tensor=torch.rand(3,4) print(f"Shape of tensor:{tensor.shape}") # 这条语句等效于 print("Shape of tensor:{}".format(tensor.shape)) 2. 语法解释 这种字符常量表⽰⽅式是在python3.6之后引⼊的。 PEP 498(即Python Enhancement Proposals, Python增强提案或Python改进建议书),引⼊了⼀种新的...
tensorflow由于其基于静态图的模式,导致写代码的时候很难调试,除了用官方的调试工具外,最直接的方法就是把中间结果输出出来查看,然而,直接用print函数只能输出tensor变量的形状(如("Placeholder:0", shape=(128, 346), dtype=float32),原因是:A Tensor object is a symbolic handle to the result of an operation...
1.1 创建TENSOR # 创建未初始化的Tensorx = torch.empty(5,3)print(x) # 创建随机初始化的Tensorx = torch.rand(5,3)print(x) # 创建全为0的Tensorx = torch.zeros(5,3,dtype=torch.long)print(x) # 根据数据创建Tensorx = torch.tensor([5.5,3])print(x) # 修改原Tensor为全1的Tensorx = x...
message: A string, prefix of the error message. first_n: Only log `first_n` number of times. Negative numbers log always; this is the default. summarize: Only print this many entries of each tensor. If None, then a maximum of 3 elements are printed per input tensor. name: A name f...
summarize是对每个tensor只打印的条目数量,如果是None,对于每个输入tensor只打印3个元素 name是op的名字 需要注意的是tf.Print()只是构建一个op,需要run之后才会打印。 例子: x=tf.constant([2,3,4,5]) y=tf.Print(x,[x,x.shape,'test', x],message='Debug message:',summarize=100) ...
此外,tf.Print中的第二个参数[]中放入的内容,也必须是能够转为Tensor。否则会提示 TypeError: Tensors in list passed to 'data' of 'Print' Op have types [<NOT CONVERTIBLE TO TENSOR>] that are invalid. 比如,一个Tensor的shape中如果有“?”,就不能转换为Tensor。对于这种不能Tensor,我们不能用get_...
我在Jupyter 笔记本中使用 tf.Print 操作。它按要求工作,但只会将输出打印到控制台,而不在笔记本中打印。有什么办法可以解决这个问题吗? 示例如下(在笔记本中): import tensorflow as tf a = tf.constant(1.0) a = tf.Print(a, [a], 'hi')
>> print(b.shape) torch.Size([3, 2, 3]) dim = 0,通过合并b[0], b[1]..得到正确结果 # dim 0 >> print(b[0]) tensor([[1, 2, 3], [4, 5, 6]]) >> print(b[1]) tensor([[1, 2, 3], [4, 5, 6]]) >> print(torch.sum(b, dim=0)) ...
shape:[2,2] values: [[8, 2], [5, 6]] 范例2: Javascript // Importing the tensorflow.js libraryimport*astffrom"@tensorflow/tfjs"// Setting the value of verboseconstverbose =false;// Creating the tensorvarval = tf.tensor2d([8,2,5,6], [2,2]);// Printing the tensorval.print(ve...
x.print(style:TensorStringStyle.Julia); x.print(style:TensorStringStyle.CSharp); [10],type= Int64, device = cpu 0 1 2 3 4 5 6 7 8 9 [0, 1, 2, ... 7, 8, 9] [10],type= Int64, device = cpu [10],type= Int64, device = cpu 0 1 2 3 4 5 6 7 8 9 ...