diff options
Diffstat (limited to 'tensorflow/docs_src/performance')
| -rw-r--r-- | tensorflow/docs_src/performance/performance_guide.md | 2 | ||||
| -rw-r--r-- | tensorflow/docs_src/performance/quantization.md | 8 |
2 files changed, 2 insertions, 8 deletions
diff --git a/tensorflow/docs_src/performance/performance_guide.md b/tensorflow/docs_src/performance/performance_guide.md index a5508ac23e..9ac60024a1 100644 --- a/tensorflow/docs_src/performance/performance_guide.md +++ b/tensorflow/docs_src/performance/performance_guide.md @@ -104,7 +104,7 @@ with tf.device('/cpu:0'): Under some circumstances, both the CPU and GPU can be starved for data by the I/O system. If you are using many small files to form your input data set, you may be limited by the speed of your filesystem. If your training loop runs -faster when using SSDs vs HDDs for storing your input data, you could could be +faster when using SSDs vs HDDs for storing your input data, you could be I/O bottlenecked. If this is the case, you should pre-process your input data, creating a few diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md index a37748d0c9..d050fc5c56 100644 --- a/tensorflow/docs_src/performance/quantization.md +++ b/tensorflow/docs_src/performance/quantization.md @@ -93,7 +93,7 @@ curl http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.t tar xzf /tmp/inceptionv3.tgz -C /tmp/ bazel build tensorflow/tools/graph_transforms:transform_graph bazel-bin/tensorflow/tools/graph_transforms/transform_graph \ - --in_graph=/tmp/classify_image_graph_def.pb \ + --inputs="Mul" --in_graph=/tmp/classify_image_graph_def.pb \ --outputs="softmax" --out_graph=/tmp/quantized_graph.pb \ --transforms='add_default_attributes strip_unused_nodes(type=float, shape="1,299,299,3") remove_nodes(op=Identity, op=CheckNumerics) fold_constants(ignore_errors=true) @@ -108,12 +108,6 @@ versus 91MB). You can still run this model using exactly the same inputs and outputs though, and you should get equivalent results. Here's an example: ```sh -# Note: You need to add the dependencies of the quantization operation to the -# cc_binary in the BUILD file of the label_image program: -# -# //tensorflow/contrib/quantization:cc_ops -# //tensorflow/contrib/quantization/kernels:quantized_ops - bazel build tensorflow/examples/label_image:label_image bazel-bin/tensorflow/examples/label_image/label_image \ --image=<input-image> \ |