![]() %timeit benchmarks show the difference in speed: In : %timeit np.dot(x, y) import numpy as npĪssert np.allclose(np.dot(x, y), np.dot(xf, y))Īssert np.allclose(np.dot(x, y), np.dot(xf, yf)) ![]() Xf is F_CONTIGUOUS - and the same relationship for y and yf. There might be as much as a 3x speed up by doing so:īelow, x is the same as xf except that x is C_CONTIGUOUS and It can help to build the arrays in C_CONTIGUOUS-order (at least, if using np.dot). For example, when you compute np.dot(A, B)Īnd A.shape = (n, m) and B.shape = (m, p), then np.dot(A, B) will be an array of shape (n, p). Or at least, paying attention to the shapes can help you know if an operation is sensible. The shape of the arrays can help guide you toward the right NumPy functions to use. Knowing the shape of the arrays helped me understand what your code was doing. Tip: Notice that I left in the comments the shape of all the intermediate arrays. Return partial_j1, partial_j2, partial_b1, partial_b2 My timeit benchmark shows a 6.8x improvement in speed: In : %timeit orig() Here is a runnable example with an alternative implementation ( alt) of your code ( orig). For example, sum2 looks like a constant: sum2 = sparse.beta*(-float(sparse.rho)/rhoest + float(1.0 - sparse.rho) / (1.0 - rhoest) ) Some of the computations in the for-loop do not depend on i and therefore should be lifted outside the loop. Whereas in the for-loop delta3 is a vector, using the vectorized equation delta3 is a matrix. You can compute delta3 for each i all at once: delta3 = -(x-a3)*a3*(1-a3) For example, instead of for i in range(m):ĭelta3 = -(x-a3)*a3* (1 - a3) So, remove the for-loop use "vectorized" equations when possible. That dynamic expressivity, to get better performance, try to limit yourself to The magic is accomplished by giving the objects different _getitem_ methods.īut that expressive power comes at a cost in speed. These hooks give PythonĮxpressive power - indexing for strings means something different than indexingįor dicts for example. Those function callsĪdd up to a significant hinderance to speed. Lookup (such as in like np.dot) involves function calls. What's wrong with a Python loop you ask? Every iteration through the Python loop isĪ call to a next method. In general, you get poorer performance when you call those NumPy function on smaller arrays or scalars in a Python loop. Underlying functions written in C/C++/Fortran. Performance you have to keep in mind that NumPy's speed comes from calling It would be wrong to say "Matlab is always faster than NumPy" or vice Sum2 = beta*(-sparsityParam./rhoest + (1 - sparsityParam). The performance is now numpy: 0.65 vs matlab: 0.25. UPDATE: I have introduced changes in the code following some of the ideas of the responses. Sparse.rho is a tuning parameter, sparse.nodes are the number of nodes in the hidden layer (25), sparse.input (64) the number of nodes in the input layer, theta1 and theta2 are the weight matrices for the first and second layer respectively with dimensions 25圆4 and 64x25, m is equal to 10000, rhoest has a dimension of (25,), x has a dimension of 10000圆4, a3 10000圆4 and a2 10000x25. Is this a normal behaviour? How could I improve the performance in numpy? I will call this code several times later in a minimization problem so this difference leads to several minutes of delay between the implementations. The time matlab takes to complete the task is 0.252454 seconds while numpy 0.973672151566, that is almost four times more. ![]() The code is almost the same, but the performance is very different. I have implemented it in python using numpy and in matlab. I am computing the backpropagation algorithm for a sparse autoencoder.
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