MNIST est un exemple de multi-class classification. Pour changer un peu, utilisons fashion MNIST.
import tensorflow as tf from tensorflow import keras # Helper libraries import numpy as np import matplotlib.pyplot as plt print(tf.__version__) fashion_mnist = keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images = train_images / 255.0 test_images = test_images / 255.0
model = keras.Sequential([ keras.layers.Flatten(input_shape=(28, 28)), keras.layers.Dense(128, activation='relu'), keras.layers.Dense(10) ]) model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) model.fit(train_images, train_labels, epochs=10)
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2) print('\nTest accuracy:', test_acc)
Test accuracy: 0.8789
probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()]) predictions = probability_model.predict(test_images) predictions[0] np.argmax(predictions[0])
Le probability_model ajoute une couche Softmax au modèle, ce qui nous permet d’avoir des probabilités en output plutôt que des logits.