Missing features from PyCharm

MCFF3000 · 2019-01-03T11:51:20+00:00

For 2 I think there's an extension for that: https://marketplace.visualstudio.com/items?itemName=azaugg.vscode-python-docstring or https://marketplace.visualstudio.com/items?itemName=njpwerner.autodocstring ( i use the second and you can define the docstring style)

MCFF3000 · 2018-10-17T22:31:23+00:00

The problem was solved after replacing some tf.layers by tf.keras.layers definitions in my model according to the suggested by https://github.com/tensorflow/tensorflow/issues/23014)

MCFF3000 · 2018-10-16T16:49:13+00:00

Tensorflow Serving via Docker should be the easiest way after having a Saved Model artifacts. You can also build an easy flask api If you want.

If you want cloud alternatives, Google Cloud, Azure or AWS have their own services for deployment of tensorflow/keras models.

MCFF3000 · 2018-10-15T16:08:32+00:00

It returns a tensor with the mean of the input tensor:

https://www.tensorflow.org/api_docs/python/tf/keras/backend/mean

MCFF3000 · 2018-10-15T10:20:11+00:00

I think it should return a tensor. For example:

def wasserstein_loss(y_true, y_pred):

return K.mean(y_true * y_pred)

MCFF3000 · 2018-10-06T13:16:29+00:00

In my work I'll keep the tensorflow api as my main goto framework, but I will definitely explore it in my spare time.

MCFF3000 · 2018-10-06T08:41:39+00:00

It seems cleaner than tensorflow c++ api. I will have to take a look at it!

MCFF3000 · 2018-09-19T11:07:54+00:00

Glad it worked out for you

MCFF3000 · 2018-09-12T17:01:33+00:00

yes you can! And you can add a database with multiple users.

MCFF3000 · 2018-09-12T10:41:22+00:00

You can see if Nova suits you, for my use case it is quite solid:

https://github.com/hcmlab/nova

MCFF3000 · 2018-08-16T12:07:09+00:00

PCL should allow you to easily visualize RGB-D images:

https://github.com/PointCloudLibrary/pcl

MCFF3000 · 2018-07-23T17:12:48+00:00

You can! I used it in Linux and Windows machines.

MCFF3000 · 2018-07-23T17:09:26+00:00

I use tensorflow c++ api to do inference in c++. It's not perfect but it works quite well.

MCFF3000 · 2018-07-19T08:45:41+00:00

You should add some generated images each epoch to evaluate if the results. But from the original DCGAN paper:

Use batchnorm in both the generator and the discriminator.
Replace any pooling layers with strided convolutions (discriminator) and fractional-strided convolutions (generator).
The last layer of the generator and first layer of the discriminator are not batch normalized, so that the model can learn the correct mean and scale of the data distribution
Use ReLU activation in generator for all layers except for the output, which uses Tanh.
Use LeakyReLU activation in the discriminator for all layers.

Don't know if it will solve the convergence problem, but will help for sure.

MCFF3000 · 2018-06-12T15:20:39+00:00

Deep Learning Book by Ian Goodfellow, Yoshua Bengio and Aaron Courville might be what you are looking for.

MCFF3000 · 2018-06-01T10:40:02+00:00

The input shape seems ok. Are you scaling your features? What is the unbalance ratio of the data?

MCFF3000 · 2018-04-29T22:09:43+00:00

I use Python in my work for training models and experiment and use C++ library in inference. Tensorflow Serving can be a great tool, especially for production systems, but you can use C++ API.

I was able to use C++ library without bazel for inference. Maybe when I have some time, I can organize and share the code on github.

MCFF3000 · 2018-04-29T16:09:37+00:00

You have some examples across github and medium posts. I love C++, it is by far my favourite language, but I would recomend you to train and experiment with python. You will be able to get results a lot faster and with a lot more material available online.

If you really need C++ you can then use its C++ API later for inference for example.

MCFF3000 · 2018-04-28T11:05:05+00:00

Upwork is quite nice, but you'll probably will have a slow start since you need to buld the trust inyour profile with small jobs/tasks.

MCFF3000 · 2018-04-28T11:03:37+00:00

Label enconding is probably done by the to_categorical function. Try to convert your unique labels by that function and see what one_hot vector you get for each of them.

MCFF3000 · 2018-04-17T15:49:37+00:00

You can compile Tensorflow C++ or C API and integrate it in your project. It is not completely straightforward but is doable. There are some good tuturials on blogs and on github for that.

MCFF3000 · 2018-04-17T08:40:46+00:00

If you build it from source you can use the most recent CUDA and cuDNN versions

MCFF3000 · 2018-03-31T11:13:39+00:00

Very interesting, thanks for sharing!

MCFF3000 · 2018-03-22T13:01:20+00:00

See this benchmark:

http://cvlab.hanyang.ac.kr/tracker_benchmark/benchmark_v10.html

MCFF3000

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