tf.convert_to_tensor(value,dtype=None,dtype_hint=None,name=None) 该函数将各种类型的Python对象转换为张量对象。它接受张量对象、数字数组、Python列表和Python标量。 例: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 importnumpyasnp defmy_func(arg):arg=tf
利用tf.convert_to_tensor转换为类型 简介:【8月更文挑战第11天】利用tf.convert_to_tensor转换为类型。 从numpy,list对象创建,再利用tf.convert_to_tensor转换为类型。 将给定制转换为张量。可利用这个函数将python的数据类型转换成TensorFlow可用的tensor数据类型。 tf.convert_to_tensor(value,dtype=None,dtype_hi...
#将python的数据类型(列表和矩阵)转换成TensorFlow可用的tensor数据类型importtensorflowastfimportnumpyasnpA= [1,2,3]B= np.array([1,2,3])C= tf.convert_to_tensor(A)D= tf.convert_to_tensor(B)print(type(A),A)print(type(B),B)print(type(C),C)print(type(D),D)结果<class'list'> [1,2...
tf.convert_to_tensor import tensorflow as tf import numpy as np def my_func(arg):arg= tf.convert_to_tensor(arg, dtype=tf.float32)returnarg# The following calls are equivalent. value_1 = my_func(tf.constant([[1.0, 2.0], [3.0, 4.0]]))print(value_1) value_2 = my_func([[1.0, ...
convert_to_tensor(self.image_list, dtype=tf.string) self.labels = tf.convert_to_tensor(self.label_list, dtype=tf.string) 这里的read_labeled_image_list是用来读取图像和标签的路径的,返回两个list。 打上断点,看这两句话执行之后的效果。 tf.convert_to_tensor()执行后返回一个Tensor,问题是,这个...
tf.convert_to_tensor( value, dtype=None, name=None, preferred_dtype=None, dtype_hint=None ) 1. 2. 3. 4. 5. 6. 7. 该函数将各种类型的Python对象转换为张量对象。它接受张量对象、数字数组、Python列表和Python标量。例如: import numpy as np ...
tf.convert_to_tensor(new_tensor_v) print(type(new_tensor)) # <class 'tensorflow.python.framework.ops.EagerTensor'> # print(new_tensor.numpy()) ''' [[ 0. 1. 2. 3. 4.] [ 5. 6. 7. 8. 9.] [ 10. 11. 12. 100. 14.] ...
arrut = tf.convert_to_tensor(arru) print "squeeze=>" print tf.squeeze(arrut).eval() print "x", "*" * 20 arrx = np.array([[1,2, 3], [2, 3, 4], [4, 5,6], [6, 7,8]]) print np.shape(arrx) print arrx
y = tf.convert_to_tensor(y, dtype=tf.int32)# 转换为张量(标签) print(x.shape, y.shape) train_dataset = tf.data.Dataset.from_tensor_slices((x, y))# 构建数据集对象 train_dataset = train_dataset.batch(32).repeat(10)# 设置批量训练的batch为32,要将训练集重复训练10遍 ...
import numpy.random as npr import tensorflow as tf with tf.device("GPU"): A=tf.convert_to_tensor(npr.randn(500)) will create an eager tensor A on the CPU device (it will not allocate ram on the gpu). This is counter-intuitive to someone who has only read the doc as it is writ...