Download the latest Nvidia driver on http://www.nvidia.com/drivers
Update the kernel to the latest version
$ yum update
Change to runlevel 3 - multi user mode for the next reboot
cqt_filter_fft = librosa.constantq.__cqt_filter_fft | |
class PseudoCqt(): | |
"""A class to compute pseudo-CQT with Pytorch. | |
Written by Keunwoo Choi | |
API (+implementations) follows librosa (https://librosa.github.io/librosa/generated/librosa.core.pseudo_cqt.html) | |
Usage: | |
src, _ = librosa.load(filename) | |
src_tensor = torch.tensor(src) |
"""An example of how to use tf.Dataset in Keras Model""" | |
import tensorflow as tf # only work from tensorflow==1.9.0-rc1 and after | |
_EPOCHS = 5 | |
_NUM_CLASSES = 10 | |
_BATCH_SIZE = 128 | |
def training_pipeline(): | |
# ############# | |
# Load Dataset |
Download the latest Nvidia driver on http://www.nvidia.com/drivers
Update the kernel to the latest version
$ yum update
Change to runlevel 3 - multi user mode for the next reboot
MIT License | |
Copyright (c) 2019 endolith | |
Permission is hereby granted, free of charge, to any person obtaining a copy | |
of this software and associated documentation files (the "Software"), to deal | |
in the Software without restriction, including without limitation the rights | |
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
copies of the Software, and to permit persons to whom the Software is | |
furnished to do so, subject to the following conditions: |