What is difference between tf.keras.models.Sequential() vs tf.keras.Sequential()? I don't understand differences between them quite well. Can somebody explain it to me? I am new to TensorFlow but have some basic understanding on machine learning.
What is difference between tf.keras.models.sequential vs tf.keras.sequential?
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3 Answers
>>> tf.keras.models.Sequential==tf.keras.Sequential
True
Both are same as of TFv2. You could use the later.
Added in this commit.
tf.keras.models.Sequential
and
tf.keras.Sequential
Do the same thing but they are from different versions of tensorflow. By the documentation (TensorFlow 2.0), tf.keras.Sequential is the most recent way of called this function.
Keras (keras.io) is a library which is available on its own. It specifies the high-level api. tf.keras (https://www.tensorflow.org/guide/keras) implements the Keras API specification within TensorFlow.
If you intend to stick to the Tensorflow implementation I would stick to tf.keras. Otherwise you have the advantage to be backend agnostic.
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update for updated question.
The renaming of the package for tf.keras.models.Sequential to tf.keras.Sequential must have happened from 1.15 to 2.x you can either downgrade your tensor flow version or update the code. I'd go for the latter
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