# -*- coding: utf-8 -*-
"""
Created on Sun Aug 14 11:32:44 2022
@author: Ricore
"""
# Charger les données
# from zipfile import ZipFile
# with ZipFile('images.zip', 'r') as zipObj:
# zipObj.extractall('images')
train_dir = 'images/train/'
validation_dir = 'images/validation/'
test_dir = 'images/test/'
train_cats_dir = 'images/train/cats'
train_dogs_dir = 'images/train/dogs'
validation_cats_dir = 'images/validation/cats'
validation_dogs_dir = 'images/validation/dogs'
test_cats_dir = 'images/test/cats'
test_dogs_dir = 'images/test/dogs'
import os
print('total training cat images:', len(os.listdir(train_cats_dir)))
print('total training dog images:', len(os.listdir(train_dogs_dir)))
print('total validation cat images:', len(os.listdir(validation_cats_dir)))
print('total validation dog images:', len(os.listdir(validation_dogs_dir)))
print('total test cat images:', len(os.listdir(test_cats_dir)))
print('total test dog images:', len(os.listdir(test_dogs_dir)))
# Les générateurs
from keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(rescale=1./255)
# Preprocessing
train_generator = datagen.flow_from_directory(train_dir,
batch_size=20,
target_size=(150, 150),
class_mode='binary')
validation_generator = datagen.flow_from_directory(validation_dir,
batch_size=20,
target_size=(150, 150),
class_mode='binary')
for data_batch, labels_batch in train_generator:
print('data batch shape:', data_batch.shape)
print('labels batch shape:', labels_batch.shape)
break
# Construire le modèle
from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
from keras import optimizers
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
# Entraîner le modèle
history = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=30,
validation_data=validation_generator,
validation_steps=50)
model.save('model_trained.h5')
#model = model.load_model('model_trained.h5')
# Évaluer le modèle
import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
test_generator = datagen.flow_from_directory(
test_dir,
target_size=(150, 150),
batch_size=20,
class_mode='binary')
model.evaluate(test_generator)
# Contourner l’overfitting
# Data Augmentation
augmented_datagen = ImageDataGenerator(
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
from keras.preprocessing import image
fnames = [os.path.join(train_cats_dir, fname) for
fname in os.listdir(train_cats_dir)]
img_path = fnames[4]
img = image.load_img(img_path, target_size=(150, 150))
x = image.img_to_array(img)
x = x.reshape((1,) + x.shape)
i=0
fig = plt.figure(figsize=(7,7))
for batch in augmented_datagen.flow(x, batch_size=1):
ax = fig.add_subplot(2,2,i+1)
ax.imshow(image.array_to_img(batch[0]))
i += 1
if i % 4 == 0:
break
plt.show()
augmented_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,)
datagen = ImageDataGenerator(rescale=1./255)
train_generator = augmented_datagen.flow_from_directory(
train_dir,
target_size=(150, 150),
batch_size=20,
class_mode='binary')
validation_generator = datagen.flow_from_directory(
validation_dir,
target_size=(150, 150),
batch_size=20,
class_mode='binary')
for data_batch, labels_batch in train_generator:
print('data batch shape:', data_batch.shape)
print('labels batch shape:', labels_batch.shape)
break
# Dropout
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
# Entraîner le nouveau modèle
history = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=100,
validation_data=validation_generator,
validation_steps=50)
model.save('model_trained_enhanced.h5')
#model = model.load_model('model_trained_enhanced.h5')
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
test_generator = datagen.flow_from_directory(
test_dir,
target_size=(150, 150),
batch_size=20,
class_mode='binary')
model.evaluate(test_generator)
