In [1]:
# Exporting and Importing a MetaGraph
def to_proto(self, export_scope=None):
"""Converts a `Variable` to a `VariableDef` protocol buffer.
Args:
export_scope: Optional `string`. Name scope to remove.
Returns:
A `VariableDef` protocol buffer, or `None` if the `Variable` is not
in the specified name scope.
"""
if (export_scope is None or
self._variable.name.startswith(export_scope)):
var_def = variable_pb2.VariableDef()
var_def.variable_name = ops.strip_name_scope(
self._variable.name, export_scope)
var_def.initializer_name = ops.strip_name_scope(
self.initializer.name, export_scope)
var_def.snapshot_name = ops.strip_name_scope(
self._snapshot.name, export_scope)
if self._save_slice_info:
var_def.save_slice_info_def.MergeFrom(self._save_slice_info.to_proto(
export_scope=export_scope))
return var_def
else:
return None
@staticmethod
def from_proto(variable_def, import_scope=None):
"""Returns a `Variable` object created from `variable_def`."""
return Variable(variable_def=variable_def, import_scope=import_scope)
ops.register_proto_function(ops.GraphKeys.GLOBAL_VARIABLES,
proto_type=variable_pb2.VariableDef,
to_proto=Variable.to_proto,
from_proto=Variable.from_proto)
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def export_meta_graph(filename=None, collection_list=None, as_text=False):
"""Writes `MetaGraphDef` to save_path/filename.
Args:
filename: Optional meta_graph filename including the path.
collection_list: List of string keys to collect.
as_text: If `True`, writes the meta_graph as an ASCII proto.
Returns:
A `MetaGraphDef` proto.
"""
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# Build the model
...
with tf.Session() as sess:
# Use the model
...
# Export the model to /tmp/my-model.meta.
meta_graph_def = tf.train.export_meta_graph(filename='/tmp/my-model.meta')
meta_graph_def = tf.train.export_meta_graph(
filename='/tmp/my-model.meta',
collection_list=["input_tensor", "output_tensor"])
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# export meta graph
...
# Create a saver.
saver = tf.train.Saver(...variables...)
# Remember the training_op we want to run by adding it to a collection.
tf.add_to_collection('train_op', train_op)
sess = tf.Session()
for step in xrange(1000000):
sess.run(train_op)
if step % 1000 == 0:
# Saves checkpoint, which by default also exports a meta_graph
# named 'my-model-global_step.meta'.
saver.save(sess, 'my-model', global_step=step)
# import meta graph
with tf.Session() as sess:
new_saver = tf.train.import_meta_graph('my-save-dir/my-model-10000.meta')
new_saver.restore(sess, 'my-save-dir/my-model-10000')
# tf.get_collection() returns a list. In this example we only want the
# first one.
train_op = tf.get_collection('train_op')[0]
for step in xrange(1000000):
sess.run(train_op)
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# build an inference graph, export it as a meta graph:
# Creates an inference graph.
# Hidden 1
images = tf.constant(1.2, tf.float32, shape=[100, 28])
with tf.name_scope("hidden1"):
weights = tf.Variable(
tf.truncated_normal([28, 128],
stddev=1.0 / math.sqrt(float(28))),
name="weights")
biases = tf.Variable(tf.zeros([128]),
name="biases")
hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
# Hidden 2
with tf.name_scope("hidden2"):
weights = tf.Variable(
tf.truncated_normal([128, 32],
stddev=1.0 / math.sqrt(float(128))),
name="weights")
biases = tf.Variable(tf.zeros([32]),
name="biases")
hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
# Linear
with tf.name_scope("softmax_linear"):
weights = tf.Variable(
tf.truncated_normal([32, 10],
stddev=1.0 / math.sqrt(float(32))),
name="weights")
biases = tf.Variable(tf.zeros([10]),
name="biases")
logits = tf.matmul(hidden2, weights) + biases
tf.add_to_collection("logits", logits)
init_all_op = tf.global_variables_initializer()
with tf.Session() as sess:
# Initializes all the variables.
sess.run(init_all_op)
# Runs to logit.
sess.run(logits)
# Creates a saver.
saver0 = tf.train.Saver()
saver0.save(sess, 'my-save-dir/my-model-10000')
# Generates MetaGraphDef.
saver0.export_meta_graph('my-save-dir/my-model-10000.meta')
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# import it and extend it to a training graph.
with tf.Session() as sess:
new_saver = tf.train.import_meta_graph('my-save-dir/my-model-10000.meta')
new_saver.restore(sess, 'my-save-dir/my-model-10000')
# Addes loss and train.
labels = tf.constant(0, tf.int32, shape=[100], name="labels")
batch_size = tf.size(labels)
labels = tf.expand_dims(labels, 1)
indices = tf.expand_dims(tf.range(0, batch_size), 1)
concated = tf.concat([indices, labels], 1)
onehot_labels = tf.sparse_to_dense(
concated, tf.stack([batch_size, 10]), 1.0, 0.0)
logits = tf.get_collection("logits")[0]
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
labels=onehot_labels, logits=logits, name="xentropy")
loss = tf.reduce_mean(cross_entropy, name="xentropy_mean")
tf.summary.scalar('loss', loss)
# Creates the gradient descent optimizer with the given learning rate.
optimizer = tf.train.GradientDescentOptimizer(0.01)
# Runs train_op.
train_op = optimizer.minimize(loss)
sess.run(train_op)
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# Import a graph with preset devices.
with tf.Session() as sess:
new_saver = tf.train.import_meta_graph('my-save-dir/my-model-10000.meta',
clear_devices=True)
new_saver.restore(sess, 'my-save-dir/my-model-10000')
...
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# Import within the default graph.
meta_graph_def = tf.train.export_meta_graph()
...
tf.reset_default_graph()
...
tf.train.import_meta_graph(meta_graph_def)
...
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# Retrieve Hyper Parameters
filename = ".".join([tf.train.latest_checkpoint(train_dir), "meta"])
tf.train.import_meta_graph(filename)
hparams = tf.get_collection("hparams")