TensorFlow是Google设计的开源Python库,用于开发机器学习模型和深度学习神经网络。
graph用于查找包含值,索引和形状张量的Graph。
用法:tensorflow.IndexedSlices.graph
返回:它返回一个Graph实例。
范例1:
Python3
# Importing the library
import tensorflow as tf
# Initializing the input
data = tf.constant([[1, 2, 3], [4, 5, 6]], dtype = tf.float32)
# Printing the input
print('data:', data)
# Calculating result
res = tf.IndexedSlices(data, [0])
# Finding Graph
@tf.function
def gfg():
tf.compat.v1.disable_eager_execution()
graph = res.graph
# Printing the result
print('graph:', graph)
gfg()
输出:
data: Tensor("Const_1:0", shape=(2, 3), dtype=float32) graph: <tensorflow.python.framework.ops.Graph object at 0x7f2eeda9e630> <tf.Operation 'PartitionedCall_1' type=PartitionedCall>
范例2:
Python3
# Importing the library
import tensorflow as tf
# Initializing the input
data = tf.constant([1, 2, 3])
# Printing the input
print('data:', data)
# Calculating result
res = tf.IndexedSlices(data, [0])
# Finding Graph
graph = res.graph
# Printing the result
print('graph:', graph)
输出:
data: Tensor("Const_6:0", shape=(3, ), dtype=int32) graph: <tensorflow.python.framework.ops.Graph object at 0x7f2eeda9e630>
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注:本文由纯净天空筛选整理自aman neekhara大神的英文原创作品 Python – tensorflow.IndexedSlices.graph Attribute。非经特殊声明,原始代码版权归原作者所有,本译文未经允许或授权,请勿转载或复制。