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Posted to commits@singa.apache.org by wa...@apache.org on 2016/12/02 05:13:17 UTC
[14/17] incubator-singa git commit: SINGA-268 Add IPython notebooks
to the documentation
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/4d7a8eeb/python/singa/layer.py
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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
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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,