本文整理汇总了Python中tensorflow.contrib.tpu.python.tpu.tpu_estimator.TPUEstimatorSpec方法的典型用法代码示例。如果您正苦于以下问题：Python tpu_estimator.TPUEstimatorSpec方法的具体用法？Python tpu_estimator.TPUEstimatorSpec怎么用？Python tpu_estimator.TPUEstimatorSpec使用的例子？那么恭喜您, 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.python.tpu.tpu_estimator的用法示例。
在下文中一共展示了tpu_estimator.TPUEstimatorSpec方法的3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: estimator_spec_predict
# 需要导入模块: from tensorflow.contrib.tpu.python.tpu import tpu_estimator [as 别名]
# 或者: from tensorflow.contrib.tpu.python.tpu.tpu_estimator import TPUEstimatorSpec [as 别名]
def estimator_spec_predict(self, features, mesh, mesh_impl, use_tpu):
mtf_samples = mtf.anonymize(self.sample(features, mesh))
lowering = mtf.Lowering(mesh.graph, {mesh: mesh_impl})
outputs = lowering.export_to_tf_tensor(mtf_samples)
if self.has_input:
ndims = len(outputs.shape.as_list())
actual_batch_size = tf.shape(features["inputs"])[0]
outputs = tf.slice(
outputs, [0] * ndims, [actual_batch_size] + [-1] * (ndims - 1))
predictions = {
"outputs": outputs
}
if features.get("infer_targets") is not None:
predictions["infer_targets"] = features["infer_targets"]
if features.get("inputs") is not None:
predictions["inputs"] = features["inputs"]
if use_tpu:
t2t_model.remove_summaries()
return tpu_estimator.TPUEstimatorSpec(
mode=tf.estimator.ModeKeys.PREDICT,
predictions=predictions,
prediction_hooks=[mtf.MtfRestoreHook(lowering)])
else:
return tf.estimator.EstimatorSpec(
tf.estimator.ModeKeys.PREDICT,
predictions=predictions,
prediction_hooks=[mtf.MtfRestoreHook(lowering)]) 示例2: estimator_spec_eval
# 需要导入模块: from tensorflow.contrib.tpu.python.tpu import tpu_estimator [as 别名]
# 或者: from tensorflow.contrib.tpu.python.tpu.tpu_estimator import TPUEstimatorSpec [as 别名]
def estimator_spec_eval(
self, features, logits, labels, loss, restore_hook, use_tpu):
"""Construct EstimatorSpec for EVAL mode."""
hparams = self.hparams
problem = hparams.problem
if logits.get_shape().ndims == 3:
logits = tf.expand_dims(tf.expand_dims(logits, 2), 3)
eval_metrics_fns = metrics.create_evaluation_metrics([problem], hparams)
if use_tpu:
def metric_fn(tf_logits, labels):
with tf.device("cpu:0"), mtf.utils.outside_all_rewrites():
eval_metrics = {}
for metric_name, metric_fn in six.iteritems(eval_metrics_fns):
if metric_name.split("/")[-1] not in t2t_model.TPU_METRIC_BLACKLIST:
eval_metrics[metric_name] = metric_fn(
tf_logits, None, tf.identity(labels))
return eval_metrics
return tpu_estimator.TPUEstimatorSpec(
tf.estimator.ModeKeys.EVAL,
evaluation_hooks=[restore_hook],
loss=loss,
eval_metrics=(metric_fn, [logits, labels]))
else:
eval_metrics = {}
predictions = {"predictions": logits}
for metric_name, metric_fn in six.iteritems(eval_metrics_fns):
eval_metrics[metric_name] = metric_fn(logits, features,
features["targets"])
return tf.estimator.EstimatorSpec(
tf.estimator.ModeKeys.EVAL,
predictions=predictions,
eval_metric_ops=eval_metrics,
evaluation_hooks=[restore_hook],
loss=loss) 示例3: estimator_spec_eval
# 需要导入模块: from tensorflow.contrib.tpu.python.tpu import tpu_estimator [as 别名]
# 或者: from tensorflow.contrib.tpu.python.tpu.tpu_estimator import TPUEstimatorSpec [as 别名]
def estimator_spec_eval(
self, features, logits, labels, loss, restore_hook, use_tpu):
"""Construct EstimatorSpec for EVAL mode."""
hparams = self.hparams
problem = hparams.problem
if logits.get_shape().ndims == 3:
logits = tf.expand_dims(tf.expand_dims(logits, 2), 3)
# Support for multiproblem
task_list = [problem]
if hasattr(problem, "task_list"):
task_list = problem.task_list
eval_metrics_fns = metrics.create_evaluation_metrics(task_list, hparams)
if use_tpu:
def metric_fn(tf_logits, labels):
with tf.device("cpu:0"), mtf.utils.outside_all_rewrites():
eval_metrics = {}
for metric_name, metric_fn in six.iteritems(eval_metrics_fns):
if metric_name.split("/")[-1] not in t2t_model.TPU_METRIC_BLACKLIST:
eval_metrics[metric_name] = metric_fn(
tf_logits, None, tf.identity(labels))
return eval_metrics
return tpu_estimator.TPUEstimatorSpec(
tf.estimator.ModeKeys.EVAL,
evaluation_hooks=[restore_hook],
loss=loss,
eval_metrics=(metric_fn, [logits, labels]))
else:
eval_metrics = {}
predictions = {"predictions": logits}
for metric_name, metric_fn in six.iteritems(eval_metrics_fns):
eval_metrics[metric_name] = metric_fn(logits, features,
features["targets"])
return tf.estimator.EstimatorSpec(
tf.estimator.ModeKeys.EVAL,
predictions=predictions,
eval_metric_ops=eval_metrics,
evaluation_hooks=[restore_hook],
loss=loss)