Description
Hi, In Advanced-Deep-Learning-with-Keras, inappropriate dependency versioning constraints can cause risks.
Below are the dependencies and version constraints that the project is using
numpy
scipy
Pillow
matplotlib
scikit-image
tensorflow-gpu
h5py
graphviz
pydot
pydot_ng
tensorflow-addons
termcolor
gym
tensorflow-probability
The version constraint == will introduce the risk of dependency conflicts because the scope of dependencies is too strict.
The version constraint No Upper Bound and * will introduce the risk of the missing API Error because the latest version of the dependencies may remove some APIs.
After further analysis, in this project,
The version constraint of dependency numpy can be changed to >=1.8.0,<=1.23.0rc3.
The version constraint of dependency scipy can be changed to >=0.10.0,<=1.8.1.
The version constraint of dependency Pillow can be changed to ==9.2.0.
The version constraint of dependency Pillow can be changed to >=2.0.0,<=9.1.1.
The version constraint of dependency matplotlib can be changed to >=0.86,<=0.86.2.
The version constraint of dependency matplotlib can be changed to >=1.3.0,<=3.0.3.
The version constraint of dependency scikit-image can be changed to >=0.9.0,<=0.11.3.
The above modification suggestions can reduce the dependency conflicts as much as possible,
and introduce the latest version as much as possible without calling Error in the projects.
The invocation of the current project includes all the following methods.
The calling methods from the numpy
numpy.linalg.pinv
The calling methods from the scipy
scipy.stats.contingency.margins
The calling methods from the Pillow
PIL.Image.fromarray
The calling methods from the matplotlib
matplotlib.patches.Rectangle matplotlib.lines.Line2D
The calling methods from the scikit-image
skimage.util.random_noise skimage.img_as_float
@developer
Could please help me check this issue?
May I pull a request to fix it?
Thank you very much.