deep_learning | Deep Learning Resources and Tutorials using Keras | Machine Learning library

Maybe you are more confortable using list comphension:

It seems this problem is specifically related to Pyspark. The problem can be solved by using the findspark package. Below is the quote from the findspark readme file:

PySpark isn't on sys.path by default, but that doesn't mean it can't be used as a regular library. You can address this by either symlinking pyspark into your site-packages, or adding pyspark to sys.path at runtime. findspark does the latter.

Adding the code below before initiating SparkContext solves the problem:

You need to batch your data:

Based upon my research & looking through the code :

You will not be able to use the directly Mic in a Google Collab - because the instance in which the python gets executed - you will less likely have access/operate the same. Hence you made use of the article which facilitates in recording the audio at the web browser level.

Now - the recorded audio is in the WEBM format.As per code, they further made use of the FFMPEG in order to convert to WAV format.

But however, please note that this will have the headers in addition to the audio data

Now this is not returned in the below snippet code - instead of returning the audio,sr in the get_audio() you will have to return the riff - this is the WAV AUDIO in bytes (but this includes the header in addition to the audio data)

Came accross the post which explains the composition of the WAV file at the byte level (this can be related to the output)

http://soundfile.sapp.org/doc/WaveFormat/

In this you will have to strip out the audio data bytes,sample per second and all the necessary data & use the PushAudioInputStream method

There are several misunderstandings:

• __all__ is a way to define what is importable
• you still need to import those symbols!
• you need an __init__.py also in your controllers package

Usually, Python projects, which make use of a src top level directory, have a structure like this:

using ax.patches you can achieve it.

This will do:

I suspect your test_image has an additional alpha channel per pixel, thus it has 4 channels instead on only three.
Try:

As mentioned explicitly in the documentation, cross_val_score includes a scoring argument, which is

Similar to cross_validate but only a single metric is permitted.

hence it cannot be used for returning all the loss & metric info of Keras model.fit().

The scikit-learn wrapper of Keras is meant as a convenience, provided that you are not really interested in all the underlying details (such as training & validation loss and accuracy). If this is not the case, you should revert to using Keras directly. Here is how you could do that using the example you have linked to and elements of this answer of mine:

Can you say what versions of TF and TFP you have? print(tf.__version__, tfp.__version__). I think these warnings should not be present in the latest versions.