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tf.compat.v1.keras.initializers.Constant

Initializer that generates tensors with constant values.

Inherits From: `Initializer`

The resulting tensor is populated with values of type `dtype`, as specified by arguments `value` following the desired `shape` of the new tensor (see examples below).

The argument `value` can be a constant value, or a list of values of type `dtype`. If `value` is a list, then the length of the list must be less than or equal to the number of elements implied by the desired shape of the tensor. In the case where the total number of elements in `value` is less than the number of elements required by the tensor shape, the last element in `value` will be used to fill the remaining entries. If the total number of elements in `value` is greater than the number of elements required by the tensor shape, the initializer will raise a `ValueError`.

`value` A Python scalar, list or tuple of values, or a N-dimensional numpy array. All elements of the initialized variable will be set to the corresponding value in the `value` argument.
`dtype` Default data type, used if no `dtype` argument is provided when calling the initializer.
`verify_shape` Boolean that enables verification of the shape of `value`. If `True`, the initializer will throw an error if the shape of `value` is not compatible with the shape of the initialized tensor.

`TypeError` If the input `value` is not one of the expected types.

Examples:

The following example can be rewritten using a numpy.ndarray instead of the `value` list, even reshaped, as shown in the two commented lines below the `value` list initialization.

```  `import numpy as np`
`import tensorflow as tf`

`  `
```

value = [0, 1, 2, 3, 4, 5, 6, 7]

value = value.reshape([2, 4])

init = tf.compat.v1.constant_initializer(value)

``````<pre class="devsite-click-to-copy prettyprint lang-py">
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">print(&#x27;fitting shape:&#x27;)</code>
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">with tf.compat.v1.Session():</code>
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">  x = tf.compat.v1.get_variable(&#x27;x&#x27;, shape=[2, 4], initializer=init)</code>
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">  x.initializer.run()</code>
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">  print(x.eval())</code>
<code class="no-select nocode">  </code>
</pre>

fitting shape:
[[ 0.  1.  2.  3.]
[ 4.  5.  6.  7.]]

<pre class="devsite-click-to-copy prettyprint lang-py">
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">print(&#x27;larger shape:&#x27;)</code>
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">with tf.compat.v1.Session():</code>
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">  x = tf.compat.v1.get_variable(&#x27;x&#x27;, shape=[3, 4], initializer=init)</code>
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">  x.initializer.run()</code>
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">  print(x.eval())</code>
<code class="no-select nocode">  </code>
</pre>

larger shape:
[[ 0.  1.  2.  3.]
[ 4.  5.  6.  7.]
[ 7.  7.  7.  7.]]

<pre class="devsite-click-to-copy prettyprint lang-py">
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">print(&#x27;smaller shape:&#x27;)</code>
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">with tf.compat.v1.Session():</code>
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">  x = tf.compat.v1.get_variable(&#x27;x&#x27;, shape=[2, 3], initializer=init)</code>
<code class="no-select nocode">  </code>
</pre>

ValueError: Too many elements provided. Needed at most 6, but received 8

<pre class="devsite-click-to-copy prettyprint lang-py">
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">print(&#x27;shape verification:&#x27;)</code>
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">init_verify = tf.compat.v1.constant_initializer(value,</code>
<code class="no-select nocode">  verify_shape=True)</code>
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">with tf.compat.v1.Session():</code>
<code class="devsite-terminal" data-terminal-prefix="&gt;&gt;&gt;">  x = tf.compat.v1.get_variable(&#x27;x&#x27;, shape=[3, 4],</code>
<code class="no-select nocode">  initializer=init_verify)</code>
<code class="no-select nocode">  </code>
</pre>

TypeError: Expected Tensor's shape: (3, 4), got (8,).
``````

Methods

`from_config`

View source

Instantiates an initializer from a configuration dictionary.

Example:

``````initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)
``````

Args
`config` A Python dictionary. It will typically be the output of `get_config`.

Returns
An Initializer instance.

`get_config`

View source

Returns the configuration of the initializer as a JSON-serializable dict.

Returns
A JSON-serializable Python dict.

`__call__`

View source

Returns a tensor object initialized as specified by the initializer.

Args
`shape` Shape of the tensor.
`dtype` Optional dtype of the tensor. If not provided use the initializer dtype.
`partition_info` Optional information about the possible partitioning of a tensor.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]