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Introduction to Neural Networks

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Neural Network with scikit-learn

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

  1. 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;

  2. Optimization: the sklearn library is optimized for performance, which can reduce the training time of your model;

  3. Extensive documentation:sklearn provides extensive documentation with usage examples, which can greatly speed up the learning process;

  4. 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
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:

python
Note
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:

python

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

python
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Swipe to start coding

Your goal is to create, train, and evaluate a perceptron with the same structure as the one you previously implemented, but using the sklearn library:

  1. Initialize a perceptron with 100 training epochs, two hidden layers of 6 neurons each, and a learning rate of 0.01 (set the parameters in this exact order).
  2. Train the model on the training data.
  3. Obtain predictions on the test set.
  4. Compute the accuracy of the model on the test set.

Lösning

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book
Neural Network with scikit-learn

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

  1. 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;

  2. Optimization: the sklearn library is optimized for performance, which can reduce the training time of your model;

  3. Extensive documentation:sklearn provides extensive documentation with usage examples, which can greatly speed up the learning process;

  4. 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
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:

python
Note
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:

python

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

python
Uppgift

Swipe to start coding

Your goal is to create, train, and evaluate a perceptron with the same structure as the one you previously implemented, but using the sklearn library:

  1. Initialize a perceptron with 100 training epochs, two hidden layers of 6 neurons each, and a learning rate of 0.01 (set the parameters in this exact order).
  2. Train the model on the training data.
  3. Obtain predictions on the test set.
  4. Compute the accuracy of the model on the test set.

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

close

Awesome!

Completion rate improved to 4

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