本文整理汇总了Python中tensorflow.contrib.tpu.rewrite方法的典型用法代码示例。如果您正苦于以下问题：Python tpu.rewrite方法的具体用法？Python tpu.rewrite怎么用？Python tpu.rewrite使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.contrib.tpu的用法示例。
在下文中一共展示了tpu.rewrite方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: execute_tpu
# 需要导入模块: from tensorflow.contrib import tpu [as 别名]
# 或者: from tensorflow.contrib.tpu import rewrite [as 别名]
def execute_tpu(self, graph_fn, inputs):
"""Constructs the graph, executes it on TPU and returns the result.
Args:
graph_fn: a callable that constructs the tensorflow graph to test. The
arguments of this function should correspond to `inputs`.
inputs: a list of numpy arrays to feed input to the computation graph.
Returns:
A list of numpy arrays or a scalar returned from executing the tensorflow
graph.
"""
with self.test_session(graph=tf.Graph()) as sess:
placeholders = [tf.placeholder_with_default(v, v.shape) for v in inputs]
tpu_computation = tpu.rewrite(graph_fn, placeholders)
sess.run(tpu.initialize_system())
sess.run([tf.global_variables_initializer(), tf.tables_initializer(),
tf.local_variables_initializer()])
materialized_results = sess.run(tpu_computation,
feed_dict=dict(zip(placeholders, inputs)))
sess.run(tpu.shutdown_system())
if len(materialized_results) == 1:
materialized_results = materialized_results[0]
return materialized_results 示例2: execute_tpu
# 需要导入模块: from tensorflow.contrib import tpu [as 别名]
# 或者: from tensorflow.contrib.tpu import rewrite [as 别名]
def execute_tpu(self, graph_fn, inputs):
"""Constructs the graph, executes it on TPU and returns the result.
Args:
graph_fn: a callable that constructs the tensorflow graph to test. The
arguments of this function should correspond to `inputs`.
inputs: a list of numpy arrays to feed input to the computation graph.
Returns:
A list of numpy arrays or a scalar returned from executing the tensorflow
graph.
"""
with self.test_session(graph=tf.Graph()) as sess:
placeholders = [tf.placeholder_with_default(v, v.shape) for v in inputs]
tpu_computation = tpu.rewrite(graph_fn, placeholders)
sess.run(tpu.initialize_system())
sess.run([tf.global_variables_initializer(), tf.tables_initializer(),
tf.local_variables_initializer()])
materialized_results = sess.run(tpu_computation,
feed_dict=dict(zip(placeholders, inputs)))
sess.run(tpu.shutdown_system())
if (hasattr(materialized_results, '__len__') and
len(materialized_results) == 1 and
(isinstance(materialized_results, list) or
isinstance(materialized_results, tuple))):
materialized_results = materialized_results[0]
return materialized_results 示例3: execute_tpu
# 需要导入模块: from tensorflow.contrib import tpu [as 别名]
# 或者: from tensorflow.contrib.tpu import rewrite [as 别名]
def execute_tpu(self, graph_fn, inputs):
"""Constructs the graph, executes it on TPU and returns the result.
Args:
graph_fn: a callable that constructs the tensorflow graph to test. The
arguments of this function should correspond to `inputs`.
inputs: a list of numpy arrays to feed input to the computation graph.
Returns:
A list of numpy arrays or a scalar returned from executing the tensorflow
graph.
"""
with self.test_session(graph=tf.Graph()) as sess:
placeholders = [tf.placeholder_with_default(v, v.shape) for v in inputs]
tpu_computation = tpu.rewrite(graph_fn, placeholders)
sess.run(tpu.initialize_system())
sess.run([tf.global_variables_initializer(), tf.tables_initializer(),
tf.local_variables_initializer()])
materialized_results = sess.run(tpu_computation,
feed_dict=dict(zip(placeholders, inputs)))
sess.run(tpu.shutdown_system())
if (len(materialized_results) == 1
and (isinstance(materialized_results, list)
or isinstance(materialized_results, tuple))):
materialized_results = materialized_results[0]
return materialized_results 示例4: execute_tpu
# 需要导入模块: from tensorflow.contrib import tpu [as 别名]
# 或者: from tensorflow.contrib.tpu import rewrite [as 别名]
def execute_tpu(self, graph_fn, inputs):
"""Constructs the graph, executes it on TPU and returns the result.
Args:
graph_fn: a callable that constructs the tensorflow graph to test. The
arguments of this function should correspond to `inputs`.
inputs: a list of numpy arrays to feed input to the computation graph.
Returns:
A list of numpy arrays or a scalar returned from executing the tensorflow
graph.
"""
with self.test_session(graph=tf.Graph()) as sess:
placeholders = [tf.placeholder_with_default(v, v.shape) for v in inputs]
tpu_computation = tpu.rewrite(graph_fn, placeholders)
sess.run(tpu.initialize_system())
sess.run([tf.global_variables_initializer(), tf.tables_initializer(),
tf.local_variables_initializer()])
materialized_results = sess.run(tpu_computation,
feed_dict=dict(zip(placeholders, inputs)))
sess.run(tpu.shutdown_system())
if (len(materialized_results) == 1
and (isinstance(materialized_results, list)
or isinstance(materialized_results, tuple))):
materialized_results = materialized_results[0]
return materialized_results 示例5: execute_tpu
# 需要导入模块: from tensorflow.contrib import tpu [as 别名]
# 或者: from tensorflow.contrib.tpu import rewrite [as 别名]
def execute_tpu(self, graph_fn, inputs):
"""Constructs the graph, executes it on TPU and returns the result.
Args:
graph_fn: a callable that constructs the tensorflow graph to test. The
arguments of this function should correspond to `inputs`.
inputs: a list of numpy arrays to feed input to the computation graph.
Returns:
A list of numpy arrays or a scalar returned from executing the tensorflow
graph.
"""
with self.test_session(graph=tf.Graph()) as sess:
placeholders = [tf.placeholder_with_default(v, v.shape) for v in inputs]
tpu_computation = tpu.rewrite(graph_fn, placeholders)
sess.run(tpu.initialize_system())
sess.run([tf.global_variables_initializer(), tf.tables_initializer(),
tf.local_variables_initializer()])
materialized_results = sess.run(tpu_computation,
feed_dict=dict(list(zip(placeholders, inputs))))
sess.run(tpu.shutdown_system())
if (len(materialized_results) == 1
and (isinstance(materialized_results, list)
or isinstance(materialized_results, tuple))):
materialized_results = materialized_results[0]
return materialized_results