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46 lines
1.1 KiB
Plaintext
46 lines
1.1 KiB
Plaintext
# this trains LeNet on MNIST with a dropout layer
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# see https://github.com/csiro-mlai/mnist-mpc for data preparation
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program.options_from_args()
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training_samples = MultiArray([50000, 32, 32, 3], sfix)
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training_labels = MultiArray([50000, 10], sint)
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test_samples = MultiArray([10000, 32, 32, 3], sfix)
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test_labels = MultiArray([10000, 10], sint)
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training_labels.input_from(0)
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training_samples.input_from(0)
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test_labels.input_from(0)
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test_samples.input_from(0)
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from Compiler import ml
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tf = ml
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ml.set_n_threads(36)
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layers = [
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tf.keras.layers.Conv2D(20, 5, 1, 'valid', activation='relu'),
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tf.keras.layers.MaxPooling2D(2),
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tf.keras.layers.Conv2D(50, 5, 1, 'valid', activation='relu'),
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tf.keras.layers.MaxPooling2D(2),
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tf.keras.layers.Flatten(),
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tf.keras.layers.Dropout(0.5),
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tf.keras.layers.Dense(500, activation='relu'),
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tf.keras.layers.Dense(10, activation='softmax')
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]
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model = tf.keras.models.Sequential(layers)
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optim = tf.keras.optimizers.Adam(amsgrad=True)
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model.compile(optimizer=optim)
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opt = model.fit(
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training_samples,
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training_labels,
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epochs=10,
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batch_size=128,
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validation_data=(test_samples, test_labels)
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)
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