Muscle Gain NN

The Muscle Gain neural network learns and predicts weight gain based on training data.

Xnorm = X / max(x) and Ynorm = Y / max(y) Z^(2) = XW A^(2) = f(Z^(2)) Z^(3) = A^(2)W^(2) Yhat = f(Z^(3))

• Neural Network Visualization

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Data Initialization:

• raw_x and raw_y arrays represent the input features and target values for training.

Weight Initialization:

• Weights for the connections between the input layer and the hidden layer are stored in the syn0 array.
• Weights for the connections between the hidden layer and the output layer are stored in the syn1 array.
• The weight_random_initialization and weight_random_initialization_1d functions initialize these weights randomly.

Normalization:

• The normalize_data_2d function is used to normalize the input data.

Neural Network Computation:

• The program focuses on predicting the mass (train_y) based on the input features (train_x) for a specific example (train_x_eg1 and train_y_eg1).
• The neural network is then computed step by step:
  • multiple_input_multiple_output_nn: Computes the weighted sum for each node in the hidden layer.
  • vector_sigmoid: Applies the sigmoid activation function to the hidden layer nodes.
  • multiple_input_single_output: Computes the weighted sum for the output layer.
  • sigmoid: Applies the sigmoid activation function to the output layer
• The final output (yhat_eg1) represents the predicted mass.

Happy coding!