我们从Python开源项目中，提取了以下46个代码示例，用于说明如何使用tensorflow.python.ops.control_flow_ops.switch()。
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]
def apply_with_random_selector(self, 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) return control_flow_ops.merge([ func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case) for case in range(num_cases)])[0]
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]
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]
def apply_with_random_selector(x, func, num_cases): sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32) return control_flow_ops.merge([ func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case) for case in range(num_cases)])[0]
def _apply_with_random_selector(x, func, num_cases): 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]
def _apply_with_random_selector_tuples(x, func, num_cases): """Computes func(x, sel), with sel sampled from [0...num_cases-1]. Args: x: A tuple of input tensors. 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. """ num_inputs = len(x) rand_sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32) # Pass the real x only to one of the func calls. tuples = [list() for t in x] for case in range(num_cases): new_x = [control_flow_ops.switch(t, tf.equal(rand_sel, case))[1] for t in x] output = func(tuple(new_x), case) for j in range(num_inputs): tuples[j].append(output[j]) for i in range(num_inputs): tuples[i] = control_flow_ops.merge(tuples[i])[0] return tuple(tuples)
