completed part 2 implementing gradient descent

python/Deep Learning/Implementing Gradient Descent/Backpropagation/backprop.py

@@ -0,0 +1,48 @@
+import numpy as np
+
+
+def sigmoid(x):
+    """
+    Calculate sigmoid
+    """
+    return 1 / (1 + np.exp(-x))
+
+
+x = np.array([0.5, 0.1, -0.2])
+target = 0.6
+learnrate = 0.5
+
+weights_input_hidden = np.array([[0.5, -0.6],
+                                 [0.1, -0.2],
+                                 [0.1, 0.7]])
+
+weights_hidden_output = np.array([0.1, -0.3])
+
+# Forward pass
+hidden_layer_input = np.dot(x, weights_input_hidden)
+hidden_layer_output = sigmoid(hidden_layer_input)
+
+output_layer_in = np.dot(hidden_layer_output, weights_hidden_output)
+output = sigmoid(output_layer_in)
+
+# Backwards pass
+# TODO: Calculate output error
+error = target - output
+
+# TODO: Calculate error term for output layer
+output_error_term = error * output * (1 - output)
+
+# TODO: Calculate error term for hidden layer
+hidden_error_term = np.dot(output_error_term, weights_hidden_output
+                           * hidden_layer_output * (1 - hidden_layer_output))
+
+# TODO: Calculate change in weights for hidden layer to output layer
+delta_w_h_o = learnrate * output_error_term * hidden_layer_output
+
+# TODO: Calculate change in weights for input layer to hidden layer
+delta_w_i_h = learnrate * hidden_error_term * x[:, None]
+
+print('Change in weights for hidden layer to output layer:')
+print(delta_w_h_o)
+print('Change in weights for input layer to hidden layer:')
+print(delta_w_i_h)