abgabe ready

uebungen/aufgabe3/uebung3.py

@@ -20,58 +20,61 @@ def sigmoid(x):
 class RBM:
 
     def __init__(self, visible_size: int, hidden_size: int, learnrate: float = 0.1, epochs: int = 20):
-        """__init__ Initializes a newly created Restricted Bolzmann Machine.
+        """__init__ Initialisiere der RBM.
 
         Args:
-            visible_size (int): amount of neurons inside the visible layer
+            visible_size (int): anzahl neuronen sichtbare schicht
-            hidden_size (int): amount of neurons inside the hidden layer
+            hidden_size (int): anzahl neuronen sichtbare schicht
-            learnrate (float, optional): learnrate eta in [0;1]. Defaults to 0.1.
+            learnrate (float, optional): learnrate eta in [0;1]. Default als 0.1.
-            epochs (int, optional): training epochs. Defaults to 20.
+            epochs (int, optional): training epochs. Defaults als 20.
         """
         self.learnrate = learnrate
         self.visible_size = visible_size
         self.hidden_size = hidden_size
         self.epochs = epochs
 
-        # initialize/reset learnable attributes
+        # Initialisieren lernbarer Attribute
         self.weights = np.random.randn(self.visible_size, self.hidden_size)
         self.visible_bias = np.zeros(self.visible_size) * 0.1
         self.hidden_bias = np.zeros(self.hidden_size) * 0.1
 
     def activate(self, v0):
-        return sigmoid(np.matmul(v0.T, self.weights) + self.hidden_bias)
+        return self.sample(sigmoid(np.matmul(v0.T, self.weights) + self.hidden_bias))
 
     def reactivate(self, h0):
-        return sigmoid(np.matmul(self.weights, h0.T) + self.visible_bias)
+        return self.sample(sigmoid(np.matmul(self.weights, h0.T) + self.visible_bias))
+
+    def sample(self, a):
+        return np.where(a > np.random.uniform(0, 1, a.shape), 1, 0)
 
     def contrastive_divergence(self, v0, h0, v1, h1):
-        # calculate gradients
+        # Gradient
         postive_gradient = np.outer(v0, h0)
-        negative_gradient = np.outer(v1, h0)
+        negative_gradient = np.outer(v1, h1)
 
-        # Adjust weights by delta
+        # Gewichte per delta anpassen
         self.weights += self.learnrate * (postive_gradient - negative_gradient)
 
-        # Adjust biases by delta
+        # Biases per delta anpassen
         self.visible_bias += self.learnrate * (v0 - v1)
         self.hidden_bias += self.learnrate * (h0 - h1)
 
     def train(self, v0):
         for _ in range(self.epochs):
-            # activate hidden layer
+            # versteckte schichten aktivieren
             h0 = self.activate(v0)
 
-            # reactivate visible layer
+            # Reaktivieren sichtbarer Schichten
             v1 = self.reactivate(h0)
 
-            # activate next hidden layer
+            # Aktivieren nächster versteckter Schicht
             h1 = self.activate(v1)
 
-            # Adjust weights
+            # Gewichte anpassen
             self.contrastive_divergence(v0, h0, v1, h1)
 
     def run(self, v0):
-        # activate hidden layer
+        # Aktivieren der Schichten
         h0 = self.activate(v0)
         v1 = self.reactivate(h0)
 
@@ -81,22 +84,24 @@ class RBM:
 rbm = RBM(28 ** 2, 100, 0.2, epochs=2)
 
 for i in range(100, 600):
-    # normalize mnist data and train
+    # Normalisieren der mnist daten und trainieren
     number = mnist[i] / 255
     rbm.train(number)
 
-# plot results
+# Ergebnisse plotten
 rows, columns = (9, 9)
 
 fig = plt.figure(figsize=(10, 7))
-fig.canvas.manager.set_window_title("Reconstruction of MNIST Numbers using a Restricted Boltzmann Machine")
+fig.canvas.manager.set_window_title(
+    "Rekonstruktion des MNIST Datensatzes mit einer Restricted Boltzmann Machine")
 
 for i in range((rows * columns)):
     if i % 3 == 0:
         number = mnist[i] / 255
         (hidden, visible) = rbm.run(number)
 
-        results = [hidden.reshape((10, 10)), visible.reshape((28, 28)), number.reshape((28, 28))]
+        results = [hidden.reshape((10, 10)), visible.reshape(
+            (28, 28)), number.reshape((28, 28))]
 
         for j, item in enumerate(results):
             fig.add_subplot(rows, columns, i + j + 1)