (or size for 1D) of input data
      • depth: number of channels of input data
      • length: calculates internally
    • output_shape: pointer to petal_shape_s struct:
      • rows: height of output data
      • cols: width (or size for 1D) of output data
      • depth: number of channels of output data
      • length: calculates internally
    • weights: pointer to weights_s struct (for PETAL_TYPE_DENSE_1D) or NULL for other types:
      • trainable: 1 if weights will be trained or 0 if not
      • initializer: weights initializer (WEIGHTS_INIT_...)
      • weights: pass NULL to initialize weights or pointer to previously initialized weights
      • center: constant for WEIGHTS_INIT_CONSTANT or center of distribution for other initializers
      • deviation: deviation of distribution (ignored for WEIGHTS_INIT_CONSTANT)
    • bias_weights: pointer to weights_s struct (for PETAL_TYPE_DENSE_1D) or NULL for other types:
      • trainable: 1 if bias weights will be trained or 0 if not
      • initializer: bias weights initializer (WEIGHTS_INIT_...)
      • weights: pass NULL to initialize bias weights or pointer to previously initialized bias weights
      • center: constant for WEIGHTS_INIT_CONSTANT or center of distribution for other initializers
      • deviation: deviation of distribution (ignored for WEIGHTS_INIT_CONSTANT)
    • activation: pointer to activation_s struct or NULL to disable activation:
      • type: activation function (ACTIVATION_...)
      • linear_alpha: factor for linear activation (ax + c) (for ACTIVATION_LINEAR only). Default = 1.0
      • linear_const: constant for linear activation (ax + c) (for ACTIVATION_LINEAR only). Default = 0.0
      • relu_leak: leak amount (for ACTIVATION_RELU only). Default = 0.01
      • elu_alpha: the value to which an ELU saturates for negative net inputs (for ACTIVATION_ELU only). Default = 0.01
      • swish_beta: beta for turning Swish into E-Swish (for ACTIVATION_SWISH only). Default = 1.0
    • dropout: ratio of dropped outputs (0 to 1)
    • center: center of normalization for PETAL_TYPE_NORMALIZE_... Default: 0.0
    • deviation: deviation of normalization for PETAL_TYPE_NORMALIZE_... Default: 1.0

    Returns

    • petal_s*: petal's struct

    Available weights initializers:

    • WEIGHTS_INIT_CONSTANT
    • WEIGHTS_INIT_RANDOM_UNIFORM
    • WEIGHTS_INIT_RANDOM_GAUSSIAN
    • WEIGHTS_INIT_XAVIER_GLOROT_UNIFORM
    • WEIGHTS_INIT_XAVIER_GLOROT_GAUSSIAN
    • WEIGHTS_INIT_KAIMING_HE_UNIFORM
    • WEIGHTS_INIT_KAIMING_HE_GAUSSIAN

    Available activation functions:

    • ACTIVATION_LINEAR
    • ACTIVATION_RELU
    • ACTIVATION_ELU
    • ACTIVATION_SOFTSIGN
    • ACTIVATION_SIGMOID
    • ACTIVATION_HARD_SIGMOID
    • ACTIVATION_SWISH
    • ACTIVATION_SOFTMAX
    • ACTIVATION_TANH
    petal_s *petal_hidden1 =
    petal_init(PETAL_TYPE_DENSE_1D, true, &(petal_shape_s){1U, 2U, 1U, 0UL}, &(petal_shape_s){1U, 2U, 1U, 0UL},
                &(weights_s){true, WEIGHTS_INIT_XAVIER_GLOROT_GAUSSIAN, 4U, NULL, NULL, 0.f, 1.f, NULL, NULL, 0U},
                &(weights_s){true, WEIGHTS_INIT_CONSTANT, 2U, NULL, NULL, 0.f, 1.f, NULL, NULL, 0U},
                &(activation_s){ACTIVATION_RELU, 1.f, 0.f, 0.0f, 0.00f, 1.f, NULL}, 0.0f, 0.f, 1.f);
petal_s *petal_hidden2 =
    petal_init(PETAL_TYPE_DENSE_1D, false, &(petal_shape_s){1U, 2U, 1U, 0UL}, &(petal_shape_s){1U, 2U, 1U, 0UL},
                &(weights_s){true, WEIGHTS_INIT_XAVIER_GLOROT_GAUSSIAN, 4U, NULL, NULL, 0.f, 1.f, NULL, NULL, 0U},
                &(weights_s){true, WEIGHTS_INIT_CONSTANT, 2U, NULL, NULL, 0.f, 1.f, NULL, NULL, 0U},
                &(activation_s){ACTIVATION_RELU, 1.f, 0.f, 0.0f, 0.00f, 1.f, NULL}, 0.0f, 0.f, 1.f);
petal_s *petal_output =
    petal_init(PETAL_TYPE_DENSE_1D, false, &(petal_shape_s){1U, 2U, 1U, 0UL}, &(petal_shape_s){1U, 2U, 1U, 0UL},
                &(weights_s){true, WEIGHTS_INIT_XAVIER_GLOROT_GAUSSIAN, 6U, NULL, NULL, 0.f, 1.f, NULL, NULL, 0U},
                &(weights_s){true, WEIGHTS_INIT_CONSTANT, 2U, NULL, NULL, 0.f, 1.f, NULL, NULL, 0U},
                &(activation_s){ACTIVATION_SOFTMAX, 1.f, 0.f, 0.0f, 0.01f, 1.f, NULL}, 0.0f, 0.f, 1.f);

  5. Review generated weights

    printf("In -> hidden 1 weights:\n");
print_array(petal_hidden1->weights->weights, 2U, 2U, 1U);
printf("In -> hidden 1 bias weights:\n");
print_array(petal_hidden1->bias_weights->weights, 1U, 2U, 1U);

printf("hidden 1 -> hidden 2 weights:\n");
print_array(petal_hidden2->weights->weights, 2U, 2U, 1U);
printf("hidden 1 -> hidden 2 bias weights:\n");
print_array(petal_hidden2->bias_weights->weights, 1U, 2U, 1U);

printf("hidden 2 -> out weights:\n");
print_array(petal_output->weights->weights, 2U, 2U, 1U);
printf("hidden 2 -> out bias weights:\n");
print_array(petal_output->bias_weights->weights, 1U, 2U, 1U);
    In -> hidden 1 weights:
0.0464  0.6727
0.7127  -1.2468
In -> hidden 1 bias weights:
0.0000  0.0000
hidden 1 -> hidden 2 weights:
-0.2981 -0.2883
-0.7801 -0.4236
hidden 1 -> hidden 2 bias weights:
0.0000  0.0000
hidden 2 -> out weights:
1.5173  -1.2947
-0.9410 0.3021
hidden 2 -> out bias weights:
0.0000  0.0000

  6. Initialize flower

    Initializes flower using array of petals.

    Parameters

    • petals: pointer to an array of pointers of petals
    • petals_length: number of petals

    Returns

    • flower_s*: initialized flower
    petal_s *petals[] = {petal_hidden1, petal_hidden2, petal_output};
flower_s *flower = flower_init(petals, 3U);

  7. Show model output before training

    X1 = 1, X2 = 2

    // Show prediction before training
printf("Before training [1.0, 2.0] -> [1 > 2, 1 <= 2]:\t\t");
print_array(flower_predict(flower, (float[]){1.f, 2.f}), 1U, 2U, 1U);
    Before training [1.0, 2.0] -> [1 > 2, 1 <= 2]:          0.5000  0.5000

  8. Initialize optimizer and metrics

    Parameters

    • type: optimizer type (OPTIMIZER_...)
    • learning_rate: learning rate (required for all optimizer types) Default: 0.01
    • momentum: accelerates gradient descent and dampens oscillations (for OPTIMIZER_SGD_MOMENTUM)
    • beta_1: hyperparameter (for OPTIMIZER_RMS_PROP and OPTIMIZER_ADAM) Default: 0.9
    • beta_2: hyperparameter (for OPTIMIZER_ADAM) Default: 0.999

    Available metrics:

    • METRICS_TIME_ELAPSED
    • METRICS_LOSS_TRAIN
    • METRICS_ACCURACY_TRAIN
    • METRICS_LOSS_VALIDATION
    • METRICS_ACCURACY_VALIDATION
    // Initialize optimizer
optimizer_s optimizer = (optimizer_s){OPTIMIZER_ADAM, .01f, 0.f, .89f, .99f};

// Initialize metrics
metrics_s *metrics = metrics_init(1);
metrics_add(metrics, METRICS_TIME_ELAPSED);
metrics_add(metrics, METRICS_LOSS_TRAIN);
metrics_add(metrics, METRICS_ACCURACY_TRAIN);
metrics_add(metrics, METRICS_LOSS_VALIDATION);
metrics_add(metrics, METRICS_ACCURACY_VALIDATION);

  9. Train model

    Early implementation of backpropagation learning.

    Parameters

    • flower: pointer to initialized flower_s struct
    • loss_type: loss function (LOSS_...)
    • optimizer: pointer to initialized optimizer_s struct with the following parameters:
      • type: optimizer type (OPTIMIZER_...)
      • learning_rate: learning rate (required for all optimizer types) Default: 0.01
      • momentum: accelerates gradient descent and dampens oscillations (for OPTIMIZER_SGD_MOMENTUM)
      • beta_1: hyperparameter (for OPTIMIZER_RMS_PROP and OPTIMIZER_ADAM) Default: 0.9
      • beta_2: hyperparameter (for OPTIMIZER_ADAM) Default: 0.999
    • metrics: pointer to initialized metrics_s struct
    • inputs_train: pointer to array of arrays of training input data (train dataset)
    • outputs_true_train: pointer to array of arrays of training output data (train dataset)
    • outputs_true_train_sparse: pointer to array of label_s arrays of sparse training output data (1 = [0, 1, ...])
    • train_length: number of training samples (size of training dataset)
    • inputs_validation: pointer to array of arrays of validation input data (validation dataset)
    • outputs_true_validation: pointer to array of arrays of validation output data (train dataset)
    • outputs_true_validation_sparse: pointer to array of label_s arrays of sparse validation output data
    • validation_length: number of validation samples (size of validation dataset)
    • batch_size: samples per batch
    • epochs: total number of training epochs

    Available loss functions:

    • LOSS_MEAN_SQUARED_ERROR
    • LOSS_MEAN_SQUARED_LOG_ERROR
    • LOSS_ROOT_MEAN_SQUARED_LOG_ERROR
    • LOSS_MEAN_ABS_ERROR
    • LOSS_BINARY_CROSSENTROPY
    • LOSS_CATEGORICAL_CROSSENTROPY
    uint32_t epochs = 10;
uint32_t batch_size = 40;
flower_train(flower, LOSS_CATEGORICAL_CROSSENTROPY, &optimizer, metrics, train_dataset_inputs,
             train_dataset_outputs, NULL, train_dataset_length, validation_dataset_inputs,
             validation_dataset_outputs, NULL, validation_dataset_length, batch_size, epochs);
    [2024-04-16 00:28:45] [INFO] [flower_train] Training started
[2024-04-16 00:28:45] [INFO] [flower_train] Epoch: 1/10
[====================] 20/20 | 00:00:00 | Tloss:   0.2788 | Tacc:  95.00% | Vloss:   0.2481 | Vacc:  93.50%
[2024-04-16 00:28:45] [INFO] [flower_train] Epoch: 2/10
[====================] 20/20 | 00:00:00 | Tloss:   0.1589 | Tacc:  92.50% | Vloss:   0.1467 | Vacc:  96.00%
[2024-04-16 00:28:45] [INFO] [flower_train] Epoch: 3/10
[====================] 20/20 | 00:00:00 | Tloss:   0.1022 | Tacc:  95.00% | Vloss:   0.1076 | Vacc:  95.50%
[2024-04-16 00:28:45] [INFO] [flower_train] Epoch: 4/10
[====================] 20/20 | 00:00:00 | Tloss:   0.0770 | Tacc:  97.50% | Vloss:   0.0761 | Vacc:  96.50%
[2024-04-16 00:28:45] [INFO] [flower_train] Epoch: 5/10
[====================] 20/20 | 00:00:00 | Tloss:   0.0519 | Tacc: 100.00% | Vloss:   0.0515 | Vacc:  98.50%
[2024-04-16 00:28:45] [INFO] [flower_train] Epoch: 6/10
[====================] 20/20 | 00:00:00 | Tloss:   0.0199 | Tacc: 100.00% | Vloss:   0.0334 | Vacc:  99.00%
[2024-04-16 00:28:45] [INFO] [flower_train] Epoch: 7/10
[=============>      ] 20/20 | 00:00:00 | Tloss:   0.0109 | Tacc: 100.00% | Vloss:   0.0185 | Vacc: 100.00%
...

  10. Test the result

    // Test training result on a new data
float *result;
printf("After training [1.0, 10.0] -> [1 > 2, 1 <= 2]:\t\t");
result = flower_predict(flower, (float[]){1.f, 10.f});
printf("After training [20.0, 10.0] -> [1 > 2, 1 <= 2]:\t\t");
result = flower_predict(flower, (float[]){20.f, 10.f});
print_array(result, 1U, 2U, 1U);
printf("After training [-1.0, 10.0] -> [1 > 2, 1 <= 2]:\t\t");
result = flower_predict(flower, (float[]){-1.f, 10.f});
print_array(result, 1U, 2U, 1U);
    After training [1.0, 10.0] -> [1 > 2, 1 <= 2]:          0.0072  0.9928
After training [20.0, 10.0] -> [1 > 2, 1 <= 2]:         0.9339  0.0661
After training [-1.0, 10.0] -> [1 > 2, 1 <= 2]:         0.0072  0.9928

  11. Free memory

    void weights_destroy(weights_s *weights,
                     bool destroy_struct,
                     bool destroy_internal_array)

    Frees memory allocated by weights struct.

    Parameters

    • weights: pointer to weights_s struct or NULL
    • destroy_struct: true to destroy struct itself (set to false if struct was defined manually)
    • destroy_internal_array: true to also destroy weights->weights array
    void flower_destroy(flower_s *flower,
                     bool destroy_petals,
                     bool destroy_weights_array,
                     bool destroy_bias_weights_array)

    Frees memory allocated by flower struct.

    Parameters

    • flower: pointer to flower_s struct
    • destroy_petals: true to also destroy each petal
    • destroy_weights_array: true to also destroy weights->weights array for each petal, false to not
    • destroy_bias_weights_array: true to also destroy bias_weights->weights array for each petal, false to not
    // Destroy internal array of weights
weights_destroy(petal_hidden1->weights, false, true);
weights_destroy(petal_hidden1->bias_weights, false, true);
weights_destroy(petal_hidden2->weights, false, true);
weights_destroy(petal_hidden2->bias_weights, false, true);
weights_destroy(petal_output->weights, false, true);
weights_destroy(petal_output->bias_weights, false, true);

// Destroy flower without destroying petals
flower_destroy(flower, false, false, false);

// Destroy metrics
metrics_destroy(metrics);

// Destroy datasets
for (uint16_t i = 0; i < train_dataset_length; ++i) {
    free(train_dataset_inputs[i]);
    free(train_dataset_outputs[i]);
}
for (uint16_t i = 0; i < validation_dataset_length; ++i) {
    free(validation_dataset_inputs[i]);
    free(validation_dataset_outputs[i]);
}
free(train_dataset_inputs);
free(train_dataset_outputs);
free(validation_dataset_inputs);
free(validation_dataset_outputs);

🧾 Logging

PetalFlow has a simple logging implementation for formatting debug, info, warning and error messages

logger()

void logger(uint8_t level,
            const char *tag,
            const char *message_or_format,
            ... )

Formats and prints logging entry.

Parameters

  • level: logging level (LOG_D, LOG_I, LOG_W, LOG_E, or LOG_NONE)
  • tag: logging tag (for example, name of function)
  • message_or_format: message to log or format for other arguments
  • ...: other logger arguments, for example: logger(LOG_I, "TAG", "Hello world %d, %.2f", 123, 4.5678f); would result in [YYYY-MM-DD HH:MM:SS] [INFO] [TAG] Hello world 123, 4.57

You can enable logging by defining LOGGING (for gcc: -DLOGGING). Other available definitions:

  • LOGGER_LEVEL <value> - Minimal allowed logging level. Default: LOG_I
    • LOG_D - 0
    • LOG_I - 1
    • LOG_W - 2
    • LOG_E - 3
    • LOG_NONE - 255
  • LOGGER_DISABLE_TIME - Define to disable printing current time
  • LOGGER_TIME_FORMAT <value> - Time formatter. Default: "[%Y-%m-%d %H:%M:%S]"
  • LOGGER_DISABLE_LEVEL - Define to disable printing current entry level
  • LOGGER_LEVEL_FIXED - Define to enable fixed-width logging level printing
  • LOGGER_LEVEL_FIXED_FORMAT <value> - Fixed-width level formatter. Default: "[%-7s]"
  • LOGGER_DISABLE_TAG - Define to disable printing tag

✅ Tests and examples

You can find more examples in test/main.c file

You can build petalflow_tests target by using cmake:

cmake -B build -DTESTS=ON -DLOGGER_LEVEL=1
cmake --build build --config Release

build/petalflow_tests

Or by using gcc:

gcc -o petalflow test/main.c src/*.c -Iinclude -DLOGGING -DLOGGER_LEVEL=1 -lm

./petalflow

📄 Documentation

You can build Doxygen documentation using provided Doxygen file

Just clone repo and run:

doxygen

This will generate HTML and LaTeX with the following structure:

docs
├── html
│   ├── activation_8c.html
...
│   ├── index.html
...
│   └── weights_8h_source.html
└── latex
    ├── activation_8c.tex
    ...
    ├── Makefile
    ...
    └── weights_8h.tex

About

A pure C light-weight library for machine learning

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c alternative arduino avr machine-learning arm ai neural-network amd tensorflow x86-64 esp32 stm32 ml artificial-intelligence neural-networks

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