http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/4d7a8eeb/python/singa/layer.py ---------------------------------------------------------------------- diff --git a/python/singa/layer.py b/python/singa/layer.py index f0024c4..5d087af 100644 --- a/python/singa/layer.py +++ b/python/singa/layer.py @@ -21,7 +21,6 @@ Example usages:: from singa import layer from singa import tensor from singa import device - from singa.model_pb2 import kTrain layer.engine = 'cudnn' # to use cudnn layers dev = device.create_cuda_gpu() @@ -31,7 +30,7 @@ Example usages:: conv.to_device(dev) # move the layer data onto a CudaGPU device x = tensor.Tensor((3, 32, 32), dev) x.uniform(-1, 1) - y = conv.foward(kTrain, x) + y = conv.foward(True, x) dy = tensor.Tensor() dy.reset_like(y)
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/4d7a8eeb/python/singa/tensor.py ---------------------------------------------------------------------- diff --git a/python/singa/tensor.py b/python/singa/tensor.py index 6e59223..57ce563 100644 --- a/python/singa/tensor.py +++ b/python/singa/tensor.py @@ -17,31 +17,31 @@ # ============================================================================= """ Example usage:: - import numpy as np from singa import tensor from singa import device - # create a tensor with shape (2,3), default CppCPU device and float32 - x = tensor.Tensor((2,3)) +# create a tensor with shape (2,3), default CppCPU device and float32 + x = tensor.Tensor((2, 3)) x.set_value(0.4) - # create a tensor from a numpy array - y = tensor.from_numpy((3,3), dtype=np.float32) - y.uniform(-1, 1) +# create a tensor from a numpy array + npy = np.zeros((3, 3), dtype=np.float32) + y = tensor.from_numpy(npy) + + y.uniform(-1, 1) # sample values from the uniform distribution - z = mult(x, y) # gemm -> z of shape (2, 3) + z = tensor.mult(x, y) # gemm -> z of shape (2, 3) - x += z # element-wise addition + x += z # element-wise addition - dev = device.create_cuda_gpu() + dev = device.get_default_device() x.to_device(dev) # move the data to a gpu device - r = relu(x) + r = tensor.relu(x) r.to_host() # move the data back to host cpu - s = r.to_numpy() # tensor -> numpy array, r must be on cpu - + s = tensor.to_numpy(r) # tensor -> numpy array, r must be on cpu There are two sets of tensor functions,
