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Tensorflow/tutoriel23/README.md

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+# Tutoriel tensorflow
+## Tensorflow 2.0
+
+La vidéo de ce tutoriel est disponible à l'adresse suivante: https://www.youtube.com/watch?v=-lz3zHRTfow
+

Tensorflow/tutoriel23/mnist.py

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

Tensorflow/tutoriel23/test_vitesse_1.py

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+import tensorflow as tf
+import numpy as np
+from tensorflow.keras import layers
+
+mnist = tf.keras.datasets.mnist
+
+(x_train, y_train),(x_test, y_test)=mnist.load_data()
+batch_size=64
+epochs=5
+
+(x_train, y_train), (x_test, y_test)=mnist.load_data()
+x_train=(x_train.reshape(-1, 28, 28, 1)/255).astype(np.float32)
+x_test=(x_test.reshape(-1, 28, 28, 1)/255).astype(np.float32)
+
+train_ds=tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size)
+test_ds=tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batch_size)
+
+model = tf.keras.models.Sequential([
+    layers.Conv2D(64,  3, strides=2, activation='relu'),
+    layers.BatchNormalization(),
+    layers.Conv2D(128,  3, strides=2, activation='relu'),
+    layers.BatchNormalization(),
+    layers.Flatten(),
+    layers.Dense(512, activation='relu'),
+    layers.BatchNormalization(),
+    layers.Dense(10, activation='softmax')
+])
+
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+
+model.fit(x_train, y_train, epochs=epochs)
+#model.evaluate(x_test, y_test)

Tensorflow/tutoriel23/test_vitesse_2.py

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+import tensorflow as tf
+from tensorflow.keras import layers, models
+import numpy as np
+from sklearn.utils import shuffle
+
+batch_size=16
+epochs=5
+
+def stl10(path):
+    labels=['avion', 'oiseau', 'voiture', 'chat', 'cerf', 'chien', 'cheval', 'singe', 'bateau', 'camion']
+    train_images=np.fromfile(path+"/train_X.bin", dtype=np.uint8).reshape(-1, 3, 96, 96).transpose(0, 2, 3, 1)
+    train_labels=np.fromfile(path+"/train_y.bin", dtype=np.uint8)-1
+    train_images, train_labels=shuffle(train_images, train_labels)
+    test_images=np.fromfile(path+"/test_X.bin", dtype=np.uint8).reshape(-1, 3, 96, 96).transpose(0, 2, 3, 1)
+    test_labels=np.fromfile(path+"/test_y.bin", dtype=np.uint8)-1
+    return labels, train_images, train_labels, test_images, test_labels
+
+labels, x_train, y_train, x_test, y_test=stl10("stl10_binary")
+x_train=(x_train/255).astype(np.float32)
+x_test=(x_test/255).astype(np.float32)
+
+train_ds=tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size)
+test_ds=tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batch_size)
+
+model=models.Sequential([
+    layers.Conv2D(256, 5, strides=1),
+    layers.BatchNormalization(),
+    layers.Activation('relu'),
+    layers.MaxPool2D(pool_size=2, strides=2),
+
+    layers.Conv2D(512, 5, strides=1),
+    layers.BatchNormalization(),
+    layers.Activation('relu'),
+    layers.MaxPool2D(pool_size=2, strides=2),
+
+    layers.Conv2D(1024, 5, strides=1),
+    layers.BatchNormalization(),
+    layers.Activation('relu'),
+    layers.MaxPool2D(pool_size=2, strides=2),
+
+    layers.Conv2D(2048, 5, strides=1),
+    layers.BatchNormalization(),
+    layers.Activation('relu'),
+    layers.MaxPool2D(pool_size=2, strides=2),
+
+    layers.Flatten(),
+    layers.Dense(1024, activation='relu'),
+    layers.BatchNormalization(),
+    layers.Dense(10, activation='softmax')
+])
+
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+
+model.fit(x_train, y_train, epochs=epochs)
+#model.evaluate(x_test, y_test)
+#model.summary()