ValueError: For 'MatMul', the input dimensions must be equal, but got 'x1_col': 12800 and 'x2_row': 10. And 'x' shape [32, 12800](transpose_a=False), 'y' shape [10, 1280](transpose_b=False). 原因分析 我们来看报错信息,在ValueError中,写到the input dimensions must be equal, but...
ValueError: For 'MatMul' the input dimensions must be equal, but got 'x1_col': 5120 and 'x2_row': 640. matmul算子编译报错,两个矩阵维度对不上 x = matmul(x, weight) 其中,x.shape=(-1,5120),weight.shape=(5120, 5120) 算子matmul的并行策略为: self.matmul.shard(((1,8), (8,1))...
ValueError: For'MatMul'the input dimensions must be equal, but got'x1_col':32768and'x2_row':...
报错信息:ValueError: For 'MatMul', the inputdimensionsmust be equal, but got 'x1_col': 2 and 'x2_row': 1. And 'x' shape [2, 3](transpose_a=True), 'y' shape [1, 3](transpose_b=False). 2 原因分析 根据报错信息,是MatMul算子在infer shape时检查输入的shape不正确,具体是x1的列数...
报错信息:ValueError: For ‘MatMul’, the input dimensions must be equal, but got ‘x1_col’: 2 and ‘x2_row’: 1. And ‘x’ shape [2, 3](#), ‘y’ shape [1, 3](#). 2 原因分析 根据报错信息,是MatMul算子在infer shape时检查输入的shape不正确,具体是x1的列数不等于x2的行数。
1.2.2报错报错信息:ValueError: For ‘MatMul’, the input dimensions must be equal, but got ‘x1_col’: 2 and ‘x2_row’: 1. And ‘x’ shape2, 3, ‘y’ shape1, 3. 2 原因分析根据报错信息,是MatMul算子在infer shape时检查输入的shape不正确,具体是x1的列数不等于x2的行数。打开报错提供...
ValueError: Dimensions must be equal, but are 784 and 10 for ‘add‘ (op: ‘Add‘) with input shapes: [7,程序员大本营,技术文章内容聚合第一站。
Internally 64-bit integers are not supported (the reason is they are not supported in the current version of WGSL); inputs and initializers with 64-bit scalars are converted to 32-bit values (possibly overflowing). For MatMul and Gemm, the matrix dimensions must be divisible by 2, or the...
ValueError: Dimensions must be equal, but are 1300 and 1750 for 'LSTMTDNN/LSTM/rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/MatMul_1' (op: 'MatMul') with input shapes: [100,1300], [1750,2600]. #26 Open kaierlong mentioned this issue Jul 27, 2018 Py3 tf 1.8 #28 ...
Whichever way you choose, you must also define which tensors are the inputs and outputs of the network. Tensors that are not marked as outputs are considered to be transient values that can be optimized away by the builder. Input and output tensors must be named, so that at run...