from tensorflow.keras.datasets import cifar10
from tensorflow.keras.utils import to_categorical
import numpy as np
def prepare_cifar10():
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = x_train.astype(np.float)
x_test = x_test.astype(np.float)
x_train = x_train / 255.
x_test = x_test / 255.
x_train = x_train.reshape((-1, 32, 32, 3))
x_test = x_test.reshape((-1, 32, 32, 3))
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
return x_train, x_test, y_train, y_test
x_train, x_test, y_train, y_test = prepare_cifar10()
model = Sequential([
Input(shape=(32, 32, 3)),
Conv2D(filters=6,
kernel_size=5, # Nombre de pixel perdu + 1
strides=1,
padding='valid', # La taille de l'image diminue (dans le problème d'origine 32x32)
activation='relu'),
MaxPool2D(pool_size=2),
Conv2D(filters=16,
kernel_size=5,
strides=1,
activation='relu',
padding='valid'),
MaxPool2D(pool_size=2),
Flatten(),
Dense(units=120, activation='relu'),
Dense(units=84, activation='relu'),
Dense(units=10, activation='softmax'),
])
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics="accuracy")
model.summary()
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.datasets import boston_housing
def prepare_boston():
(x_train, y_train), (x_test, y_test) = boston_housing.load_data()
scaler_x = MinMaxScaler()
scaler_x.fit(x_train)
x_train = scaler_x.transform(x_train)
x_test = scaler_x.transform(x_test)
scaler_y = MinMaxScaler()
scaler_y.fit(y_train.reshape((-1, 1)))
y_train = scaler_y.transform(y_train.reshape((-1, 1)))
y_test = scaler_y.transform(y_test.reshape((-1, 1)))
return x_train, x_test, y_train, y_test
x_train, x_test, y_train, y_test = prepare_boston()
