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Initializer that generates tensors with constant values.

Inherits From: Initializer

tf.initializers.constant(
    value=0, dtype=tf.dtypes.float32, verify_shape=False
)

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.

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 = np.array(value)

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

@classmethod
from_config(
    config
)

Instantiates an initializer from a configuration dictionary.

Example:

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

get_config

View source

get_config()

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

__call__

View source

__call__(
    shape, dtype=None, partition_info=None, verify_shape=None
)

Returns a tensor object initialized as specified by the initializer.