tf.keras.backend.get_value
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Returns the value of a variable.
tf.keras.backend.get_value(
x
)
backend.get_value is the compliment of backend.set_value, and provides
a generic interface for reading from variables while abstracting away the
differences between TensorFlow 1.x and 2.x semantics.
K = tf.keras.backend # Common keras convention
v = K.variable(1.)
# reassign
K.set_value(v, 2.)
print(K.get_value(v))
2.0
# increment
K.set_value(v, K.get_value(v) + 1)
print(K.get_value(v))
3.0
Variable semantics in TensorFlow 2 are eager execution friendly. The above
code is roughly equivalent to:
v = tf.Variable(1.)
_ = v.assign(2.)
print(v.numpy())
2.0
_ = v.assign_add(1.)
print(v.numpy())
3.0
Arguments |
|---|
x
|
input variable.
|
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Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.keras.backend.get_value\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 1 version](/versions/r1.15/api_docs/python/tf/keras/backend/get_value) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.2.0/tensorflow/python/keras/backend.py#L3279-L3310) |\n\nReturns the value of a variable.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.keras.backend.get_value`](/api_docs/python/tf/keras/backend/get_value)\n\n\u003cbr /\u003e\n\n tf.keras.backend.get_value(\n x\n )\n\n[`backend.get_value`](../../../tf/keras/backend/get_value) is the compliment of [`backend.set_value`](../../../tf/keras/backend/set_value), and provides\na generic interface for reading from variables while abstracting away the\ndifferences between TensorFlow 1.x and 2.x semantics. \n\n K = tf.keras.backend # Common keras convention\n v = K.variable(1.)\n\n # reassign\n K.set_value(v, 2.)\n print(K.get_value(v))\n 2.0\n\n # increment\n K.set_value(v, K.get_value(v) + 1)\n print(K.get_value(v))\n 3.0\n\nVariable semantics in TensorFlow 2 are eager execution friendly. The above\ncode is roughly equivalent to: \n\n v = tf.Variable(1.)\n\n _ = v.assign(2.)\n print(v.numpy())\n 2.0\n\n _ = v.assign_add(1.)\n print(v.numpy())\n 3.0\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Arguments --------- ||\n|-----|-----------------|\n| `x` | input variable. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A Numpy array. ||\n\n\u003cbr /\u003e"]]
TensorFlow 1 version
View source on GitHub