Is there an established way to convert a Tensorflow network architecture written in graph/session, to Keras...
In order to use Google TPU, your code must either use the Estimator API or Keras API.
Converting a graph to use the Estimator API is pretty straight forward, as you mostly allocate the code among model_fn, features, input_fn, etc.
Converting to graph to Keras is not as straight forward, as Keras has unique functions and datatypes to handle various operations. However, from Tensorflow's blog post, they seem to recommend Keras over Estimator
https://medium.com/tensorflow/standardizing-on-keras-guidance-on-high-level-apis-in-tensorflow-2-0-bad2b04c819a.
By establishing Keras as the high-level API for TensorFlow, we are
making it easier for developers new to machine learning to get started
with TensorFlow.
That said, if you are working on custom architectures, we suggest
using tf.keras to build your models instead of Estimator.
The Tensorflow Keras API is built on top of Tensorflow though, so I'm guessing there's a way to make anything in Tensorflow also in keras, especially since there seems to be functions that directly convert a Tensorflow function to keras, such as keras.optimizers.TFOptimizer(<tensorflow optimizer>)
Is there an established way to convert any architecture coded using graph/session to the Keras API?
python tensorflow keras
asked Jan 2 at 6:37
SantoshGupta7SantoshGupta7
6811515
add a comment |
In order to use Google TPU, your code must either use the Estimator API or Keras API.
Converting a graph to use the Estimator API is pretty straight forward, as you mostly allocate the code among model_fn, features, input_fn, etc.
Converting to graph to Keras is not as straight forward, as Keras has unique functions and datatypes to handle various operations. However, from Tensorflow's blog post, they seem to recommend Keras over Estimator
https://medium.com/tensorflow/standardizing-on-keras-guidance-on-high-level-apis-in-tensorflow-2-0-bad2b04c819a.
By establishing Keras as the high-level API for TensorFlow, we are
making it easier for developers new to machine learning to get started
with TensorFlow.
That said, if you are working on custom architectures, we suggest
using tf.keras to build your models instead of Estimator.
The Tensorflow Keras API is built on top of Tensorflow though, so I'm guessing there's a way to make anything in Tensorflow also in keras, especially since there seems to be functions that directly convert a Tensorflow function to keras, such as keras.optimizers.TFOptimizer(<tensorflow optimizer>)
Is there an established way to convert any architecture coded using graph/session to the Keras API?
python tensorflow keras
asked Jan 2 at 6:37
SantoshGupta7SantoshGupta7
6811515
add a comment |
In order to use Google TPU, your code must either use the Estimator API or Keras API.
Converting a graph to use the Estimator API is pretty straight forward, as you mostly allocate the code among model_fn, features, input_fn, etc.
Converting to graph to Keras is not as straight forward, as Keras has unique functions and datatypes to handle various operations. However, from Tensorflow's blog post, they seem to recommend Keras over Estimator
https://medium.com/tensorflow/standardizing-on-keras-guidance-on-high-level-apis-in-tensorflow-2-0-bad2b04c819a.
By establishing Keras as the high-level API for TensorFlow, we are
making it easier for developers new to machine learning to get started
with TensorFlow.
That said, if you are working on custom architectures, we suggest
using tf.keras to build your models instead of Estimator.
The Tensorflow Keras API is built on top of Tensorflow though, so I'm guessing there's a way to make anything in Tensorflow also in keras, especially since there seems to be functions that directly convert a Tensorflow function to keras, such as keras.optimizers.TFOptimizer(<tensorflow optimizer>)
Is there an established way to convert any architecture coded using graph/session to the Keras API?
python tensorflow keras
asked Jan 2 at 6:37
SantoshGupta7SantoshGupta7
6811515
In order to use Google TPU, your code must either use the Estimator API or Keras API.
Converting a graph to use the Estimator API is pretty straight forward, as you mostly allocate the code among model_fn, features, input_fn, etc.
Converting to graph to Keras is not as straight forward, as Keras has unique functions and datatypes to handle various operations. However, from Tensorflow's blog post, they seem to recommend Keras over Estimator
https://medium.com/tensorflow/standardizing-on-keras-guidance-on-high-level-apis-in-tensorflow-2-0-bad2b04c819a.
By establishing Keras as the high-level API for TensorFlow, we are
making it easier for developers new to machine learning to get started
with TensorFlow.
That said, if you are working on custom architectures, we suggest
using tf.keras to build your models instead of Estimator.
The Tensorflow Keras API is built on top of Tensorflow though, so I'm guessing there's a way to make anything in Tensorflow also in keras, especially since there seems to be functions that directly convert a Tensorflow function to keras, such as keras.optimizers.TFOptimizer(<tensorflow optimizer>)
Is there an established way to convert any architecture coded using graph/session to the Keras API?
python tensorflow keras
python tensorflow keras
asked Jan 2 at 6:37
SantoshGupta7SantoshGupta7
6811515
asked Jan 2 at 6:37
SantoshGupta7SantoshGupta7
6811515
asked Jan 2 at 6:37
SantoshGupta7SantoshGupta7
6811515
asked Jan 2 at 6:37
SantoshGupta7SantoshGupta7
6811515
asked Jan 2 at 6:37
SantoshGupta7SantoshGupta7
6811515
6811515
add a comment |
add a comment |
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