So here, machine learning models were built using TensorFlow, which required data in tensor format. so to convert that Python list into a tensor, I usedtf.convert_to_tensor()function. I successfully converted the data into a tensor and fed it to the model, so in this tutorial, I will s...
创建Tensor从numpy上转换得到,或者通过list通过`tf.convert_to_tensor()'将一个numpy的array或者list转化为tensortf.convert_to_tensor(np.ones([2,3])) #将int型转化为float64,需要再次将float64转化为float32 tf.convert_to_tensor(np.zeros([2,3])) #将int型转化为float64 直接...
tf.convert_to_tensor 顾名思义这是一个将numpy或者list类型转换为tensor的函数,具体用法如下: tf.fill 创建一个维度为dims,值为value的tensor对象。该操作会创建一个维度为dims的tensor对象,并将其值设置为value,该tensor对象中的值类型和value一致。 tf.gather tf.gather(a,b,c=0 ),从a的c维根据b的参数值...
a.shape():返回类似list 的shape类型 a.ndim:返回数据维度,标量1.1维度为0,向量[1.1]维度为1 tf.rank(b):返回一个Tenor类型,实际内容为dim(dimensionality) tf.is_tensor(b):判断b是否为一个tensor类型 a.dtype:输出a的数据类型 aa=tf.convert_to_tensor(a, dtype=tf.int32):将int64的a转为tensor且指定...
创建Tensor * from numpy, list * zeros, ones, fill * random # if big dimension, random initial * constant * Application numpy, list numpy import numpy as np import tensorflow as tf tf.convert_to_tensor(np.ones([2, 3])) <tf.Tensor: id=0, shape=(2, 3), dtype=float64, numpy= ...
tensorflow中将一个普通list转换为tensor？用tf.convert_to_tensor()转换为了tensor，但是在接下来tf....
最近在看《TensorFlow 实战Google深度学习框架第二版》这本书,测试LeNet-5这个模型时遇到了TypeError: Failed to convert object of type <class 'list'> to Tensor的报错,由于书作者没有给出测试的代码,所以根据前面第五章给出的mnist测试代码修改了测试的代码。至于报错的原因尚且不是很清楚,不过找到了解决方法。
TypeError: Failed to convert object of type <class 'list'> to Tensor. Contents: [1, 1, Dimension(None)]. Consider casting elements to a supported type. How can I access shape within dataset creation so that I cam reshape some of my variables?
#from tensorflow.python.framework.ops import convert_to_tensor txtfile=r"D://Learning//tensorflow_2.0//smile//datas//train//train.txt" batch_size = 64 num_classes = 2 image_size = (48,48) class ImageData: def read_txt_file(self): self.img_paths = [] self.labels = [] for line...
简单介绍下,首先你需要得到所有的图像的path和对应的label的列表,利用tf.convert_to_tensor转换为对应的tensor, 利用tf.train.slice_input_producer将image_list ,label_list做一个slice处理,然后做图像的读取、预处理,以及label的one_hot表示,然后就是传到tf.train.shuffle_batch产生一个个shuffle batch,这些就可以fe...