added uebung3 base

uebungen/uebung3.py

@@ -0,0 +1,42 @@
+import numpy as np
+
+visible = np.ones((10,1))
+hidden = np.ones((5,1))
+
+visible_bias = np.ones((len(visible), 1)) * 0.1
+hidden_bias = np.ones((len(hidden),1)) * 0.1
+
+weights = np.random.rand(len(visible), len(hidden))
+phases = 1
+
+learnrate = 0.2
+
+def sigmoid(x):
+    return 1 / (1 + np.exp(-x))  # Sigmoidfunktion
+
+
+for i in range(1):
+    activation = sigmoid(np.matmul(visible.T, weights) + hidden_bias)
+
+    # 2. Computer outer product vh
+    positive_gradient = np.matmul(visible, hidden.T)
+
+    t = sigmoid(np.matmul(weights, hidden) + visible_bias)
+    reconstructed = sigmoid(np.matmul(weights, hidden) + visible_bias)
+
+    # 4. Computer outer product v'h'
+    negative_gradient = np.matmul(reconstructed.T, activation)
+    # 5. Update weight matrix using gradients
+
+    delta_weights = learnrate * (positive_gradient - negative_gradient)
+
+    # 6. Update bias for visible and hidden layer
+    
+    delta_visible_bias = learnrate * (visible - reconstructed)
+    delta_hidden_bias = learnrate * (hidden - activation)
+
+
+
+
+
+