128、TensorFlow元数据MetaData

#tf.Session.run也接收一个可选的参数options
#能够让你来配置训练时的参数
#run_metadata参数让你能够收集关于训练的元信息
#列如你可以使用这些可选项来追踪执行的信息
import tensorflow as tf
y = tf.matmul([[37.0, -23.0], [1.0, 4.0]], tf.random_uniform([2, 2]))
with tf.Session() as sess:
    # Define options for the sess.run() call
    options = tf.RunOptions()
    options.output_partition_graphs = True
    options.trace_level = tf.RunOptions.FULL_TRACE

    # Define a container for  the returned metadata
    metadata = tf.RunMetadata()

    sess.run(y, options=options, run_metadata=metadata)

    # Print the subgraphs that executed on each device
    print(metadata.partition_graphs)

    # Print the timings of each operation that executed
    print(metadata.step_stats)
    

下面是输出的结果:

2018-02-17 11:12:58.518912: I C:\tf_jenkins\workspace\rel-win\M\windows\PY\35\tensorflow\core\platform\cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
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  op: "Const"
  device: "/job:localhost/replica:0/task:0/device:CPU:0"
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    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
          dim {
            size: 2
          }
          dim {
            size: 2
          }
        }
        tensor_content: "\000\000\024B\000\000\270\301\000\000\200?\000\000\200@"
      }
    }
  }
}
node {
  name: "random_uniform/shape"
  op: "Const"
  device: "/job:localhost/replica:0/task:0/device:CPU:0"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {
          dim {
            size: 2
          }
        }
        tensor_content: "\002\000\000\000\002\000\000\000"
      }
    }
  }
}
node {
  name: "random_uniform/RandomUniform"
  op: "RandomUniform"
  input: "random_uniform/shape"
  device: "/job:localhost/replica:0/task:0/device:CPU:0"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "seed"
    value {
      i: 0
    }
  }
  attr {
    key: "seed2"
    value {
      i: 0
    }
  }
}
node {
  name: "random_uniform/sub"
  op: "Const"
  device: "/job:localhost/replica:0/task:0/device:CPU:0"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
        }
        tensor_content: "\000\000\200?"
      }
    }
  }
}
node {
  name: "random_uniform/mul"
  op: "Mul"
  input: "random_uniform/RandomUniform"
  input: "random_uniform/sub"
  device: "/job:localhost/replica:0/task:0/device:CPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "random_uniform/min"
  op: "Const"
  device: "/job:localhost/replica:0/task:0/device:CPU:0"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
        }
        float_val: 0.0
      }
    }
  }
}
node {
  name: "random_uniform"
  op: "Add"
  input: "random_uniform/mul"
  input: "random_uniform/min"
  device: "/job:localhost/replica:0/task:0/device:CPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "MatMul"
  op: "MatMul"
  input: "MatMul/a"
  input: "random_uniform"
  device: "/job:localhost/replica:0/task:0/device:CPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "transpose_a"
    value {
      b: false
    }
  }
  attr {
    key: "transpose_b"
    value {
      b: false
    }
  }
}
node {
  name: "_retval_MatMul_0_0"
  op: "_Retval"
  input: "MatMul"
  device: "/job:localhost/replica:0/task:0/device:CPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
library {
}
versions {
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}
]
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    op_start_rel_micros: 12
    op_end_rel_micros: 12
    all_end_rel_micros: 21
    memory {
      allocator_name: "cpu"
    }
    timeline_label: "_SOURCE = NoOp()"
    scheduled_micros: 1518837178526691
    memory_stats {
    }
  }
  node_stats {
    node_name: "MatMul/a"
    all_start_micros: 1518837178526765
    op_end_rel_micros: 5
    all_end_rel_micros: 7
    memory {
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    }
    output {
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          dim {
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    }
    timeline_label: "MatMul/a = Const()"
    scheduled_micros: 1518837178526759
    memory_stats {
      host_persistent_memory_size: 16
      host_persistent_tensor_alloc_ids: -1
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  node_stats {
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    op_start_rel_micros: 1
    op_end_rel_micros: 2
    all_end_rel_micros: 2
    memory {
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    }
    timeline_label: "random_uniform/shape = Const()"
    scheduled_micros: 1518837178526772
    memory_stats {
      host_persistent_memory_size: 8
      host_persistent_tensor_alloc_ids: -1
    }
  }
  node_stats {
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    op_end_rel_micros: 1
    all_end_rel_micros: 1
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    timeline_label: "random_uniform/sub = Const()"
    scheduled_micros: 1518837178526775
    memory_stats {
      host_persistent_memory_size: 4
      host_persistent_tensor_alloc_ids: -1
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  node_stats {
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    timeline_label: "random_uniform/min = Const()"
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    memory_stats {
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      host_persistent_tensor_alloc_ids: -1
    }
  }
  node_stats {
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          allocator_name: "cpu"
          allocation_id: 1
          has_single_reference: true
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    }
    timeline_label: "random_uniform/RandomUniform = RandomUniform(random_uniform/shape)"
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    memory_stats {
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  }
  node_stats {
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    timeline_label: "random_uniform/mul = Mul(random_uniform/RandomUniform, random_uniform/sub)"
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    timeline_label: "random_uniform = Add(random_uniform/mul, random_uniform/min)"
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    memory_stats {
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    all_end_rel_micros: 47
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