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comparison tvii/logistic_regression.py @ 28:77f68c241b37

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[logistic regression] propagate
author Jeff Hammel <k0scist@gmail.com>
date Mon, 04 Sep 2017 11:53:23 -0700
parents c52d8173b056
children ae0c345ea09d
comparison
equal deleted inserted replaced
27:22218d90d33f 28:77f68c241b37
41 41
42 Tips: 42 Tips:
43 - Write your code step by step for the propagation. np.log(), np.dot() 43 - Write your code step by step for the propagation. np.log(), np.dot()
44 """ 44 """
45 45
46 m = X.shape[1]
47 46
47
48 # FORWARD PROPAGATION (FROM X TO COST)
48 cost = cost_function(w, b, X, Y) # compute cost 49 cost = cost_function(w, b, X, Y) # compute cost
49 50
50 A = sigmoid(w.T*X + b) # compute activation 51 # BACKWARD PROPAGATION (TO FIND GRADIENT)
51 raise NotImplementedError('TODO') 52 m = X.shape[1]
53 A = sigmoid(np.dot(w.T, X) + b) # compute activation
54 dw = (1./m)*np.dot(X, (A - Y).T)
55 db = (1./m)*np.sum(A - Y)
56
57 # sanity check
58 assert(A.shape[1] == m)
59 assert(dw.shape == w.shape), "dw.shape is {}; w.shape is {}".format(dw.shape, w.shape)
60 assert(db.dtype == float)
61 cost = np.squeeze(cost)
62 assert(cost.shape == ())
63
64 # return gradients
65 grads = {"dw": dw,
66 "db": db}
67 return grads, cost
52 68
53 69
54 def cost_function(w, b, X, Y): 70 def cost_function(w, b, X, Y):
55 """ 71 """
56 Cost function for binary classification 72 Cost function for binary classification