Initial commit

Tensorflow/tutoriel38/common.py

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+import numpy as np
+from tensorflow.keras import layers, models
+import tensorflow as tf
+import io
+
+def write_labels_embs(model, ds, file_embeddings, file_labels):
+    embeddings=model.predict(ds)
+    np.savetxt(file_embeddings, embeddings, delimiter='\t')
+    if file_labels is not None:
+        fichier=io.open(file_labels, 'w', encoding='utf-8')
+        for images, labels in ds:
+            [fichier.write("{:d}\n".format(x)) for x in labels]
+        fichier.close()
+
+def model_embedding(nbr_cc, embeddings_size):
+    entree=layers.Input(shape=(28, 28, 1), dtype=tf.float32)
+
+    result=layers.Conv2D(nbr_cc, 3, activation='relu', padding='same')(entree)
+    result=layers.MaxPool2D()(result)
+    result=layers.Conv2D(nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.MaxPool2D()(result)
+    result=layers.Flatten()(result)
+    result=layers.Dense(embeddings_size, activation=None)(result)
+    embeddings=layers.Lambda(lambda x: tf.math.l2_normalize(x, axis=1))(result)
+
+    model=models.Model(inputs=entree, outputs=embeddings)
+    return model