本文整理匯總了Python中tensorflow.python.ops.control_flow_ops.merge方法的典型用法代碼示例。如果您正苦於以下問題：Python control_flow_ops.merge方法的具體用法？Python control_flow_ops.merge怎麽用？Python control_flow_ops.merge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.python.ops.control_flow_ops的用法示例。
在下文中一共展示了control_flow_ops.merge方法的9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: apply_with_random_selector
# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import merge [as 別名]
def apply_with_random_selector(x, func, num_cases):
"""Computes func(x, sel), with sel sampled from [0...num_cases-1].
Args:
x: input Tensor.
func: Python function to apply.
num_cases: Python int32, number of cases to sample sel from.
Returns:
The result of func(x, sel), where func receives the value of the
selector as a python integer, but sel is sampled dynamically.
"""
sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
# Pass the real x only to one of the func calls.
return control_flow_ops.merge([
func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case)
for case in range(num_cases)])[0] 示例2: apply_with_random_selector
# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import merge [as 別名]
def apply_with_random_selector(x, func, num_cases):
"""Computes func(x, sel), with sel sampled from [0...num_cases-1].
Args:
x: input Tensor.
func: Python function to apply.
num_cases: Python int32, number of cases to sample sel from.
Returns:
The result of func(x, sel), where func receives the value of the
selector as a python integer, but sel is sampled dynamically.
"""
sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
# Pass the real x only to one of the func calls.
return control_flow_ops.merge([
func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case)
for case in range(num_cases)
])[0] 示例3: _apply_with_random_selector
# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import merge [as 別名]
def _apply_with_random_selector(x, func, num_cases):
"""Computes func(x, sel), with sel sampled from [0...num_cases-1].
Args:
x: input Tensor.
func: Python function to apply.
num_cases: Python int32, number of cases to sample sel from.
Returns:
The result of func(x, sel), where func receives the value of the
selector as a python integer, but sel is sampled dynamically.
"""
rand_sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
# Pass the real x only to one of the func calls.
return control_flow_ops.merge([func(
control_flow_ops.switch(x, tf.equal(rand_sel, case))[1], case)
for case in range(num_cases)])[0] 示例4: apply_with_random_selector
# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import merge [as 別名]
def apply_with_random_selector(x, func, num_cases):
"""Computes func(x, sel), with sel sampled from [0...num_cases-1].
Args:
x: input Tensor.
func: Python function to apply.
num_cases: Python int32, number of cases to sample sel from.
Returns:
The result of func(x, sel), where func receives the value of the
selector as a python integer, but sel is sampled dynamically.
"""
sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
# Pass the real x only to one of the func calls.
return control_flow_ops.merge([
func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case)
for case in range(num_cases)])[0] 示例5: _apply_with_random_selector
# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import merge [as 別名]
def _apply_with_random_selector(x, func, num_cases):
"""Computes func(x, sel), with sel sampled from [0...num_cases-1].
Args:
x: input Tensor.
func: Python function to apply.
num_cases: Python int32, number of cases to sample sel from.
Returns:
The result of func(x, sel), where func receives the value of the
selector as a python integer, but sel is sampled dynamically.
"""
sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
# Pass the real x only to one of the func calls.
return control_flow_ops.merge([
func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case)
for case in range(num_cases)
])[0] 示例6: apply_with_random_selector
# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import merge [as 別名]
def apply_with_random_selector(x, func, num_cases):
"""Computes func(x, sel), with sel sampled from [0...num_cases-1].
Args:
x: input Tensor.
func: Python function to apply.
num_cases: Python int32, number of cases to sample sel from.
Returns:
The result of func(x, sel), where func receives the value of the
selector as a python integer, but sel is sampled dynamically.
"""
sel = tf.random.uniform([], maxval=num_cases, dtype=tf.int32)
# Pass the real x only to one of the func calls.
return control_flow_ops.merge([
func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case)
for case in range(num_cases)])[0] 示例7: apply_with_random_selector
# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import merge [as 別名]
def apply_with_random_selector(x, func, num_cases):
"""Computes func(x, sel), with sel sampled from [0...num_cases-1].
Args:
x: input Tensor.
func: Python function to apply.
num_cases: Python int32, number of cases to sample sel from.
Returns:
The result of func(x, sel), where func receives the value of the
selector as a python integer, but sel is sampled dynamically.
"""
sel = tf.random.uniform([], maxval=num_cases, dtype=tf.int32)
# Pass the real x only to one of the func calls.
return control_flow_ops.merge([
func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case)
for case in range(num_cases)])[0] 示例8: apply_with_random_selector
# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import merge [as 別名]
def apply_with_random_selector(x, func, num_cases):
"""Computes func(x, sel), with sel sampled from [0...num_cases-1].
Args:
x: input Tensor.
func: Python function to apply.
num_cases: Python int32, number of cases to sample sel from.
Returns:
The result of func(x, sel), where func receives the value of the
selector as a python integer, but sel is sampled dynamically.
"""
sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
# Pass the real x only to one of the func calls.
return control_flow_ops.merge([
func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case)
for case in range(num_cases)])[0] 示例9: apply_with_random_selector
# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import merge [as 別名]
def apply_with_random_selector(x, func, num_cases):
"""Computes func(x, sel), with sel sampled from [0...num_cases-1].
TODO(coreylynch): add as a dependency, when slim or tensorflow/models are
pipfied.
Source:
https://raw.githubusercontent.com/tensorflow/models/a9d0e6e8923a4/slim/preprocessing/inception_preprocessing.py
Args:
x: input Tensor.
func: Python function to apply.
num_cases: Python int32, number of cases to sample sel from.
Returns:
The result of func(x, sel), where func receives the value of the
selector as a python integer, but sel is sampled dynamically.
"""
sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
# Pass the real x only to one of the func calls.
return control_flow_ops.merge([
func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case)
for case in range(num_cases)])[0]