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Theano code to find nearest neighbors to a set of vectors
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| # Implementing lamblin's workaround at https://github.com/Theano/Theano/issues/1399 | |
| # BEFORE: | |
| # bestIndices = T.argmin(s, axis=0) | |
| # Time in Function.fn.__call__: 3.870916e-02s (99.867%) | |
| # <% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Class name> | |
| # 77.7% 77.7% 0.029s 2.93e-02s C 1 1 theano.tensor.basic.MaxAndArgmax | |
| # 7.0% 84.7% 0.003s 2.64e-03s C 1 1 theano.sandbox.cuda.basic_ops.HostFromGpu | |
| # 6.9% 91.6% 0.003s 1.30e-03s C 2 2 theano.sandbox.cuda.basic_ops.GpuFromHost | |
| # 6.2% 97.8% 0.002s 2.35e-03s C 1 1 theano.sandbox.cuda.blas.GpuDot22Scalar | |
| # AFTER: | |
| # bestIndices = T.cast( ( T.arange(n).dimshuffle(0, 'x') * T.cast(T.eq(s, s.min(axis=0, keepdims=True)), 'float32') ).sum(axis=0), 'int32') | |
| # Time in 1 calls to Function.__call__: 1.155996e-02s | |
| # <% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Class name> | |
| # 39.9% 39.9% 0.004s 1.06e-03s C 4 4 theano.sandbox.cuda.basic_ops.GpuCAReduce | |
| # 23.3% 63.2% 0.002s 1.24e-03s C 2 2 theano.sandbox.cuda.basic_ops.GpuFromHost | |
| # 21.8% 85.0% 0.002s 2.32e-03s C 1 1 theano.sandbox.cuda.blas.GpuDot22Scalar | |
| # 14.6% 99.6% 0.002s 3.88e-04s C 4 4 theano.sandbox.cuda.basic_ops.GpuElemwise | |
| import numpy as np | |
| import theano | |
| import theano.tensor as T | |
| def randomMatrix(n, f): | |
| return np.random.normal(size=n*f).astype(np.float32).reshape((n, f)) | |
| n = 5000 # number of candidates | |
| m = 1000 # number of targets | |
| f = 500 # number of features | |
| x = T.matrix('x') # candidates | |
| y = T.matrix('y') # targets | |
| xL2S = T.sum(x*x, axis=-1) # [n] | |
| yL2S = T.sum(y*y, axis=-1) # [m] | |
| xL2SM = T.zeros((m, n)) + xL2S # broadcasting, [m, n] | |
| yL2SM = T.zeros((n, m)) + yL2S # # broadcasting, [n, m] | |
| squaredPairwiseDistances = xL2SM.T + yL2SM - 2.0*T.dot(x, y.T) # [n, m] | |
| np.random.seed(1) | |
| N = randomMatrix(n, f) | |
| M = randomMatrix(m, f) | |
| lamblinsTrick = True | |
| if lamblinsTrick: | |
| s = squaredPairwiseDistances | |
| bestIndices = T.cast( ( T.arange(n).dimshuffle(0, 'x') * T.cast(T.eq(s, s.min(axis=0, keepdims=True)), 'float32') ).sum(axis=0), 'int32') | |
| else: | |
| bestIndices = T.argmin(squaredPairwiseDistances, axis=0) | |
| nearests_fn = theano.function([x, y], bestIndices, profile=True) | |
| print nearests_fn(N, M).sum() |
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| import numpy as np | |
| import theano | |
| import theano.tensor as T | |
| def randomMatrix(n, f): | |
| return np.random.normal(size=n*f).astype(np.float32).reshape((n, f)) | |
| n = 5000 # number of candidates | |
| m = 1000 # number of targets | |
| f = 500 # number of features | |
| x = T.matrix('x') # candidates | |
| y = T.matrix('y') # targets | |
| xL2S = T.sum(x*x, axis=-1) # [n] | |
| yL2S = T.sum(y*y, axis=-1) # [m] | |
| xL2SM = T.zeros((m, n)) + xL2S # broadcasting, [m, n] | |
| yL2SM = T.zeros((n, m)) + yL2S # # broadcasting, [n, m] | |
| squaredPairwiseDistances = xL2SM.T + yL2SM - 2.0*T.dot(x, y.T) # [n, m] | |
| bestIndices = T.argmin(squaredPairwiseDistances, axis=0) | |
| nearests_fn = theano.function([x, y], bestIndices, profile=True) | |
| print nearests_fn(randomMatrix(n, f), randomMatrix(m, f)).shape |
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