Backward Propagation - Gradient error [Python]

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I am working through Andrew Ng new deep learning Coursera course, week2.

We are supposed to implement a logistic regression algorithm.
I am stuck at gradient code ( dw ) - giving me a syntax error.

The algorithm is as follows:

import numpy as np

def propagate(w, b, X, Y):
    m = X.shape[1]

    A = sigmoid(np.dot(w.T,X) + b )  # compute activation
    cost = -(1/m)*(np.sum(np.multiply(Y,np.log(A)) + np.multiply((1-Y),np.log(1-A)), axis=1)    

    dw =(1/m)*np.dot(X,(A-Y).T)
    db = (1/m)*(np.sum(A-Y))
    assert(dw.shape == w.shape)
    assert(db.dtype == float)
    cost = np.squeeze(cost)
    assert(cost.shape == ())

    grads = {"dw": dw,
             "db": db}

    return grads, cost

Any ideas why I keep on getting this syntax error?

File "<ipython-input-1-d104f7763626>", line 32
    dw =(1/m)*np.dot(X,(A-Y).T)
     ^
SyntaxError: invalid syntax
deep-learning gradient machine-learning python
3 Answers

There is one missing close bracket ")" in your 'cost' line which generating the Syntax Error in your next line.

Try this -

cost = -(1/m) * sum(np.multiply(Y, np.log(A)) + np.multiply((1 - Y), np.log(1 - A)))
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