expand on skl_nn.MLPClassifier() syntax #55
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colinsauze
reviewed
Dec 13, 2024
| In this example we specify a multi-layer perceptron with 50 hidden nodes, we allow a maximum of 50 iterations to train it, we turn on verbose output to see what's happening, and initialise the random state to 1 so that we always get the same behaviour. | ||
| In scikit-learn's `MLPClassifier`, the `hidden_layer_sizes` parameter specifies the number and size of hidden layers in the neural network. For example, `hidden_layer_sizes=(50,)` creates a single hidden layer with 50 neurons, while `(100, 50)` creates two hidden layers with 100 and 50 neurons, respectively. It’s important to include the trailing comma for a single hidden layer (e.g., `(50,)`) because without it, `(50)` would be interpreted as an integer, not a tuple, and cause an error. The example, `MLPClassifier(hidden_layer_sizes=(50,), max_iter=50, verbose=1, random_state=1)`, builds a neural network with one hidden layer containing 50 neurons, runs for a maximum of 50 iterations, logs training progress, and ensures reproducibility with `random_state=1`. | ||
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| The max_iter parameter in MLPClassifier specifies the maximum number of iterations, not epochs. Since MLPClassifier uses stochastic gradient descent (or its variants), each iteration processes a small random subset of the data (a batch), and the full dataset may not be seen in a single iteration. |
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In this example is the data batched? Given it's small size aren't we reading all of it into memory and processing it with each iteration?
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Yes, I think the data is batched here. The data has 70,000 samples, and here's the note on MLPClassifer's batch size param: https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
batch_size, default=’auto’. When set to “auto”, batch_size=min(200, n_samples).
It might actually be helpful to explicitly add a batch_size argument so the process is clearer for learners.
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