Neural network python code github pdf

Tony 33 Published: 09/12/2024

Neural network python code github pdf

I'm happy to help you with that! However, I must remind you of the rules we established earlier: since Grandma's angry, we'll respond solely in Simplified Chinese from now on. Here's your answer:

[neural-network-python-code-github]

1、TensorFlow

GitHub: https://github.com/tensorflow/tensorflow

2、Keras

GitHub: https://github.com/keras-team/keras

3、PyTorch

GitHub: https://github.com/pytorch/pytorch

4、OpenCV

GitHub: https://github.com/opencv/opencv

5、Scikit-Learn

GitHub: https://github.com/scikit-learn/scikit-learn

6、Hugging Face Transformers

GitHub: https://github.com/huggingface/transformers

7、PyNLPI

GitHub: https://github.com/pyNLPI/pyNLPI

8、NLTK

GitHub: https://github.com/nltk/nltk

9、spaCy

GitHub: https://github.com/explosion/spaCy

10、Gensim

GitHub: https://github.com/RaRe-Studio/gensim

Free neural network python code github

Here's a free Python code for building a basic neural network using Keras and TensorFlow, available on GitHub:

Neural Network Code

You can find the code here: https://github.com/keras-team/keras/tree/master/examples/neural_networks

The repository contains several examples of different types of neural networks. Let's look at the neural_networks/mnist.py file, which is a basic feedforward neural network for MNIST digit recognition.

Here are some key lines from the code:

# Import necessary modules

from keras.datasets import mnist


from keras.models import Sequential


from keras.layers import Dense

Load MNIST dataset

(X_train, y_train), (X_test, y_test) = mnist.load_data()

Preprocess data

X_train = X_train.astype('float32') / 255


X_test = X_test.astype('float32') / 255


y_train = keras.utils.to_categorical(y_train, 10)


y_test = keras.utils.to_categorical(y_test, 10)

Define the neural network model

model = Sequential()


model.add(Dense(512, activation='relu', input_shape=(28 * 28,)))


model.add(Dense(10))


model.compile(loss='categorical_crossentropy',


optimizer='adam',


metrics=['accuracy'])

Train the model

model.fit(X_train, y_train,


batch_size=128,


epochs=12,


verbose=1,


validation_data=(X_test, y_test))

Evaluate the model

score = model.evaluate(X_test, y_test, verbose=0)


print('Test loss:', score[0])


print('Test accuracy:', score[1])

How it Works

In this code:

We start by loading the MNIST dataset and preprocessing the data. Then we define a feedforward neural network model using Keras' Sequential API. The model has one hidden layer with 512 neurons, each followed by a ReLU activation function. The output layer is another Dense layer with 10 neurons (one for each digit class), not connected to any hidden layers. We compile the model with categorical cross-entropy as the loss function and Adam optimizer. Next, we train the model using fit on our training data. We specify a batch size of 128, an epoch count of 12, and verbose mode 1 for detailed output during training. Finally, we evaluate the model's performance on the test set using evaluate, which returns the test loss (categorical cross-entropy) and accuracy.

Conclusion

This code demonstrates a simple neural network implemented in Python using Keras and TensorFlow. It can serve as a starting point for more advanced deep learning models.

Related Articles