Initial commit

Tensorflow/tutoriel38/mnist.py

@@ -0,0 +1,29 @@
+import numpy as np
+import tensorflow as tf
+import tensorflow_addons as tfa
+
+import common
+
+batch_size=64
+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)
+
+common.write_labels_embs(model, test_ds, 'embeddings1.tsv', 'labels.tsv')
+
+model.compile(
+    optimizer=tf.keras.optimizers.Adam(1E-3),
+    loss=tfa.losses.TripletSemiHardLoss())
+
+model.fit(train_ds, epochs=5)
+
+common.write_labels_embs(model, test_ds, 'embeddings2.tsv', None)