train.py

'''
Arduino Hand Recogniton. It recognizes hand state(open and close), and turns LED on.
Copyright (C) 2023  Luka Jovanovic

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.
'''
import csv
import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split

RANDOM_SEED = 42
dataset = 'model/keypoint.csv'
model_save_path = 'model/keypoint.hdf5'
tflite_save_path = 'model/keypoint.tflite'
NUM_CLASSES = 5
X_dataset = np.loadtxt(dataset, delimiter=',', dtype='float32', usecols=list(range(1, (21 * 2) + 1)))
y_dataset = np.loadtxt(dataset, delimiter=',', dtype='int32', usecols=(0))
X_train, X_test, y_train, y_test = train_test_split(X_dataset, y_dataset, train_size=0.75, random_state=RANDOM_SEED)
model = tf.keras.models.Sequential([
    tf.keras.layers.Input((21 * 2, )),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(20, activation='relu'),
    tf.keras.layers.Dropout(0.4),
    tf.keras.layers.Dense(10, activation='relu'),
    tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')
])
model.summary()  
cp_callback = tf.keras.callbacks.ModelCheckpoint(model_save_path, verbose=1, save_weights_only=False)
es_callback = tf.keras.callbacks.EarlyStopping(patience=20, verbose=1)
model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)
model.fit(
    X_train,
    y_train,
    epochs=1000,
    batch_size=128,
    validation_data=(X_test, y_test),
    callbacks=[cp_callback, es_callback]
)
val_loss, val_acc = model.evaluate(X_test, y_test, batch_size=128)
model = tf.keras.models.load_model(model_save_path)
predict_result = model.predict(np.array([X_test[0]]))
print(np.squeeze(predict_result))
print(np.argmax(np.squeeze(predict_result)))
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, classification_report


Y_pred = model.predict(X_test)
y_pred = np.argmax(Y_pred, axis=1)

model.save(model_save_path, include_optimizer=False)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_quantized_model = converter.convert()

open(tflite_save_path, 'wb').write(tflite_quantized_model)
interpreter = tf.lite.Interpreter(model_path=tflite_save_path)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
interpreter.set_tensor(input_details[0]['index'], np.array([X_test[0]]))
interpreter.invoke()
tflite_results = interpreter.get_tensor(output_details[0]['index'])
print(np.squeeze(tflite_results))
print(np.argmax(np.squeeze(tflite_results)))