conv1d.py 文件源码

def test_kmax_pooling_time():
  nbatches, nkernels_in, nwords, ndim = 50, 16, 58, 300
  input_shape = (nbatches, nkernels_in, nwords, ndim)

  input = T.tensor4('input')

  k = 1
  f_kmax_argsort = theano.function([input], k_max_pooling(input, k))
  f_kmax_unroll = theano.function([input], _k_max_pooling(input, k))
  f_max = theano.function([input], max_pooling(input))

  image_data = np.random.randn(*input_shape).astype(dtype=np.float64)
  # np.random.shuffle(image_data)
  image_data = image_data.reshape(input_shape)
  # print image_data
  # print 'kmax'
  print 'f_kmax_argsort', timeit.timeit(lambda: f_kmax_argsort(image_data), number=10)
  print 'f_kmax_unroll', timeit.timeit(lambda: f_kmax_unroll(image_data), number=10)
  print 'f_max', timeit.timeit(lambda: f_max(image_data), number=10)