ml-finance-python

restricted_boltzmann_machine.py

(1821B)

 import logging
 
 import numpy as np
 from sklearn import datasets
 from sklearn.datasets import fetch_mldata
 import matplotlib.pyplot as plt
 
 from mlfromscratch.unsupervised_learning import RBM
 
 logging.basicConfig(level=logging.DEBUG)
 
 def main():
 
     mnist = fetch_mldata('MNIST original')
 
     X = mnist.data / 255.0
     y = mnist.target
 
     # Select the samples of the digit 2
     X = X[y == 2]
 
     # Limit dataset to 500 samples
     idx = np.random.choice(range(X.shape[0]), size=500, replace=False)
     X = X[idx]
 
     rbm = RBM(n_hidden=50, n_iterations=200, batch_size=25, learning_rate=0.001)
     rbm.fit(X)
 
     # Training error plot
     training, = plt.plot(range(len(rbm.training_errors)), rbm.training_errors, label="Training Error")
     plt.legend(handles=[training])
     plt.title("Error Plot")
     plt.ylabel('Error')
     plt.xlabel('Iterations')
     plt.show()
 
     # Get the images that were reconstructed during training
     gen_imgs = rbm.training_reconstructions
 
     # Plot the reconstructed images during the first iteration
     fig, axs = plt.subplots(5, 5)
     plt.suptitle("Restricted Boltzmann Machine - First Iteration")
     cnt = 0
     for i in range(5):
         for j in range(5):
             axs[i,j].imshow(gen_imgs[0][cnt].reshape((28, 28)), cmap='gray')
             axs[i,j].axis('off')
             cnt += 1
     fig.savefig("rbm_first.png")
     plt.close()
 
     # Plot the images during the last iteration
     fig, axs = plt.subplots(5, 5)
     plt.suptitle("Restricted Boltzmann Machine - Last Iteration")
     cnt = 0
     for i in range(5):
         for j in range(5):
             axs[i,j].imshow(gen_imgs[-1][cnt].reshape((28, 28)), cmap='gray')
             axs[i,j].axis('off')
             cnt += 1
     fig.savefig("rbm_last.png")
     plt.close()
 
 
 if __name__ == "__main__":
     main()