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Tensorflow/tutoriel38/mnist_avec_predictions.py

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+import numpy as np
+import tensorflow as tf
+import tensorflow_addons as tfa
+from tensorflow.keras import layers, models
+from sklearn import cluster
+import sklearn
+
+import common
+
+batch_size=16
+nbr_cc=64 # Nombre de cartes de caracteristique du modele
+embeddings_size=256 # Taille du vecteur embedding (sortie du réseau de neurone)
+
+(x_train, y_train), (x_test, y_test)=tf.keras.datasets.mnist.load_data()
+
+train_dataset=(x_train.reshape(-1, 28, 28, 1)/255).astype(np.float32)
+test_dataset=(x_test.reshape(-1, 28, 28, 1)/255).astype(np.float32)
+
+train_ds=tf.data.Dataset.from_tensor_slices((train_dataset, y_train)).batch(batch_size)
+test_ds=tf.data.Dataset.from_tensor_slices((test_dataset, y_test)).batch(batch_size)
+
+model=common.model_embedding(nbr_cc, embeddings_size)
+
+model.compile(
+    optimizer=tf.keras.optimizers.Adam(1E-3),
+    loss=tfa.losses.TripletSemiHardLoss())
+
+model.fit(train_ds, epochs=5)
+
+# Recherche des centroids des clusters
+embeddings=model.predict(train_dataset)
+kmeans=cluster.KMeans(n_clusters=len(set(y_train)))
+kmeans.fit(embeddings)
+centroids=kmeans.cluster_centers_
+
+# Recherche du label des centroids
+distances=sklearn.metrics.pairwise_distances(embeddings, centroids)
+# shape distances: 6000, 10
+# lmin contient le vecteur embeddings le plus proche de chacun des centroids
+lmin=np.argmin(distances, axis=0)
+labels_centroids=y_train[lmin]
+
+# Calcul de précision de la base d'entrainement
+result=np.equal(y_train, labels_centroids[np.argmin(distances, axis=-1)]).astype(np.float32)
+print("Train: précision {:4.2%}".format(np.mean(result)))
+
+# Calcul de précision de la base de test
+embeddings=model.predict(test_dataset)
+distances=sklearn.metrics.pairwise_distances(embeddings, centroids)
+result=np.equal(y_test.astype(np.int32), labels_centroids[np.argmin(distances, axis=-1)]).astype(np.float32)
+print("Test : précision {:4.2%}".format(np.mean(result)))