mobileNet.py

'''
José Enrique Maese Álvarez
TFG: 
'''

import matplotlib.pyplot as plt
import os
import tensorflow as tf
import tensorflow.keras.layers as tfl
import numpy as np
from tensorflow.keras.preprocessing import image_dataset_from_directory
from tensorflow.keras.layers.experimental.preprocessing import RandomFlip, RandomRotation
import funciones as fun
os.environ['KMP_DUPLICATE_LIB_OK']='True'



BATCH_SIZE = 1
IMG_SIZE = (224, 224)   #160x160
directory = "Imagenes/Numeros/"
train_dataset = image_dataset_from_directory(directory,
                                             shuffle=True,
                                             batch_size=BATCH_SIZE,
                                             image_size=IMG_SIZE,
                                             validation_split=0.2,
                                             subset='training',
                                             seed=42)
test_dataset = image_dataset_from_directory(directory,
                                             shuffle=True,
                                             batch_size=BATCH_SIZE,
                                             image_size=IMG_SIZE,
                                             validation_split=0.2,
                                             subset='validation',
                                             seed=42)


class_names = train_dataset.class_names

BATCH_SIZE = 10
# plt.figure(figsize=(10, 10))
# for images, labels in train_dataset.take(2):
#    for i in range(BATCH_SIZE):
#        ax = plt.subplot(2, 3, i + 1)
#        plt.imshow(images[i].numpy().astype('uint8'))
#        plt.title(class_names[labels[i]])
#        plt.axis("off")


AUTOTUNE = tf.data.experimental.AUTOTUNE
train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)
test_dataset = test_dataset.prefetch(buffer_size=AUTOTUNE)
data_augmentation = fun.data_augmenter()

# for image, _ in train_dataset.take(1):
#     plt.figure(figsize=(10, 10))
#     first_image = image[0]
#     for i in range(3):
#         ax = plt.subplot(1, 3, i + 1)
#         augmented_image = data_augmentation(tf.expand_dims(first_image, 0))
#         plt.imshow(augmented_image[0] / 255)
#         plt.axis('off')
        
        
'A partir de este punto se implementara mobileNet'
#preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input

IMG_SHAPE = IMG_SIZE + (3,)
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=True, weights='imagenet')

base_model.summary()

mobileNet_model = fun.number_mobileNet_model(IMG_SIZE, data_augmentation)

base_learning_rate = 0.001
mobileNet_model.compile(optimizer=tf.keras.optimizers.Adam(lr=base_learning_rate),
              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
              metrics=['accuracy'])

initial_epochs = 10
history = mobileNet_model.fit(train_dataset, validation_data=test_dataset, epochs=initial_epochs)


acc = [0.] + history.history['accuracy']
val_acc = [0.] + history.history['val_accuracy']

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

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

plt.subplot(2, 1, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.ylabel('Cross Entropy')
plt.ylim([0,1.0])
plt.title('Training and Validation Loss')
plt.xlabel('epoch')
plt.show()

class_names