b = sparse_tensor.convert_to_tensor_or_sparse_tensor(b, name="b")ifb.dtype.base_dtype != a.dtype.base_dtype:raiseTypeError("Types don't match, %s vs %s."% (a.dtype, b.dtype))# pylint: disable=protected-accessifisinstance(a, sparse_tensor.SparseTensor):if...
shape for i in range(nrows): sp_indices = [] for j in range(ncols): if np_matrix[i][j] == 1: sp_indices.append([j]) num_non_zeros = len(sp_indices) list_of_sparse_tensors.append(sparse_tensor.SparseTensor( indices=np.array(sp_indices), values=np.ones((num_non_zeros,))...
TypeError: Failed to convert object of type <class 'tensorflow.python.framework.sparse_tensor.SparseTensor'> to Tensor. Contents: SparseTensor(indices=Tensor("DeserializeSparse:0", shape=(?, 2), dtype=int64, device=/device:GPU:*), values=Tensor("DeserializeSparse:1", shape=(?,), dtype=int6...
I get the error [2]. I have seen that this issue has been raised for TF2.4 (#47931), but didn't see an issue raised for TF2.5. I know I can convert the sparse tensor to dense 'on-the-fly' in a generator Dataset
use tensor.dense(),这里提供详细的解答和解决方案。 1. 确认错误类型及原因 这个错误表明你尝试将一个稀疏布局的张量(Sparse Tensor)直接转换为NumPy数组,但这样的转换是不被支持的。在PyTorch等深度学习框架中,稀疏张量的存储方式与密集张量(Dense Tensor)不同,因此无法直接转换为NumPy数组。 2. 查找解决方法 为了...
OSError: /root/anaconda3/lib/python3.7/site-packages/torch_sparse/_convert_cpu.so: undefined symbol: _ZNK2at6Tensor5zero_Ev I followed the Quick Start installation, with torch=1.10.0+cu102 andrei-rusumentioned this issueMar 31, 2022
feature_values: An optional 1-D tensor float tensor of shape `[N]`. Also, accepts python lists, or numpy arrays. Returns: A `SparseFeatureColumn` """withname_scope(None,'SparseFeatureColumn',[example_indices,feature_indices]):self._example_indices=convert_to_tensor(example_indices,name='ex...
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 复制 importnumpyasnp defmy_func(arg):arg=tf.convert_to_tensor(arg,dtype=tf.float32)returntf.matmul(arg,arg)...
Python's.format() function is a flexible way to format strings; it lets you dynamically insert variables into strings without changing their original data types. Example - 4: Using f-stringOutput: <class 'int'> <class 'str'> Explanation: An integer variable called n is initialized with ...
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?