@@ -535,36 +535,36 @@ Running the Code
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The user can then run the code by calling:
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.. code-block:: bash
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-
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python code/convolutional_mlp.py
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- The following output was obtained with the default parameters on a Xeon E5450
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- CPU clocked at 3.00GHz and using flags 'floatX=float32':
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+ The following output was obtained with the default parameters on a Core i7-2600K
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+ CPU clocked at 3.40GHz and using flags 'floatX=float32':
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.. code-block:: bash
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Optimization complete.
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- Best validation score of 0.910000 % obtained at iteration 16099 ,with test
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- performance 0.930000 %
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- The code for file convolutional_mlp.py ran for 755.32m
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+ Best validation score of 0.910000 % obtained at iteration 17800 ,with test
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+ performance 0.920000 %
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+ The code for file convolutional_mlp.py ran for 380.28m
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Using a GeForce GTX 285, we obtained the following:
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.. code-block:: bash
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Optimization complete.
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- Best validation score of 0.910000 % obtained at iteration 20099 ,with test
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+ Best validation score of 0.910000 % obtained at iteration 15500 ,with test
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performance 0.930000 %
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- The code for file convolutional_mlp.py ran for 47.96m
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+ The code for file convolutional_mlp.py ran for 46.76m
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And similarly on a GeForce GTX 480:
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.. code-block:: bash
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Optimization complete.
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- Best validation score of 0.910000 % obtained at iteration 18499 ,with test
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- performance 0.910000 %
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- The code for file convolutional_mlp.py ran for 43.09m
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+ Best validation score of 0.910000 % obtained at iteration 16400 ,with test
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+ performance 0.930000 %
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+ The code for file convolutional_mlp.py ran for 32.52m
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Note that the discrepancies in validation and test error (as well as iteration
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count) are due to different implementations of the rounding mechanism in
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