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"""Utility functions for training."""
import os.path
from tensorflow.python.platform import gfile
def global_step(sess, global_step_tensor):
"""Small helper to get the global step.
```python
# Creates a variable to hold the global_step.
global_step_tensor = tf.Variable(10, trainable=False, name='global_step')
# Creates a session.
sess = tf.Session()
# Initializes the variable.
sess.run(global_step_tensor.initializer)
print 'global_step:', tf.train.global_step(sess, global_step_tensor)
global_step: 10
```
Args:
sess: A brain `Session` object.
global_step_tensor: `Tensor` or the `name` of the operation that contains
the global step.
Returns:
The global step value.
"""
return int(sess.run(global_step_tensor))
def write_graph(graph_def, logdir, name, as_text=True):
"""Writes a graph proto on disk.
The graph is written as a binary proto unless as_text is `True`.
```python
v = tf.Variable(0, name='my_variable')
sess = tf.Session()
tf.train.write_graph(sess.graph_def, '/tmp/my-model', 'train.pbtxt')
```
Args:
graph_def: A `GraphDef` protocol buffer.
logdir: Directory where to write the graph.
name: Filename for the graph.
as_text: If `True`, writes the graph as an ASCII proto.
"""
path = os.path.join(logdir, name)
gfile.MakeDirs(os.path.dirname(path))
f = gfile.FastGFile(path, "w")
if as_text:
f.write(str(graph_def))
else:
f.write(graph_def.SerializeToString())
f.close()
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