train_model.py

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import pathlib
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential

# configurations
DATASET_PATH = "./dataset/"
OUTPUT_MODEL_PATH = 'saved_model/my_model'
OUTPUT_WEIGHTS_PATH = 'saved_weight/my_checkpoint'
EPOCHS = 10
BATCH_SIZE = 32
IMG_HEIGHT = 180
IMG_WIDTH = 180

# read data
data_dir = pathlib.Path(DATASET_PATH)
image_count = len(list(data_dir.glob('*/*.jpg')))
print(f'number of images: {image_count}')

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="training",
  seed=123,
  image_size=(IMG_HEIGHT, IMG_WIDTH),
  batch_size=BATCH_SIZE)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="validation",
  seed=123,
  image_size=(IMG_HEIGHT, IMG_WIDTH),
  batch_size=BATCH_SIZE)

# class names equals to relevant directory name
class_names = train_ds.class_names

# load all data to ram to improve access speed
AUTOTUNE = tf.data.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)


# create model
num_classes = 2
model = Sequential([
  layers.experimental.preprocessing.Rescaling(1./255, input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)),
  layers.Conv2D(16, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(32, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(64, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Flatten(),
  layers.Dense(128, activation='relu'),
  layers.Dense(num_classes)
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.summary()

history = model.fit(
  train_ds,
  validation_data=val_ds,
  epochs=EPOCHS
)

# save weights
model.save_weights(OUTPUT_WEIGHTS_PATH)
# save models
#model.save(OUTPUT_MODEL_PATH)

# plot
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

epochs_range = range(EPOCHS)

plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()