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Diffstat (limited to 'tensorflow/g3doc/how_tos/tool_developers/index.md')
| -rw-r--r-- | tensorflow/g3doc/how_tos/tool_developers/index.md | 28 |
1 files changed, 10 insertions, 18 deletions
diff --git a/tensorflow/g3doc/how_tos/tool_developers/index.md b/tensorflow/g3doc/how_tos/tool_developers/index.md index 89e0867e99..67aad20831 100644 --- a/tensorflow/g3doc/how_tos/tool_developers/index.md +++ b/tensorflow/g3doc/how_tos/tool_developers/index.md @@ -28,8 +28,7 @@ by calling `as_graph_def()`, which returns a `GraphDef` object. The GraphDef class is an object created by the ProtoBuf library from the definition in -[tensorflow/core/framework/graph.proto](https://github.com/tensorflow/tensorflow -/blob/master/tensorflow/core/framework/graph.proto). The protobuf tools parse +[tensorflow/core/framework/graph.proto](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/graph.proto). The protobuf tools parse this text file, and generate the code to load, store, and manipulate graph definitions. If you see a standalone TensorFlow file representing a model, it's likely to contain a serialized version of one of these `GraphDef` objects @@ -37,8 +36,7 @@ saved out by the protobuf code. This generated code is used to save and load the GraphDef files from disk. A good example to look at as we dig into this is -[graph_metrics.py](https://github.com/tensorflow/tensorflow/blob/master/tensorfl -ow/python/tools/graph_metrics.py). This Python script takes a saved graph +[graph_metrics.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/graph_metrics.py). This Python script takes a saved graph definition, and analyzes the model to estimate performance and resource statistics. The code that actually loads the model looks like this: @@ -69,16 +67,14 @@ There are actually two different formats that a ProtoBuf can be saved in. TextFormat is a human-readable form, which makes it nice for debugging and editing, but can get large when there's numerical data like weights stored in it. You can see a small example of that in -[poly5-graph.pbtxt](https://github.com/tensorflow/tensorflow/blob/master/tensorf -low/tensorboard/components/tf-tensorboard/demo/data/poly5-graph.pbtxt). +[poly5-graph.pbtxt](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/components/tf-tensorboard/demo/data/poly5-graph.pbtxt). Binary format files are a lot smaller than their text equivalents, even though they're not as readable for us. In this script, we ask the user to supply a flag indicating whether the input file is binary or text, so we know the right function to call. You can find an example of a large binary file inside the [inception_dec_2015.zip -archive](https://storage.googleapis.com/download.tensorflow.org/models/inception -_dec_2015.zip), as `tensorflow_inception_graph.pb`. +archive](https://storage.googleapis.com/download.tensorflow.org/models/inception_dec_2015.zip), as `tensorflow_inception_graph.pb`. The API itself can be a bit confusing - the binary call is actually `ParseFromString()`, whereas you use a utility function from the `text_format` @@ -104,7 +100,7 @@ single operation along with its input connections. Here are the members of a Every node should have a unique identifier that's not used by any other nodes in the graph. If you don't specify one as you're building a graph using the Python API, one reflecting the name of operation, such as "MatMul", -concatenated with a monotonically increasing number, such as "5", will be +concatenated with a monotonically increasing number, such as "5", will be picked for you. an arbitrary one will be picked for you. The name is used when defining the connections between nodes, and when setting inputs and outputs for the whole graph when it's run. @@ -115,8 +111,7 @@ This defines what operation to run, for example `"Add"`, `"MatMul"`, or `"Conv2D"`. When a graph is run, this op name is looked up in a registry to find an implementation. The registry is populated by calls to the `REGISTER_OP()` macro, like those in -[tensorflow/core/ops/nn_ops.cc](https://github.com/tensorflow/tensorflow/blob/ma -ster/tensorflow/core/ops/nn_ops.cc). +[tensorflow/core/ops/nn_ops.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ops/nn_ops.cc). ### `input` @@ -142,8 +137,7 @@ size of filters for convolutions, or the values of constant ops. Because there can be so many different types of attribute values, from strings, to ints, to arrays of tensor values, there's a separate protobuf file defining the data structure that holds them, in -[tensorflow/core/framework/attr_value.proto](https://github.com/tensorflow/tenso -rflow/blob/master/tensorflow/core/framework/attr_value.proto). +[tensorflow/core/framework/attr_value.proto](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/attr_value.proto). Each attribute has a unique name string, and the expected attributes are listed when the operation is defined. If an attribute isn't present in a node, but it @@ -161,8 +155,7 @@ the file format during training. Instead, they're held in separate checkpoint files, and there are `Variable` ops in the graph that load the latest values when they're initialized. It's often not very convenient to have separate files when you're deploying to production, so there's the -[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflo -w/python/tools/freeze_graph.py) script that takes a graph definition and a set +[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py) script that takes a graph definition and a set of checkpoints and freezes them together into a single file. What this does is load the `GraphDef`, pull in the values for all the variables @@ -178,10 +171,9 @@ the most common problems is extracting and interpreting the weight values. A common way to store them, for example in graphs created by the freeze_graph script, is as `Const` ops containing the weights as `Tensors`. These are defined in -[tensorflow/core/framework/tensor.proto](https://github.com/tensorflow/tensorflo -w/blob/master/tensorflow/core/framework/tensor.proto), and contain information +[tensorflow/core/framework/tensor.proto](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto), and contain information about the size and type of the data, as well as the values themselves. In -Python, you get a `TensorProto` object from a `NodeDef` representing a `Const` +Python, you get a `TensorProto` object from a `NodeDef` representing a `Const` op by calling something like `some_node_def.attr['value'].tensor`. This will give you an object representing the weights data. The data itself |