Question

有人能告诉我这里发生了什么吗？我很困惑。

In [125]: a
Out[125]: <tf.Tensor 'MatMul_86739:0' shape=(100, 1) dtype=float32>

In [126]: embed
Out[126]: <tf.Tensor 'embedding_lookup_41205:0' shape=(100,) dtype=float32>


In [128]: a+embed
Out[128]: <tf.Tensor 'add_43373:0' shape=(100, 100) dtype=float32>

（100,1）+（100，）怎么能（100,100）？如果是的话，为什么？

Answer 1

TensorFlow广播运营商的规则基于NumPy's broadcasting rules。

广播的基本算法从右到左工作。假设我们正在添加（或应用另一个二进制广播运算符）两个张量x和y，以下代码计算结果的形状：

result_shape = []

# Loop over the matching dimensions of x and y in reverse.
for x_dim, y_dim in zip(x.shape[::-1], y.shape[::-1]):
  if x.shape == y.shape:
    result_shape.insert(0, x.shape)
  elif x.shape == 1:
    result_shape.insert(0, y.shape)  # x will be broadcast along this dimension.
  elif y.shape == 1:
    result_shape.insert(0, x.shape)  # y will be broadcast along this dimension.
  else:
    raise ValueError("Shapes of x and y are incompatible.")

# If x and y have a different rank, the leading dimensions are inherited
# from the tensor with higher rank.
if len(x.shape) > len(y.shape):
  num_leading_dims = len(x.shape) - len(y.shape)
  result_shape = x.shape[0:num_leading_dims] + result_shape
elif len(y.shape) > len(x.shape):
  num_leading_dims = len(y.shape) - len(x.shape)
  result_shape = y.shape[0:num_leading_dims] + result_shape

现在，在您的示例中，您有x.shape = (100,)和y.shape = (100, 1)：

第一次比较是在100和1之间，因此result_shape = [100]。
y.shape超过x.shape，因此我们将结果的前导维度添加到result_shape = [100, 100]。

Tensorflow添加广播

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