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...
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...
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.Tensor: id=25, shape=(1,), dtype=int32, numpy=array([0], dtype=int32)> ones tf.ones(1) <tf.Tensor: id=29, shape=(1,), dtype=float32, numpy=array([1.], dtype=float32)> tf.ones([]) <tf.Tensor: id=31, shape=(), dtype=float32, numpy=1.0> tf.ones([2]) <tf....
c = tf.reshape(b, [1, -1])
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...
<tf.Tensor: id=6, shape=(2, 3), dtype=float32, numpy= array([[1., 2., 3.], [4., 5., 6.]], dtype=float32)> 如果输入的数据与指定的数据类型不相符,会产生以下异常: TypeError: Cannot convert provided value to EagerTensor. Provided value: 2.1 Requested dtype: int32 ...
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?
Also, you learned what tensor and numpy are and their difference. You may like to read: Python TensorFlow one_hot Convert list to tensor TensorFlow
convert_to_tensor(image_list, dtype=tf.string) label_list = tf.convert_to_tensor(label_list, dtype=tf.int32) image_label = tf.train.slice_input_producer([image_list, label_list], shuffle=True, num_epochs=10) img, labels = load_image(image_label[0]), image_label[1] print(img....