Learn Neural Network with scikit-learn | Neural Network from Scratch

Working with neural networks can be quite tricky, especially if you're trying to build them from scratch. Instead of manually coding algorithms and formulas, you can use ready-made tools such as the sklearn library.

Benefits of Using sklearn

Ease of use: you don't have to dive deep into the details of each algorithm. You can simply use ready-made methods and classes;
Optimization: the sklearn library is optimized for performance, which can reduce the training time of your model;
Extensive documentation: sklearn provides extensive documentation with usage examples, which can greatly speed up the learning process;
Compatibility: sklearn integrates well with other popular Python libraries such as numpy, pandas and matplotlib.

Perceptron in sklearn

To create the same model as in this section, you can use the MLPClassifier class from the sklearn library. Its key parameters are as follows:

max_iter: defines the maximum number of epochs for training;
hidden_layer_sizes: specifies the number of neurons in each hidden layer as a tuple;
learning_rate_init: sets the learning rate for weight updates.

Note

By default, MLPClassifier uses the ReLU activation function for hidden layers. For binary classification, the output layer is essentially the same as the one you implemented.

For example, with a single line of code, you can create a perceptron with two hidden layers of 10 neurons each, using at most 100 epochs for training and a learning rate of 0.5:

from sklearn.neural_network import MLPClassifier

model = MLPClassifier(max_iter=100, hidden_layer_sizes=(10,10), learning_rate_init=0.5)

Note

Neural networks in sklearn determine the number of inputs and outputs based on the data they are trained on. Therefore, there is no need to set them manually.

As with our implementation, training the model simply involves calling the fit() method:

model.fit(X_train, y_train)

To get the predicted labels (e.g., on the test set), all you have to do is call the predict() method:

y_pred = model.predict(X_test)

Task

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Your goal is to recreate, train, and evaluate a perceptron model using the scikit-learn library, following the same structure as the custom implementation built earlier.

Follow these steps carefully:

Initialize the perceptron using the MLPClassifier class:
- Set the number of training epochs to 100 using max_iter=100;
- Use two hidden layers, each containing 6 neurons (hidden_layer_sizes=(6, 6));
- Set the learning rate to 0.01 using learning_rate_init=0.01;
- Add random_state=10 for reproducibility.
Train the model on the training dataset using the .fit() method.
Obtain predictions for all examples in the test set using the .predict() method.
Evaluate performance by computing the model’s accuracy on the test set with the accuracy_score() function.

Solution

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Section 2. Chapter 13

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Benefits of Using sklearn

Ease of use: you don't have to dive deep into the details of each algorithm. You can simply use ready-made methods and classes;
Optimization: the sklearn library is optimized for performance, which can reduce the training time of your model;
Extensive documentation: sklearn provides extensive documentation with usage examples, which can greatly speed up the learning process;
Compatibility: sklearn integrates well with other popular Python libraries such as numpy, pandas and matplotlib.

Perceptron in sklearn

To create the same model as in this section, you can use the MLPClassifier class from the sklearn library. Its key parameters are as follows:

max_iter: defines the maximum number of epochs for training;
hidden_layer_sizes: specifies the number of neurons in each hidden layer as a tuple;
learning_rate_init: sets the learning rate for weight updates.

Note

By default, MLPClassifier uses the ReLU activation function for hidden layers. For binary classification, the output layer is essentially the same as the one you implemented.

For example, with a single line of code, you can create a perceptron with two hidden layers of 10 neurons each, using at most 100 epochs for training and a learning rate of 0.5:

from sklearn.neural_network import MLPClassifier

model = MLPClassifier(max_iter=100, hidden_layer_sizes=(10,10), learning_rate_init=0.5)

Note

Neural networks in sklearn determine the number of inputs and outputs based on the data they are trained on. Therefore, there is no need to set them manually.

As with our implementation, training the model simply involves calling the fit() method:

model.fit(X_train, y_train)

To get the predicted labels (e.g., on the test set), all you have to do is call the predict() method:

y_pred = model.predict(X_test)

Task

Swipe to start coding

Your goal is to recreate, train, and evaluate a perceptron model using the scikit-learn library, following the same structure as the custom implementation built earlier.

Follow these steps carefully:

Initialize the perceptron using the MLPClassifier class:
- Set the number of training epochs to 100 using max_iter=100;
- Use two hidden layers, each containing 6 neurons (hidden_layer_sizes=(6, 6));
- Set the learning rate to 0.01 using learning_rate_init=0.01;
- Add random_state=10 for reproducibility.
Train the model on the training dataset using the .fit() method.
Obtain predictions for all examples in the test set using the .predict() method.
Evaluate performance by computing the model’s accuracy on the test set with the accuracy_score() function.

Solution

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Everything was clear?

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Section 2. Chapter 13

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