75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
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import tensorflow as tf
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from tensorflow.keras import layers, models
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import numpy as np
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import time
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batch_size=64
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nbr_entrainement=5
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(x_train, y_train), (x_test, y_test)=tf.keras.datasets.mnist.load_data()
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x_train=(x_train.reshape(-1, 28, 28, 1)/255).astype(np.float32)
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x_test=(x_test.reshape(-1, 28, 28, 1)/255).astype(np.float32)
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train_ds=tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size)
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test_ds=tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batch_size)
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model = models.Sequential([
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layers.Conv2D(64, 3, strides=2, activation='relu'),
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layers.BatchNormalization(),
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layers.Conv2D(128, 3, strides=2, activation='relu'),
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layers.BatchNormalization(),
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layers.Flatten(),
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layers.Dense(512, activation='relu'),
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layers.BatchNormalization(),
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layers.Dense(10, activation='softmax')
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])
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optimizer=tf.keras.optimizers.Adam()
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loss_object=tf.keras.losses.SparseCategoricalCrossentropy()
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train_loss=tf.keras.metrics.Mean()
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train_accuracy=tf.keras.metrics.SparseCategoricalAccuracy()
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test_loss=tf.keras.metrics.Mean()
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test_accuracy=tf.keras.metrics.SparseCategoricalAccuracy()
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@tf.function
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def train_step(images, labels):
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with tf.GradientTape() as tape:
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predictions=model(images)
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loss=loss_object(labels, predictions)
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gradients=tape.gradient(loss, model.trainable_variables)
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optimizer.apply_gradients(zip(gradients, model.trainable_variables))
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train_loss(loss)
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train_accuracy(labels, predictions)
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def train(train_ds, nbr_entrainement):
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for entrainement in range(nbr_entrainement):
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start=time.time()
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for images, labels in train_ds:
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train_step(images, labels)
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message='Entrainement {:04d}, loss: {:6.4f}, accuracy: {:7.4f}%, temps: {:7.4f}'
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print(message.format(entrainement+1,
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train_loss.result(),
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train_accuracy.result()*100,
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time.time()-start))
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train_loss.reset_states()
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train_accuracy.reset_states()
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def test(test_ds):
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start=time.time()
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for test_images, test_labels in test_ds:
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predictions=model(test_images)
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t_loss=loss_object(test_labels, predictions)
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test_loss(t_loss)
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test_accuracy(test_labels, predictions)
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message='Loss: {:6.4f}, accuracy: {:7.4f}%, temps: {:7.4f}'
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print(message.format(test_loss.result(),
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test_accuracy.result()*100,
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time.time()-start))
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print("Entrainement")
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train(train_ds, nbr_entrainement)
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print("Jeu de test")
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test(test_ds)
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