tf.eye
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Construct an identity matrix, or a batch of matrices.
tf.eye(
num_rows, num_columns=None, batch_shape=None, dtype=tf.dtypes.float32, name=None
)
# Construct one identity matrix.
tf.eye(2)
==> [[1., 0.],
[0., 1.]]
# Construct a batch of 3 identity matricies, each 2 x 2.
# batch_identity[i, :, :] is a 2 x 2 identity matrix, i = 0, 1, 2.
batch_identity = tf.eye(2, batch_shape=[3])
# Construct one 2 x 3 "identity" matrix
tf.eye(2, num_columns=3)
==> [[ 1., 0., 0.],
[ 0., 1., 0.]]
Args |
num_rows
|
Non-negative int32 scalar Tensor giving the number of rows
in each batch matrix.
|
num_columns
|
Optional non-negative int32 scalar Tensor giving the number
of columns in each batch matrix. Defaults to num_rows .
|
batch_shape
|
A list or tuple of Python integers or a 1-D int32 Tensor .
If provided, the returned Tensor will have leading batch dimensions of
this shape.
|
dtype
|
The type of an element in the resulting Tensor
|
name
|
A name for this Op . Defaults to "eye".
|
Returns |
A Tensor of shape batch_shape + [num_rows, num_columns]
|
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Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.eye\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 1 version](/versions/r1.15/api_docs/python/tf/eye) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.0.0/tensorflow/python/ops/linalg_ops.py#L127-L169) |\n\nConstruct an identity matrix, or a batch of matrices.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.linalg.eye`](/api_docs/python/tf/eye)\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.eye`](/api_docs/python/tf/eye), [`tf.compat.v1.linalg.eye`](/api_docs/python/tf/eye)\n\n\u003cbr /\u003e\n\n tf.eye(\n num_rows, num_columns=None, batch_shape=None, dtype=tf.dtypes.float32, name=None\n )\n\n # Construct one identity matrix.\n tf.eye(2)\n ==\u003e [[1., 0.],\n [0., 1.]]\n\n # Construct a batch of 3 identity matricies, each 2 x 2.\n # batch_identity[i, :, :] is a 2 x 2 identity matrix, i = 0, 1, 2.\n batch_identity = tf.eye(2, batch_shape=[3])\n\n # Construct one 2 x 3 \"identity\" matrix\n tf.eye(2, num_columns=3)\n ==\u003e [[ 1., 0., 0.],\n [ 0., 1., 0.]]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------|----------------------------------------------------------------------------------------------------------------------------------------------------|\n| `num_rows` | Non-negative `int32` scalar `Tensor` giving the number of rows in each batch matrix. |\n| `num_columns` | Optional non-negative `int32` scalar `Tensor` giving the number of columns in each batch matrix. Defaults to `num_rows`. |\n| `batch_shape` | A list or tuple of Python integers or a 1-D `int32` `Tensor`. If provided, the returned `Tensor` will have leading batch dimensions of this shape. |\n| `dtype` | The type of an element in the resulting `Tensor` |\n| `name` | A name for this `Op`. Defaults to \"eye\". |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor` of shape `batch_shape + [num_rows, num_columns]` ||\n\n\u003cbr /\u003e"]]