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| author |  A. Unique TensorFlower <nobody@tensorflow.org> | 2016-03-23 13:13:09 -0800 |
|---|---|---|
| committer |  TensorFlower Gardener <gardener@tensorflow.org> | 2016-03-23 14:19:16 -0700 |
| commit | f6e7205516eeb54d3e85b5d283c2d7a387ec81bc (patch) | |
| tree | d0c59643de015bf4f3b9023f7150addd3732dfe5 | |
| parent | 99e298a84775fcfe91ce0e2d40566d4bd00f53f9 (diff) | |
Add dependency for contrib kernels.
Change: 117967381
| -rw-r--r-- | .gitmodules | 2 | ||||
| -rw-r--r-- | tensorflow/contrib/BUILD | 4 | ||||
| -rw-r--r-- | tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py | 8 |
3 files changed, 8 insertions, 6 deletions
diff --git a/.gitmodules b/.gitmodules index 1b17ea57b9..0edca21239 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +1,3 @@ [submodule "google/protobuf"] path = google/protobuf - url = https://github.com/google/protobuf.git + url = https://github.googlesource.com/google/protobuf.git diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD index 708cfddefc..ece038a572 100644 --- a/tensorflow/contrib/BUILD +++ b/tensorflow/contrib/BUILD @@ -11,7 +11,7 @@ py_library( name = "contrib_py", srcs = glob(["**/*.py"]), srcs_version = "PY2AND3", - visibility = ["//tensorflow:internal"], + visibility = ["//visibility:public"], deps = [ "//tensorflow/contrib/ctc:ctc_py", "//tensorflow/contrib/distributions:distributions_py", @@ -28,7 +28,7 @@ cc_library( name = "contrib_kernels", visibility = ["//visibility:public"], deps = [ - "//tensorflow/contrib/linear_optimizer/kernels:sdca_ops", + "//tensorflow/contrib/linear_optimizer:sdca_op_kernels", ], ) diff --git a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py index a54b631ef2..06c986beb3 100644 --- a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py +++ b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py @@ -224,14 +224,16 @@ class SdcaModel(object): fv = array_ops.reshape(st_i.values, [-1]) # TODO(sibyl-Aix6ihai): This does not work if examples have empty features. result += math_ops.segment_sum( - math_ops.mul( - array_ops.gather(sv, fi), fv), array_ops.reshape(ei, [-1])) + math_ops.mul(array_ops.gather(sv, fi), fv), ei) dense_features = self._convert_n_to_tensor(examples['dense_features']) dense_variables = self._convert_n_to_tensor(self._variables[ 'dense_features_weights']) + for i in range(len(dense_variables)): result += dense_features[i] * dense_variables[i] - return result + + # Reshaping to allow shape inference at graph construction time. + return array_ops.reshape(result, [-1]) def predictions(self, examples): """Add operations to compute predictions by the model. |