Python tf.compat.v1.sparse_placeholder用法及代码示例

为将始终馈送的稀疏张量插入占位符。

用法

tf.compat.v1.sparse_placeholder(
    dtype, shape=None, name=None
)

重要的：如果评估，此稀疏张量将产生错误。它的值必须使用 feed_dict 可选参数提供给 Session.run() 、 Tensor.eval() 或 Operation.run() 。

例如：

x = tf.compat.v1.sparse.placeholder(tf.float32)
y = tf.sparse.reduce_sum(x)

with tf.compat.v1.Session() as sess:
  print(sess.run(y))  # ERROR:will fail because x was not fed.

  indices = np.array([[3, 2, 0], [4, 5, 1]], dtype=np.int64)
  values = np.array([1.0, 2.0], dtype=np.float32)
  shape = np.array([7, 9, 2], dtype=np.int64)
  print(sess.run(y, feed_dict={
    x:tf.compat.v1.SparseTensorValue(indices, values, shape)}))  # Will
    succeed.
  print(sess.run(y, feed_dict={
    x:(indices, values, shape)}))  # Will succeed.

  sp = tf.sparse.SparseTensor(indices=indices, values=values,
                              dense_shape=shape)
  sp_value = sp.eval(session=sess)
  print(sess.run(y, feed_dict={x:sp_value}))  # Will succeed.

@compatibility{eager} 占位符与即刻执行不兼容。