tensorflow example

examples/TensorFlow/cifar.py

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+from __future__ import absolute_import, division, print_function, unicode_literals
+import tensorflow as tf
+from tensorflow.keras import datasets, layers, models
+
+(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
+train_images, test_images = train_images / 255.0, test_images / 255.0
+
+model = models.Sequential()
+model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.Flatten())
+model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dense(10, activation='softmax'))
+
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+
+model.summary()
+
+history = model.fit(train_images, train_labels, epochs=10, 
+                    validation_data=(test_images, test_labels))
+
+test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
+
+print(test_acc)
+
+model.save('my_model.keras')
+
+

examples/TensorFlow/readme.md

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+# TensorFlow example
+
+Simple example from the tutorial; make sure to use the datamover to pick up the cifar.py script
+
+```
+sbatch -p f4s --datamover=simple ./runscript.sh
+```

examples/TensorFlow/runscript.sh

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+sudo mount -t cvmfs software.eessi.io /cvmfs/software.eessi.io
+source /cvmfs/software.eessi.io/versions/2023.06/init/bash
+
+ml load TensorFlow
+cd outputs
+
+#python -c "import tensorflow as tf; tf.keras.datasets.cifar10.load_data()"
+python ../cifar.py
+