tf.sparse_placeholder(dtype, shape=None, name=None)
Inserts a placeholder for a sparse tensor that will be always fed.
Important: This sparse tensor will produce an error if evaluated. Its value must be fed using the feed_dict optional argument to Session.run(), Tensor.eval(), or Operation.run().
For example:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | x = tf.sparse_placeholder(tf.float32)y = tf.sparse_reduce_sum(x)with tf.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.SparseTensorValue(indices, values, shape)})) # Will succeed. print(sess.run(y, feed_dict={ x: (indices, values, shape)})) # Will succeed. sp = tf.SparseTensor(indices=indices, values=values, shape=shape) sp_value = sp.eval(session) print(sess.run(y, feed_dict={x: sp_value})) # Will succeed. |
Args:
-
dtype: The type ofvalueselements in the tensor to be fed. -
shape: The shape of the tensor to be fed (optional). If the shape is not specified, you can feed a sparse tensor of any shape. -
name: A name for prefixing the operations (optional).
Returns:
A SparseTensor that may be used as a handle for feeding a value, but not evaluated directly.
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