30 lines
963 B
Python
30 lines
963 B
Python
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import numpy as np
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import tensorflow as tf
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import tensorflow_addons as tfa
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import common
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batch_size=64
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nbr_cc=64 # Nombre de cartes de caracteristique du modele
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embeddings_size=256 # Taille du vecteur embedding (sortie du réseau de neurone)
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(x_train, y_train),(x_test, y_test)=tf.keras.datasets.mnist.load_data()
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train_dataset=(x_train.reshape(-1, 28, 28, 1)/255).astype(np.float32)
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test_dataset=(x_test.reshape(-1, 28, 28, 1)/255).astype(np.float32)
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train_ds=tf.data.Dataset.from_tensor_slices((train_dataset, y_train)).batch(batch_size)
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test_ds=tf.data.Dataset.from_tensor_slices((test_dataset, y_test)).batch(batch_size)
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model=common.model_embedding(nbr_cc, embeddings_size)
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common.write_labels_embs(model, test_ds, 'embeddings1.tsv', 'labels.tsv')
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model.compile(
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optimizer=tf.keras.optimizers.Adam(1E-3),
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loss=tfa.losses.TripletSemiHardLoss())
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model.fit(train_ds, epochs=5)
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common.write_labels_embs(model, test_ds, 'embeddings2.tsv', None)
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