tfp.experimental.distributions.marginal_fns.ps.identity
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Return a Tensor with the same shape and contents as input.
tfp.experimental.distributions.marginal_fns.ps.identity(
input, name=None
)
The return value is not the same Tensor as the original, but contains the same
values. This operation is fast when used on the same device.
For example:
a = tf.constant([0.78])
a_identity = tf.identity(a)
a.numpy()
array([0.78], dtype=float32)
a_identity.numpy()
array([0.78], dtype=float32)
Calling tf.identity on a variable will make a Tensor that represents the
value of that variable at the time it is called. This is equivalent to calling
<variable>.read_value().
a = tf.Variable(5)
a_identity = tf.identity(a)
a.assign_add(1)
<tf.Variable ... shape=() dtype=int32, numpy=6>
a.numpy()
6
a_identity.numpy()
5
This function can also be used to explicitly transfer tensors between devices.
For example, to transfer a tensor in GPU memory back to host memory, one can
use:
with tf.device("/gpu:0"):
x_on_gpu = tf.constant(1)
with tf.device("/cpu:0"):
x_on_cpu = tf.identity(x_on_gpu)
x_on_cpu.device
'/job:localhost/replica:0/task:0/device:CPU:0'
Args |
|---|
input
|
A Tensor, a Variable, a CompositeTensor or anything that can be
converted to a tensor using tf.convert_to_tensor.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor or CompositeTensor. Has the same type and contents as input.
|
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Last updated 2023-05-09 UTC.
[null,null,["Last updated 2023-05-09 UTC."],[],[],null,["# tfp.experimental.distributions.marginal_fns.ps.identity\n\n\u003cbr /\u003e\n\nReturn a Tensor with the same shape and contents as input. \n\n tfp.experimental.distributions.marginal_fns.ps.identity(\n input, name=None\n )\n\nThe return value is not the same Tensor as the original, but contains the same\nvalues. This operation is fast when used on the same device.\n\n#### For example:\n\n a = tf.constant([0.78])\n a_identity = tf.identity(a)\n a.numpy()\n array([0.78], dtype=float32)\n a_identity.numpy()\n array([0.78], dtype=float32)\n\nCalling [`tf.identity`](https://www.tensorflow.org/api_docs/python/tf/identity) on a variable will make a Tensor that represents the\nvalue of that variable at the time it is called. This is equivalent to calling\n`\u003cvariable\u003e.read_value()`. \n\n a = tf.Variable(5)\n a_identity = tf.identity(a)\n a.assign_add(1)\n \u003ctf.Variable ... shape=() dtype=int32, numpy=6\u003e\n a.numpy()\n 6\n a_identity.numpy()\n 5\n\nThis function can also be used to explicitly transfer tensors between devices.\nFor example, to transfer a tensor in GPU memory back to host memory, one can\nuse: \n\n with tf.device(\"/gpu:0\"):\n x_on_gpu = tf.constant(1)\n with tf.device(\"/cpu:0\"):\n x_on_cpu = tf.identity(x_on_gpu)\n x_on_cpu.device\n '/job:localhost/replica:0/task:0/device:CPU:0'\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `input` | A `Tensor`, a `Variable`, a `CompositeTensor` or anything that can be converted to a tensor using [`tf.convert_to_tensor`](https://www.tensorflow.org/api_docs/python/tf/convert_to_tensor). |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor` or CompositeTensor. Has the same type and contents as `input`. ||\n\n\u003cbr /\u003e"]]
