Initial commit

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()