completed part 2 implementing gradient descent

python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/gradient.py

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+import numpy as np
+
+
+def sigmoid(x):
+    """
+    Calculate sigmoid
+    """
+    return 1 / (1 + np.exp(-x))
+
+
+def sigmoid_prime(x):
+    """
+    # Derivative of the sigmoid function
+    """
+    return sigmoid(x) * (1 - sigmoid(x))
+
+
+learnrate = 0.5
+x = np.array([1, 2, 3, 4])
+y = np.array(0.5)
+
+# Initial weights
+w = np.array([0.5, -0.5, 0.3, 0.1])
+
+# Calculate one gradient descent step for each weight
+# Note: Some steps have been consolidated, so there are
+# fewer variable names than in the above sample code
+
+# TODO: Calculate the node's linear combination of inputs and weights
+h = np.dot(x, w)
+
+# TODO: Calculate output of neural network (y hat)
+nn_output = sigmoid(h)
+
+# TODO: Calculate error of neural network (y - y hat)
+error = y - nn_output
+
+# TODO: Calculate the error term
+#       Remember, this requires the output gradient, which we haven't
+#       specifically added a variable for.
+error_term = error * sigmoid_prime(h)
+# Note: The sigmoid_prime function calculates sigmoid(h) twice,
+#       but you've already calculated it once. You can make this
+#       code more efficient by calculating the derivative directly
+#       rather than calling sigmoid_prime, like this:
+# error_term = error * nn_output * (1 - nn_output)
+
+# TODO: Calculate change in weights
+del_w = learnrate * error_term * x
+
+print('Neural Network output:')
+print(nn_output)
+print('Amount of Error:')
+print(error)
+print('Change in Weights:')
+print(del_w)