本文整理汇总了Python中tensorflow.python.training.basic_session_run_hooks.StopAtStepHook方法的典型用法代码示例。如果您正苦于以下问题:Python basic_session_run_hooks.StopAtStepHook方法的具体用法?Python basic_session_run_hooks.StopAtStepHook怎么用?Python basic_session_run_hooks.StopAtStepHook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.python.training.basic_session_run_hooks
的用法示例。
在下文中一共展示了basic_session_run_hooks.StopAtStepHook方法的6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: _train_model
# 需要导入模块: from tensorflow.python.training import basic_session_run_hooks [as 别名]
# 或者: from tensorflow.python.training.basic_session_run_hooks import StopAtStepHook [as 别名]
def _train_model(self, checkpoint_dir, num_steps):
"""Trains a simple classification model.
Note that the data has been configured such that after around 300 steps,
the model has memorized the dataset (e.g. we can expect %100 accuracy).
Args:
checkpoint_dir: The directory where the checkpoint is written to.
num_steps: The number of steps to train for.
"""
with ops.Graph().as_default():
random_seed.set_random_seed(0)
tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
tf_labels = constant_op.constant(self._labels, dtype=dtypes.float32)
tf_predictions = logistic_classifier(tf_inputs)
loss = loss_ops.log_loss(tf_labels, tf_predictions)
optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0)
train_op = training.create_train_op(loss, optimizer)
loss = training.train(
train_op,
checkpoint_dir,
hooks=[basic_session_run_hooks.StopAtStepHook(num_steps)])
示例2: testTrainWithNoInitAssignCanAchieveZeroLoss
# 需要导入模块: from tensorflow.python.training import basic_session_run_hooks [as 别名]
# 或者: from tensorflow.python.training.basic_session_run_hooks import StopAtStepHook [as 别名]
def testTrainWithNoInitAssignCanAchieveZeroLoss(self):
with ops.Graph().as_default():
random_seed.set_random_seed(0)
tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
tf_labels = constant_op.constant(self._labels, dtype=dtypes.float32)
tf_predictions = batchnorm_classifier(tf_inputs)
losses.log_loss(tf_labels, tf_predictions)
total_loss = losses.get_total_loss()
optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0)
train_op = training.create_train_op(total_loss, optimizer)
loss = training.train(
train_op,
None,
hooks=[basic_session_run_hooks.StopAtStepHook(num_steps=300)],
save_summaries_steps=None,
save_checkpoint_secs=None)
self.assertLess(loss, .1)
示例3: testCanAchieveZeroLoss
# 需要导入模块: from tensorflow.python.training import basic_session_run_hooks [as 别名]
# 或者: from tensorflow.python.training.basic_session_run_hooks import StopAtStepHook [as 别名]
def testCanAchieveZeroLoss(self):
with ops.Graph().as_default():
random_seed.set_random_seed(0)
tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
tf_labels = constant_op.constant(self._labels, dtype=dtypes.float32)
tf_predictions = logistic_classifier(tf_inputs)
losses.log_loss(tf_labels, tf_predictions)
total_loss = losses.get_total_loss()
optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0)
train_op = training.create_train_op(total_loss, optimizer)
loss = training.train(
train_op,
None,
hooks=[basic_session_run_hooks.StopAtStepHook(num_steps=300)],
save_summaries_steps=None,
save_checkpoint_secs=None)
self.assertIsNotNone(loss)
self.assertLess(loss, .015)
示例4: testTrainWithLocalVariable
# 需要导入模块: from tensorflow.python.training import basic_session_run_hooks [as 别名]
# 或者: from tensorflow.python.training.basic_session_run_hooks import StopAtStepHook [as 别名]
def testTrainWithLocalVariable(self):
with ops.Graph().as_default():
random_seed.set_random_seed(0)
tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
tf_labels = constant_op.constant(self._labels, dtype=dtypes.float32)
local_multiplier = variables_lib.local_variable(1.0)
tf_predictions = logistic_classifier(tf_inputs) * local_multiplier
losses.log_loss(tf_labels, tf_predictions)
total_loss = losses.get_total_loss()
optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0)
train_op = training.create_train_op(total_loss, optimizer)
loss = training.train(
train_op,
None,
hooks=[basic_session_run_hooks.StopAtStepHook(num_steps=300)],
save_summaries_steps=None,
save_checkpoint_secs=None)
self.assertIsNotNone(loss)
self.assertLess(loss, .015)
示例5: fit
# 需要导入模块: from tensorflow.python.training import basic_session_run_hooks [as 别名]
# 或者: from tensorflow.python.training.basic_session_run_hooks import StopAtStepHook [as 别名]
def fit(self, x=None, y=None, input_fn=None, steps=None, batch_size=None,
monitors=None, max_steps=None):
# pylint: disable=g-doc-args,g-doc-return-or-yield
"""See `Trainable`.
Raises:
ValueError: If `x` or `y` are not `None` while `input_fn` is not `None`.
ValueError: If both `steps` and `max_steps` are not `None`.
"""
if (steps is not None) and (max_steps is not None):
raise ValueError('Can not provide both steps and max_steps.')
_verify_input_args(x, y, input_fn, None, batch_size)
if x is not None:
SKCompat(self).fit(x, y, batch_size, steps, max_steps, monitors)
return self
if max_steps is not None:
try:
start_step = load_variable(self._model_dir, ops.GraphKeys.GLOBAL_STEP)
if max_steps <= start_step:
logging.info('Skipping training since max_steps has already saved.')
return self
except: # pylint: disable=bare-except
pass
hooks = monitor_lib.replace_monitors_with_hooks(monitors, self)
if steps is not None or max_steps is not None:
hooks.append(basic_session_run_hooks.StopAtStepHook(steps, max_steps))
loss = self._train_model(input_fn=input_fn, hooks=hooks)
logging.info('Loss for final step: %s.', loss)
return self
示例6: testTrainWithAlteredGradients
# 需要导入模块: from tensorflow.python.training import basic_session_run_hooks [as 别名]
# 或者: from tensorflow.python.training.basic_session_run_hooks import StopAtStepHook [as 别名]
def testTrainWithAlteredGradients(self):
# Use the same learning rate but different gradient multipliers
# to train two models. Model with equivalently larger learning
# rate (i.e., learning_rate * gradient_multiplier) has smaller
# training loss.
multipliers = [1., 1000.]
number_of_steps = 10
learning_rate = 0.001
# First, train the model with equivalently smaller learning rate.
with ops.Graph().as_default():
random_seed.set_random_seed(0)
train_op = self.create_train_op(
learning_rate=learning_rate, gradient_multiplier=multipliers[0])
loss0 = training.train(
train_op,
None,
hooks=[
basic_session_run_hooks.StopAtStepHook(num_steps=number_of_steps),
],
save_checkpoint_secs=None,
save_summaries_steps=None)
self.assertIsNotNone(loss0)
self.assertGreater(loss0, .5)
# Second, train the model with equivalently larger learning rate.
with ops.Graph().as_default():
random_seed.set_random_seed(0)
train_op = self.create_train_op(
learning_rate=learning_rate, gradient_multiplier=multipliers[1])
loss1 = training.train(
train_op,
None,
hooks=[
basic_session_run_hooks.StopAtStepHook(num_steps=number_of_steps),
],
save_checkpoint_secs=None,
save_summaries_steps=None)
self.assertIsNotNone(loss1)
self.assertLess(loss1, .5)
# The loss of the model trained with larger learning rate should
# be smaller.
self.assertGreater(loss0, loss1)