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