simple-image-classifier/image_classifier_trainer.py at main · juniofaathir/simple-image-classifier · GitHub

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# -*- coding: utf-8 -*-
"""Image Recognition - Batagor - Train

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1KrXevGC4MHKcLHZagx-NBSPRn8KhRJuf
"""

import tensorflow as tf
import os

# Avoid OOM errors by setting GPU Memory Consumption Growth
gpus = tf.config.experimental.list_physical_devices('GPU')

tf.config.list_physical_devices('GPU')

import numpy as np
from matplotlib import pyplot as plt

data = tf.keras.utils.image_dataset_from_directory('/content/drive/MyDrive/dataset',)
data_iterator = data.as_numpy_iterator()
batch = data_iterator.next()

fig, ax = plt.subplots(ncols=4, figsize=(20,20))
for idx, img in enumerate(batch[0][:4]):
    ax[idx].imshow(img.astype(int))
    ax[idx].title.set_text(batch[1][idx])

data = data.map(lambda x,y: (x/255, y))

data.as_numpy_iterator().next()

train_size = int(len(data)*.7)
val_size = int(len(data)*.2)
test_size = int(len(data)*.1)
train_size

train = data.take(train_size)
val = data.skip(train_size).take(val_size)
test = data.skip(train_size+val_size).take(test_size)

train

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout

model = Sequential()
model.add(Conv2D(16, (3,3), 1, activation='relu', input_shape=(256,256,3)))
model.add(MaxPooling2D())
model.add(Conv2D(32, (3,3), 1, activation='relu'))
model.add(MaxPooling2D())
model.add(Conv2D(16, (3,3), 1, activation='relu'))
model.add(MaxPooling2D())
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile('adam', loss=tf.losses.BinaryCrossentropy(), metrics=['accuracy'])
model.summary()

logdir='logs'

tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)

hist = model.fit(train, epochs=20, validation_data=val, callbacks=[tensorboard_callback])

fig = plt.figure()
plt.plot(hist.history['loss'], color='teal', label='loss')
plt.plot(hist.history['val_loss'], color='orange', label='val_loss')
fig.suptitle('Loss', fontsize=20)
plt.legend(loc="upper left")
plt.show()

fig = plt.figure()
plt.plot(hist.history['accuracy'], color='teal', label='accuracy')
plt.plot(hist.history['val_accuracy'], color='orange', label='val_accuracy')
fig.suptitle('Accuracy', fontsize=20)
plt.legend(loc="upper left")
plt.show()

from tensorflow.keras.metrics import Precision, Recall, BinaryAccuracy

pre = Precision()
re = Recall()
acc = BinaryAccuracy()

for batch in test.as_numpy_iterator():
    X, y = batch
    yhat = model.predict(X)
    pre.update_state(y, yhat)
    re.update_state(y, yhat)
    acc.update_state(y, yhat)

print(pre.result(), re.result(), acc.result())

import cv2

img = cv2.imread('/content/drive/MyDrive/dataset/non-batagor/nasi-soto.jpg')
plt.imshow(img)
plt.show()

resize = tf.image.resize(img, (256,256))
plt.imshow(resize.numpy().astype(int))
plt.show()

yhat = model.predict(np.expand_dims(resize/255, 0))
yhat

from tensorflow.keras.models import load_model

output_model_path = '/content/drive/MyDrive/detector-model.h5'
model.save(output_model_path)
print("Model saved to:", output_model_path)