SineFromSDExample

Author: eloquentarduino

examples/.DS_Store

@@ -0,0 +1,77 @@
+#include <SPI.h>
+#include <SD.h>
+#include <EloquentTinyML.h>
+
+#define NUMBER_OF_INPUTS 1
+#define NUMBER_OF_OUTPUTS 1
+#define TENSOR_ARENA_SIZE 2*1024
+
+uint8_t *loadedModel;
+Eloquent::TinyML::TfLite<NUMBER_OF_INPUTS, NUMBER_OF_OUTPUTS, TENSOR_ARENA_SIZE> ml;
+
+
+void loadModel(void);
+
+
+/**
+ *
+ */
+void setup() {
+    Serial.begin(115200);
+    SPI.begin();
+    delay(3000);
+
+    if (!SD.begin(4)) {
+        Serial.println("Cannot init SD");
+        delay(60000);
+    }
+
+    loadModel();
+
+    if (!ml.begin(loadedModel)) {
+        Serial.println("Cannot inialize model");
+        Serial.println(ml.errorMessage());
+        delay(60000);
+    }
+}
+
+
+/**
+ *
+ */
+void loop() {
+    // pick up a random x and predict its sine
+    float x = 3.14 * random(100) / 100;
+    float y = sin(x);
+    float input[1] = { x };
+    float predicted = ml.predict(input);
+
+    Serial.print("sin(");
+    Serial.print(x);
+    Serial.print(") = ");
+    Serial.print(y);
+    Serial.print("\t predicted: ");
+    Serial.println(predicted);
+    delay(1000);
+}
+
+
+/**
+ * Load model from SD
+ */
+void loadModel() {
+    File file = SD.open("/sine.bin", FILE_READ);
+    size_t modelSize = file.size();
+
+    Serial.print("Found model on filesystem of size ");
+    Serial.println(modelSize);
+
+    // allocate memory
+    loadedModel = (uint8_t*) malloc(modelSize);
+
+    // copy data from file
+    for (size_t i = 0; i < modelSize; i++)
+        loadedModel[i] = file.read();
+
+    file.close();
+}

examples/SineFromSDExample/SineFromSDExample.ino

@@ -0,0 +1,44 @@
+import math
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras import layers
+
+
+def get_model():
+    SAMPLES = 1000
+    np.random.seed(1337)
+    x_values = np.random.uniform(low=0, high=2*math.pi, size=SAMPLES)
+    # shuffle and add noise
+    np.random.shuffle(x_values)
+    y_values = np.sin(x_values)
+    y_values += 0.1 * np.random.randn(*y_values.shape)
+
+    # split into train, validation, test
+    TRAIN_SPLIT =  int(0.6 * SAMPLES)
+    TEST_SPLIT = int(0.2 * SAMPLES + TRAIN_SPLIT)
+    x_train, x_test, x_validate = np.split(x_values, [TRAIN_SPLIT, TEST_SPLIT])
+    y_train, y_test, y_validate = np.split(y_values, [TRAIN_SPLIT, TEST_SPLIT])
+
+    # create a NN with 2 layers of 16 neurons
+    model = tf.keras.Sequential()
+    model.add(layers.Dense(8, activation='relu', input_shape=(1,)))
+    model.add(layers.Dense(16, activation='relu'))
+    model.add(layers.Dense(1))
+    model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
+    model.fit(x_train, y_train, epochs=100, batch_size=16,
+              validation_data=(x_validate, y_validate))
+    return model
+
+
+if __name__ == '__main__':
+    model = get_model()
+
+    # save to binary file
+    with open('sine.bin', 'wb') as file:
+        converter = tf.lite.TFLiteConverter.from_keras_model(model)
+        tflite_model = converter.convert()
+        file.write(tflite_model)
+
+    # read back binary file
+    with open('sine.bin', 'rb') as file:
+        print(['%x' % byte for byte in file.read()])

examples/SineFromSDExample/sine.bin

@@ -58,7 +58,7 @@ void storeModel() {
  * Load model from SPIFFS
  */
 void loadModel() {
-  File file = SPIFFS.open("/sine.bin");
+  File file = SPIFFS.open("/sine.bin", "rb");
   size_t modelSize = file.size();
 
   Serial.print("Found model on filesystem of size ");