11–20 of 21 posts
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Is there any way to use a pytorch model in Mobile and in a website without a server API? For me 5hose are two good reasons to keep using TensorFlow.
https://caffe2.ai/docs/AI-Camera-demo-android.html ? Not something I've used myself, but supposedly yes.
Earlier quoted context omitted.
https://caffe2.ai/docs/AI-Camera-demo-android.html ? Not something I've used myself, but supposedly yes.
Thanks. I always forgot about caffe2 when talking about pytorch. I couldn't anything for JavaScript and mobile seems not as good supported as TF but for sure they will improve.
Some well considered advice: drop TensorFlow and go with PyTorch. Spend your effort where it will make a difference: on deep learning, rather than on fighting with the framework. People just keep using TF because it was the first full-fledged Python framework for this, not because it has any technical merit anymore. In PyTorch you will make twice as much progress in half the time.
Some well considered advice: drop TensorFlow and go with PyTorch. Spend your effort where it will make a difference: on deep learning, rather than on fighting with the framework. People just keep using TF because it was the first full-fledged Python framework for this, not because it has any technical merit anymore. In PyTorch you will make twice as much progress in half the time.
Is there any way to use a pytorch model in Mobile and in a website without a server API? For me 5hose are two good reasons to keep using TensorFlow.
Some well considered advice: drop TensorFlow and go with PyTorch. Spend your effort where it will make a difference: on deep learning, rather than on fighting with the framework. People just keep using TF because it was the first full-fledged Python framework for this, not because it has any technical merit anymore. In PyTorch you will make twice as much progress in half the time.
Im just starting with TF and found keras quite useful to begin with. TF itself feels like assembly language and I'm sure it will evolve to something higher level such as keras in the near future.
I have never used PyTorch though, will check it out. I would appreciate your thoughts on tf.keras vs pytorch.
Some well considered advice: drop TensorFlow and go with PyTorch. Spend your effort where it will make a difference: on deep learning, rather than on fighting with the framework. People just keep using TF because it was the first full-fledged Python framework for this, not because it has any technical merit anymore. In PyTorch you will make twice as much progress in half the time.
How about tf.keras though? Im just starting with TF and found keras quite useful to begin with. TF itself feels like assembly language and I'm sure it will evolve to something higher level such as keras in the near future. I have never used PyTorch though, will check it out. I would appreciate your thoughts on tf.keras vs pytorch.
But a better question is, why bother with Keras at all, if PyTorch gives you a higher performance, more flexible, more "Pythonic" solution? And yes, did I mention performance? PyTorch blows the socks off anything TF based on most training and inference tasks.
Some well considered advice: drop TensorFlow and go with PyTorch. Spend your effort where it will make a difference: on deep learning, rather than on fighting with the framework. People just keep using TF because it was the first full-fledged Python framework for this, not because it has any technical merit anymore. In PyTorch you will make twice as much progress in half the time.
TF does have stuff which is still lacking in pytorch: for example complex numbers support, better sparse matrix support. The new distributed api is much more functional than pytorch one, allowing different levels of control, and not only high level. Surely there are more examples outside of my usecases. So yeah, pytorch is default, but if I need feature which is lacking there, I switch back to tf
Earlier quoted context omitted.
How about tf.keras though? Im just starting with TF and found keras quite useful to begin with. TF itself feels like assembly language and I'm sure it will evolve to something higher level such as keras in the near future. I have never used PyTorch though, will check it out. I would appreciate your thoughts on tf.keras vs pytorch.
Not flexible enough for research (you still have to deal with the horrible TensorFlow API that's underneath at some point), but good if you just want to implement or use something that already exists. Not good for models in which graph changes dynamically. Actually "flexible" is probably not the right word. You can make it do what you want, but you will spend a lot longer and the result will likely be unreadable. But…
Earlier quoted context omitted.
Not flexible enough for research (you still have to deal with the horrible TensorFlow API that's underneath at some point), but good if you just want to implement or use something that already exists. Not good for models in which graph changes dynamically. Actually "flexible" is probably not the right word. You can make it do what you want, but you will spend a lot longer and the result will likely be unreadable. But…
Thanks. I admit what made me chose TF is the support from Google which guarantees somehow that the tool will stick around for some time. And the numbers of contributors to the library. Where do you see PyTorch in the near future?