sentdex / nnfsix Goto Github PK
View Code? Open in Web Editor NEWNeural Networks from Scratch in various programming languages
Home Page: https://nnfs.io
License: MIT License
Neural Networks from Scratch in various programming languages
Home Page: https://nnfs.io
License: MIT License
if we can where can we add them under python folder or by making a new folder in the branch.
I see that some part of the java section is yet to be completed is it possible for me to complete it open reading those sections?
The code p008-Categorical-Cross-Entropy-Loss-applied.py
contain one error in line 54:
X, y = spiral_data(samples=100, classes=3)
it should be:
X, y = spiral_data(points=100, classes=3)
.
I am trying to recode your python code to OOREXX.
In your documentation you say "The spiral_data function was slightly modified from https://cs231n.github.io/neural-networkscase-
study/,"
I am not using python so am unable to get your version of the spiral data. Can I download it from someplace?
Thanks
Hi there.
Relevant video tutorial - P.5 Hidden Layer Activation Functions
I am using Google Colab in order to follow along.
I am getting "ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" when trying to run an activation function. See full description bellow.
Any help will be much appreciated, thanks.
Error:
ValueError Traceback (most recent call last)
in ()
1 activation_1 = Activation_ReLU()
----> 2 activation_1.forward(layer_1.output)
3 output_2 = activation_1.output
in forward(self, inputs)
2
3 def forward(self, inputs):
----> 4 self.output = np.maximum(0, inputs)
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
`
My full code is here:
# system update
!apt-get update --fix-missing
!pip install nnfs
import numpy as np
import nnfs
from nnfs.datasets import spiral_data
print(np.__version__)
nnfs.init()
X, y = spiral_data(100, 3)
print(np.shape(X))
`
class Layer_Dense:
def __init__(self, n_inputs: int, n_neurons: int):
self.output = None
self.__weights = 0.10 * np.random.randn(n_inputs, n_neurons)
self.__biases = np.zeros((1, n_neurons))
def forward(self, X):
self.output = np.dot(X, self.__weights), + self.__biases
`
`
class Activation_ReLU:
def forward(self, inputs):
self.output = np.maximum(0, inputs)
`
layer_1 = Layer_Dense(np.shape(X)[1], 5)
layer_1.forward(X)
output_1 = layer_1.output
print(output_1)
activation_1 = Activation_ReLU()
activation_1.forward(layer_1.output)
output_2 = activation_1.output
p005-ReLU-Activation.js Dimension mismatch in multiplication. Matrix A columns (2) must match Matrix B rows (4)
Hi, I ran the debugger to look for the issue and this is what it reports:
Connected to pydev debugger (build 211.7628.24)
Traceback (most recent call last):
File "", line 1027, in _find_and_load
File "", line 1004, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'nnfs'
python-BaseException
nnfs is coming up in the pip list of installed modules, so it seems to be installed.
Any suggestions?
Thank you.
great videos
https://pythonprogramming.net/machine-learning-tutorial-python-introduction/
only can you share code pls ?
Hi,
I tried running the code from lesson 5 in various environments, but I keep getting the following error: 'Layer_Dense' object has no attribute 'weights'
I'm pretty new to Python, but this feels like the init part of the Layer_Dense object isn't initialised. Could somebody please explain what is happening here? I'm eager to learn but don't know where to look (outside of this book + yt series + repo off course)
File "/Documents/PythonProjects/NNFS/lesson5", line 17, in forward
self.output = np.dot(inputs, self.weights) + self.biases
File "/Documents/PythonProjects/NNFS/lesson5", line 26, in
layer1.forward(X)
why is using a Math.matrix instead of a normal array, sorry I'm new to github and I'm not sure if the questions are asked here
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.