You can get a direct reference to the graphs if you want, that will let you do anything tensorflow lets you do. I think this is what you want:
# This assumes your model is ready to be called with .predict()
sess = keras.get_session()
graph = sess.graph
graph_dev = graph.as_graph_def()
frozen_graph = tf.graph_util.convert_variables_to_constants(
sess, graph_def, nodes_to_output)
encoded_frozen_graph = frozen_graph.SerializeToString()Not to mention you can more easily use channels-first data, quantize to FP16/INT8 more easily, and export to ONNX for use w/ Tensor-RT and/or Intel Nervana.
This was never true.
There was no obvious Keras API for this, but you could build a model with the Keras API, then use the TF API to save it. The inference API would be the TF API (i.e. you'd need to find the names of all your input and output tensors and use those with Session.run).
Except that this was true. I do not remember the exact details, because this was the end of 2015 or beginning of 2016. But dumping/freezing definitely failed on some graphs constructed with Keras.
There was no obvious Keras API for this, but you could build a model with the Keras API, then use the TF API to save it.
That was easy to figure out. Read the backend implementation and you can see how you can get the graph definition, etc.
Use the right tool for the job. Keras can get you to a working model faster. However, I am not sure what the current situation is, but in the past it was not possible to dump and freeze Keras' Tensorflow graphs. This can be a problem if you want to embed a model in a non-Python application.
This attitude of "real deep learning engineers use Tensorflow"
Real engineers use whatever they need to use. But I think that you are overstating the difficulty of Tensorflow. Over the last 6 months, we have hired a couple of students for a research project. Since we standardized on Tensorflow, they had to implement new models in Tensorflow. All of them were up to speed in Tensorflow pretty quickly (they mostly do RNNs and seq2seq learning).