Inserts a placeholder for a sparse tensor that will be always fed.
tf.compat.v1.sparse_placeholder(
dtype, shape=None, name=None
)
Used in the notebooks
For example:
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} Placeholders are not compatible with eager execution.
Args |
|---|
dtype
|
The type of values elements 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.
|
Raises |
|---|
RuntimeError
|
if eager execution is enabled
|
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