Neural-Networks-from-scratch | MLP , CNN , RNN , LSTM from scratch | Machine Learning library
kandi X-RAY | Neural-Networks-from-scratch Summary
kandi X-RAY | Neural-Networks-from-scratch Summary
Neural-Networks-from-scratch is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Neural Network applications. Neural-Networks-from-scratch has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Neural-Networks-from-scratch build file is not available. You can download it from GitHub.
This repository is for my own learning purpose. The goal is to learn how forward as well as backpropagation works in:.
This repository is for my own learning purpose. The goal is to learn how forward as well as backpropagation works in:.
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Neural-Networks-from-scratch has a low active ecosystem.
It has 24 star(s) with 10 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
Neural-Networks-from-scratch has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Neural-Networks-from-scratch is current.
Quality
Neural-Networks-from-scratch has 0 bugs and 0 code smells.
Security
Neural-Networks-from-scratch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Neural-Networks-from-scratch code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
Neural-Networks-from-scratch is licensed under the Unlicense License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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Neural-Networks-from-scratch releases are not available. You will need to build from source code and install.
Neural-Networks-from-scratch has no build file. You will be need to create the build yourself to build the component from source.
Top functions reviewed by kandi - BETA
kandi has reviewed Neural-Networks-from-scratch and discovered the below as its top functions. This is intended to give you an instant insight into Neural-Networks-from-scratch implemented functionality, and help decide if they suit your requirements.
- Computes the loss function
- Backpropagate back - propagation
- Computes the softmax of the input array
- Create inputs from text
- Train the given image
- Compute the output of each region
- Calculate the region of each region
- Iterate over the regions of an image
- Creates a binary dataset
- Reshapes mnist data
- Reshapes images
- Get the MNIST dataset
- Fit the model
- Compute the covariance matrix
- Implementation of the sigmoid function
- Predict the probability for each sample
- Preprocessing function
- Get MNIST dataset
- Visualize the MNIST dataset
- Plot cost
Get all kandi verified functions for this library.
Neural-Networks-from-scratch Key Features
No Key Features are available at this moment for Neural-Networks-from-scratch.
Neural-Networks-from-scratch Examples and Code Snippets
No Code Snippets are available at this moment for Neural-Networks-from-scratch.
Community Discussions
Trending Discussions on Neural-Networks-from-scratch
QUESTION
How to add one more hidden layer in ANN? And Low accuracy
Asked 2019-Dec-30 at 17:33
I am following this guide using the "moons" dataset: https://vincentblog.xyz/posts/neural-networks-from-scratch-in-python. I would like to add one more hidden layer (also 4 neurons), so how can I extend it? I am confused specifically on the feedforward and backpropagation part if I add one more hidden layer. The code below is only for one hidden layer
...ANSWER
Answered 2019-Dec-30 at 16:49def forward_propagation(X, W1, b1, W2, b2, W3, b3):
forward_params = {}
Z1 = np.dot(W1, X.T) + b1
A1 = relu(Z1)
Z2 = np.dot(W2, A1) + b2
A2 = relu(Z2)
Z3 = np.dot(W3, A2) + b3
A3 = sigmoid(Z3)
forward_params = {
"Z1": Z1,
"A1": A1,
"Z2": Z2,
"A2": A2,
"Z3": Z3,
"A3": A3
}
return forward_params
def backward_propagation(forward_params, X, Y):
A3 = forward_params["A3"]
Z3 = forward_params["Z3"]
A2 = forward_params["A2"]
Z2 = forward_params["Z2"]
A1 = forward_params["A1"]
Z1 = forward_params["Z1"]
data_size = Y.shape[1]
dZ3 = A3 - Y
dW3 = np.dot(dZ3, A2.T) / data_size
db3 = np.sum(dZ3, axis=1) / data_size
dZ2 = np.dot(dW3.T, dZ3) * prime_relu(Z2)
dW2 = np.dot(dZ2, A1.T) / data_size
db2 = np.sum(dZ2, axis=1) / data_size
db2 = np.reshape(db2, (db2.shape[0], 1))
dZ1 = np.dot(dW2.T, dZ2) * prime_relu(Z1)
dW1 = np.dot(dZ1, X) / data_size
db1 = np.sum(dZ1, axis=1) / data_size
db1 = np.reshape(db1, (db1.shape[0], 1))
grads = {
"dZ3": dZ3,
"dW3": dW3,
"db3": db3,
"dZ2": dZ2,
"dW2": dW2,
"db2": db2,
"dZ1": dZ1,
"dW1": dW1,
"db1": db1,
}
return grads
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Neural-Networks-from-scratch
You can download it from GitHub.
You can use Neural-Networks-from-scratch like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use Neural-Networks-from-scratch like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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