本文整理匯總了Python中tensorflow.contrib.tpu.rewrite方法的典型用法代碼示例。如果您正苦於以下問題：Python tpu.rewrite方法的具體用法？Python tpu.rewrite怎麽用？Python tpu.rewrite使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorflow.contrib.tpu的用法示例。
在下文中一共展示了tpu.rewrite方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: execute_tpu
# 需要導入模塊: from tensorflow.contrib import tpu [as 別名]
# 或者: from tensorflow.contrib.tpu import rewrite [as 別名]
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
# 需要導入模塊: from tensorflow.contrib import tpu [as 別名]
# 或者: from tensorflow.contrib.tpu import rewrite [as 別名]
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
# 需要導入模塊: from tensorflow.contrib import tpu [as 別名]
# 或者: from tensorflow.contrib.tpu import rewrite [as 別名]
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
# 需要導入模塊: from tensorflow.contrib import tpu [as 別名]
# 或者: from tensorflow.contrib.tpu import rewrite [as 別名]
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
# 需要導入模塊: from tensorflow.contrib import tpu [as 別名]
# 或者: from tensorflow.contrib.tpu import rewrite [as 別名]
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