本文整理汇总了Python中my.tensorflow.padded_reshape方法的典型用法代码示例。如果您正苦于以下问题：Python tensorflow.padded_reshape方法的具体用法？Python tensorflow.padded_reshape怎么用？Python tensorflow.padded_reshape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my.tensorflow的用法示例。
在下文中一共展示了tensorflow.padded_reshape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: __init__
# 需要导入模块: from my import tensorflow [as 别名]
# 或者: from my.tensorflow import padded_reshape [as 别名]
def __init__(self, config, models, tensor_dict=None):
super(MultiGPUF1Evaluator, self).__init__(config, models[0], tensor_dict=tensor_dict)
self.models = models
with tf.name_scope("eval_concat"):
N, M, JX = config.batch_size, config.max_num_sents, config.max_sent_size
self.yp = tf.concat(axis=0, values=[padded_reshape(model.yp, [N, M, JX]) for model in models])
self.yp2 = tf.concat(axis=0, values=[padded_reshape(model.yp2, [N, M, JX]) for model in models])
self.loss = tf.add_n([model.loss for model in models])/len(models) 示例2: __init__
# 需要导入模块: from my import tensorflow [as 别名]
# 或者: from my.tensorflow import padded_reshape [as 别名]
def __init__(self, config, models, tensor_dict=None):
super(MultiGPUF1Evaluator, self).__init__(config, models[0], tensor_dict=tensor_dict)
self.models = models
with tf.name_scope("eval_concat"):
N, M, JX = config.batch_size, config.max_num_sents, config.max_sent_size
self.yp = tf.concat(axis=0, values=[padded_reshape(model.yp, [N, M, JX]) for model in models])
self.yp2 = tf.concat(axis=0, values=[padded_reshape(model.yp2, [N, M, JX]) for model in models])
self.wy = tf.concat(axis=0, values=[padded_reshape(model.wy, [N, M, JX]) for model in models])
self.loss = tf.add_n([model.loss for model in models])/len(models) 示例3: __init__
# 需要导入模块: from my import tensorflow [as 别名]
# 或者: from my.tensorflow import padded_reshape [as 别名]
def __init__(self, config, models, tensor_dict=None):
super(MultiGPUF1Evaluator, self).__init__(config, models[0], tensor_dict=tensor_dict)
self.models = models
with tf.name_scope("eval_concat"):
N, M, JX = config.batch_size, config.max_num_sents, config.max_sent_size
self.yp = tf.concat(0, [padded_reshape(model.yp, [N, M, JX]) for model in models])
self.yp2 = tf.concat(0, [padded_reshape(model.yp2, [N, M, JX]) for model in models])
self.loss = tf.add_n([model.loss for model in models])/len(models)