114 lines
3.1 KiB
Python
114 lines
3.1 KiB
Python
import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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def load_data():
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df_orig_train = pd.read_csv('mnist_test_final.csv')
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df_digits = df_orig_train.drop('label', axis=1)
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return df_digits.to_numpy()
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mnist = load_data()
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def sigmoid(x):
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return 1.0 / (1.0 + np.exp(-x)) # Sigmoidfunktion
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class RBM:
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def __init__(self, visible_size: int, hidden_size: int, learnrate: float = 0.1, epochs=20):
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"""__init__ Initializes a newly created Ristricted Bolzmann Machine.
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Args:
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visible_size (int): amount of neurons inside the visible layer
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hidden_size (int): amount of neurons inside the hidden layer
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learnrate (float, optional): learnrate eta in [0;1]. Defaults to 0.1.
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epochs (int, optional): training epochs. Defaults to 20.
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"""
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self.learnrate = learnrate
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self.visible_size = visible_size
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self.hidden_size = hidden_size
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self.epochs = epochs
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self.reset()
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def reset(self):
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# initialize/reset learnable attributes
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self.weights = np.random.randn(self.visible_size, self.hidden_size)
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self.visible_bias = np.zeros(self.visible_size) * 0.1
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self.hidden_bias = np.zeros(self.hidden_size) * 0.1
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def activate(self, v0):
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return sigmoid(np.matmul(v0.T, self.weights) + self.hidden_bias)
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def reactivate(self, h0):
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return sigmoid(np.matmul(self.weights, h0.T) + self.visible_bias)
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def contrastive_divergence(self, v0, h0, v1, h1):
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# calculate gradients
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postive_gradient = np.outer(v0, h0)
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negative_gradient = np.outer(v1, h0)
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# Adjust weights by delta
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self.weights += self.learnrate * (postive_gradient - negative_gradient)
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# Adjust biases by delta
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self.visible_bias += self.learnrate * (v0 - v1)
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self.hidden_bias += self.learnrate * (h0 - h1)
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def train(self, v0):
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for _ in range(self.epochs):
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# activate hidden layer
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h0 = self.activate(v0)
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# reactivate visible layer
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v1 = self.reactivate(h0)
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#activate next hidden layer
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h1 = self.activate(v1)
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# Adjust weights
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self.contrastive_divergence(v0, h0, v1, h1)
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def run(self, v0):
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# activate hidden layer
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h0 = self.activate(v0)
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v1 = self.reactivate(h0)
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return h0, v1
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rbm = RBM(28**2, 256, 0.1, epochs=3)
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def validate(idx):
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#flatten and normalize mnist data
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test = mnist[idx].flatten()/255
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# train bolzmann machine and run
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rbm.train(test)
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(hid, out) = rbm.run(test)
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return (hid.reshape((16, 16)), out.reshape((28, 28)))
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# plot results
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rows, columns = (4, 6)
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fig = plt.figure(figsize=(10, 7))
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for i in range((rows * columns)):
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if i % 2 == 0:
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(hid, out) = validate(i)
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# hidden layer
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fig.add_subplot(rows, columns, i+1)
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plt.imshow(hid, cmap='gray')
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plt.axis('off')
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# visible layer
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fig.add_subplot(rows, columns, i+2)
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plt.imshow(out, cmap='gray')
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plt.axis('off')
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plt.show()
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