本文整理匯總了Python中my.tensorflow.padded_reshape方法的典型用法代碼示例。如果您正苦於以下問題：Python tensorflow.padded_reshape方法的具體用法？Python tensorflow.padded_reshape怎麽用？Python tensorflow.padded_reshape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my.tensorflow的用法示例。
在下文中一共展示了tensorflow.padded_reshape方法的3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: __init__
# 需要導入模塊: from my import tensorflow [as 別名]
# 或者: from my.tensorflow import padded_reshape [as 別名]
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__
# 需要導入模塊: from my import tensorflow [as 別名]
# 或者: from my.tensorflow import padded_reshape [as 別名]
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__
# 需要導入模塊: from my import tensorflow [as 別名]
# 或者: from my.tensorflow import padded_reshape [as 別名]
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)