please help making a loop faster - Printable Version +- Python Forum (https://python-forum.io) +-- Forum: Python Coding (https://python-forum.io/forum-7.html) +--- Forum: Data Science (https://python-forum.io/forum-44.html) +--- Thread: please help making a loop faster (/thread-22183.html) Pages:
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please help making a loop faster - carla_highlander - Nov-02-2019 I'm working on a forward pass for a neural network. I have written loop within loop within loops. I know there's a way to do this in numpy that is much faster and simpler. def forward_p(x, w, b): """ Inputs: - x: A numpy array of images of shape (N, H, W) - w: A numpy array of weights of shape (M, H, W) - b: A numpy vector of biases of size M Outputs: - cout: a numpy array of shape (N, M) """ N, H, W = x.shape M, _, _ = w.shape cout = np.zeros((N,M)) for ni in range(N): for mi in range(M): cout[ni,mi] = b[mi] for d1 in range(H): for d2 in range(W): cout[ni,mi] += x[ni, d1, d2] * w[mi, d1, d2] return cout RE: please help making a loop faster - paul18fr - Nov-04-2019 Hi We should be able to take advantages of vectorization (using kronecker product - see an example here), but it strongly depends on the size of (N,M,H,W); how many loops are we speaking about? million's or billion's ? the main limitation remains the RAM in my opinion I've never worked on 4 imbricated loops, but it might be interesting to test it. Paul RE: please help making a loop faster - ThomasL - Nov-04-2019 This was my first solution. This will already give you a speed boost. def forward_path_half_vectorized(x, w, b): """ Inputs: - x: A numpy array of images of shape (N, H, W) - w: A numpy array of weights of shape (M, H, W) - b: A numpy vector of biases of size M Outputs: - cout: a numpy array of shape (N, M) """ N, _, _ = x.shape M, _, _ = w.shape cout = np.zeros((N, M)) for ni in range(N): for mi in range(M): cout[ni, mi] = np.sum(x[ni] * w[mi]) return cout + bBut I thought there must be a better way and i found it looking through the numpy documentation. https://docs.scipy.org/doc/numpy/reference/generated/numpy.tensordot.html def forward_path_full_vectorized(x, w, b): """ Inputs: - x: A numpy array of images of shape (N, H, W) - w: A numpy array of weights of shape (M, H, W) - b: A numpy vector of biases of size M Outputs: - cout: a numpy array of shape (N, M) """ return np.tensordot(x, w, axes=([1,2],[1,2])) + bThe full vectorized version is even 90 times faster ! X = np.ones((100, 64, 64), dtype=np.float64) * 0.3 W = np.ones((200, 64, 64), dtype=np.float64) * 1.5 B = np.ones((200), dtype=np.float64) * 3.3 %timeit forward_path_half_vectorized(X, W, B) -> 408 ms ± 2.49 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) %timeit forward_path_full_vectorized(X, W, B) -> 4.63 ms ± 125 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)Life can be easy knowing where to look. :-) RE: please help making a loop faster - mrnapoli - Nov-04-2019 (Nov-04-2019, 02:33 PM)ThomasL Wrote: This was my first solution. This will already give you a speed boost.What if the cout twas a float (single number) type? RE: please help making a loop faster - ThomasL - Nov-04-2019 (Nov-04-2019, 02:57 PM)mrnapoli Wrote: What if the cout twas a float (single number) type?I don´t understand your question. Please provide some more details on your thoughts. RE: please help making a loop faster - mrnapoli - Nov-04-2019 In my case the inputs and outputs expected are as follow: - b_l : A float(single number) - cout: A float (single number) Therefore I receive ValueError: setting an array element with a sequence when running the loop. RE: please help making a loop faster - ThomasL - Nov-04-2019 Why would you use this function under these circumstances? That makes by no means any sense. Do you understand the docstring? Quote: """ RE: please help making a loop faster - mrnapoli - Nov-04-2019 """ Inputs: - x_i: A numpy array of images of shape (H, W) - w_l: A numpy array of weights of shape (H, W) - b_l: A float (single number) Returns: - out: A float (single number) """ N, H, W = x.shape M, _, _ = w.shape out = np.zeros((N,M)) RE: please help making a loop faster - ThomasL - Nov-04-2019 I suggest looking through the documentation: e.g. numpy.dot() e.g. numpy.matmul() RE: please help making a loop faster - mrnapoli - Nov-04-2019 i got it; i went back and read through the documentation. Thanks for the lead. |