Simplified_Stacked-RBM.py

import numpy as np
from keras.datasets import mnist
import matplotlib.pyplot as plt

# Restricted Boltzmann Machine (RBM) Class
class RBM:
    def __init__(self, input_size, output_size):
        self.weights = np.random.randn(input_size, output_size) * 0.01
        self.h_bias = np.zeros(output_size)
        self.v_bias = np.zeros(input_size)

    def train(self, data, epochs, learning_rate):
        # Placeholder for training method
        pass

    def sample_hidden(self, visible):
        # Placeholder for sampling hidden layer
        return np.random.randint(2, size=self.weights.shape[1])

    def sample_visible(self, hidden):
        # Placeholder for sampling visible layer
        return np.random.randint(2, size=self.weights.shape[0])

# Stacked Restricted Boltzmann Machine Class
class StackedRBM:
    def __init__(self, sizes):
        self.rbms = [RBM(sizes[i], sizes[i + 1]) for i in range(len(sizes) - 1)]

    def train(self, data, epochs, learning_rate):
        input_data = data
        for rbm in self.rbms:
            rbm.train(input_data, epochs, learning_rate)
            input_data = np.array([rbm.sample_hidden(sample) for sample in input_data])

# Load MNIST Data
(x_train, _), (_, _) = mnist.load_data()
x_train = x_train.astype('float32') / 255.0
x_train = x_train.reshape(-1, 784)  # Flatten the images

# Initialize and Train Stacked RBM
sizes = [784, 512, 256]  # Example layer sizes
stacked_rbm = StackedRBM(sizes)
stacked_rbm.train(x_train, epochs=10, learning_rate=0.01)

# Visualization (Optional)
# Visualize weights of the first RBM
plt.imshow(stacked_rbm.rbms[0].weights[:, :100], cmap='gray')
plt.show()