add worksheets

Author: StoneT2000

Fall20/NeuralNetworks1/worksheet/averagenn.py

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+import numpy as np
+
+# fill in this neural network and activation function to calculate the of average of 5 values using numpy and vectorization
+def activation(z):
+    raise NotImplementedError
+
+def average5NN(x):
+    # this should be a simple 2 layer network, one input layer and one output layer, figure out how many neurons are in each layer, the weights, and biases! 
+
+    # This should take input column vector x and apply an appropriate weight matrix and add a bias, apply the activation and then return the result
+
+    weights = np.array([]) # fill me in!
+
+    bias = 100 # fix me!
+
+    weighted_input = 0 # fill me in!
+
+    raise NotImplementedError
+
+
+# Your network works if you don't get any errors!
+assert average5NN(np.array([1, 1, 1, 1, 1])) == 1
+assert average5NN(np.array([1, -1, 1, -1, 0])) == 0
+assert average5NN(np.array([100, 200, 300, 400, -400])) == 1.2

Fall20/NeuralNetworks1/worksheet/squarenn.py

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+# fill in this neural network and activation function to calculate the square of the sum of 2 inputs
+def activation(z):
+    raise NotImplementedError
+
+def squareNN(x1, x2):
+    # this should be a simple 2 layer network, one input layer and one output layer, figure out how many neurons are in each layer, the weights, and biases!
+    # This should take x1 and x2 and return the square of the sum of the 2 inputs, but obviously in neural network style!
+    raise NotImplementedError
+
+
+# Your network works if you don't get any errors!
+assert squareNN(1, 1) == 4
+assert squareNN(-1, 1) == 0
+assert squareNN(10, 2.5) == 156.25
+assert squareNN(0, 0) == 0