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# Press Mayús+F10 to execute it or replace it with your code.
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import os
import cv2
import random
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras.preprocessing.image import ImageDataGenerator
training_images = './Dataset/training'
validation_images = './Dataset/validation'
training_images_list = os.listdir(training_images)
validation_images_list = os.listdir(validation_images)
IMAGE_SIZE = 100
# Get images from dataset
def get_dataset_image(is_training_data):
images = []
tags = []
data = []
count = 0
image_list = validation_images_list
image_rute = validation_images
if is_training_data:
image_list = training_images_list
image_rute = training_images
for dir_name in image_list:
name = image_rute + '/' + dir_name
for file_name in os.listdir(name):
tags.append(count)
img = cv2.imread(name + '/' + file_name, 0)
if img is None:
print('Wrong path:', name + '/' + file_name)
else:
img = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE), interpolation=cv2.INTER_CUBIC)
img = img.reshape(IMAGE_SIZE, IMAGE_SIZE, 1)
data.append([img, count])
images.append(img)
count = count + 1
return images, tags, data, count
# Normalize images - white and black
def normalize_images(images):
new_images = np.array(images).astype(float) / 255
return new_images
# Avoid over fitting
def avoid_over_fitting(images, tags):
rotation_range = random.randint(0, 90)
width_shift_range = random.uniform(0, 1)
height_shift_range = random.uniform(0, 1)
shear_range = random.randint(0, 25)
img_train_gen = ImageDataGenerator(
rotation_range=rotation_range,
width_shift_range=width_shift_range,
height_shift_range=height_shift_range,
shear_range=shear_range,
zoom_range=[0.5, 1.5],
vertical_flip=True,
horizontal_flip=True
)
img_train_gen.fit(images)
img_train = img_train_gen.flow(images, tags, batch_size=38)
return img_train
# Training lists
train_images, train_tags, train_data, train_count = get_dataset_image(True)
# Validation lists
val_images, val_tags, val_data, val_count = get_dataset_image(False)
print('Read images finalized!')
# Normalize
train_images = normalize_images(train_images)
val_images = normalize_images(val_images)
train_tags = np.array(train_tags)
val_tags = np.array(val_tags)
img_to_train = avoid_over_fitting(train_images, train_tags)
# Set layers and config CNN
CNN_model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(IMAGE_SIZE, IMAGE_SIZE, 1)),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(2, 2),
# Clasification dense layers
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(250, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
CNN_model.compile(
optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy']
)
# Train model
BoardCNN = TensorBoard(log_dir='./logs/cnn')
CNN_model.fit(
img_to_train,
batch_size=38,
validation_data=(val_images, val_tags),
epochs=500,
callbacks=[BoardCNN],
steps_per_epoch=int(np.ceil(len(train_images)/float(38))),
validation_steps=int(np.ceil(len(val_images)/float(38)))
)
# Save model
CNN_model.save('./saves/dogs-cats.h5')
CNN_model.save_weights('./saves/wights-dogs-cats.h5')
print("Finish!")
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