Phidnet
Project description
Phidnet
1. Introduction to phidnet
- Phidnet is a library developed for neural network construction and deep learning.
2. Install phidnet
pip install phidnet
- PyPI url: https://pypi.org/project/phidnet/
3. Requirements of phidnet
- numpy
- matplotlib
- pandas(Optional)
4. Use phidnet
- Import phidnet
- import phidnet
- Numpy
- All data, such as matrix and vector, must be converted to numpy array object.
- Configuration of the Piednet
- phidnet.activation
- phidnet.optimizer
- phidnet.load
- phidnet.matrix
- phidnet.set
- phidnet.one_hot_encode
- phidnet.model
- Define activation function
- Sigmoid = phidnet.activation.Sigmoid()
- Relu = phidnet.activation.Relu()
- ect
- Define optimizer
- SGD = phidnet.optimizer.SGD(lr=0.01) # lr: learning rate
- Momentum = phidnet.optimizer.Momentum(lr=0.01, momentum=0.9)
- AdaGrad = phidnet.optimizer.AdaGrad(lr=0.01)
- Set data
- Set input data
- phidnet.set.input_data(X)
- Set output data
- phidnet.set.target_data(T)
- Set input data
- Set weight and bias
- phidnet.set.weight(row, column, layer=layer)
- phidnet.set.bias(column, layer=layer)
- phidnet.set.weight(2, 10, layer=1) # 2×10 matrix, 1st layer
- phidnet.set.bias(10, layer=1) # 1×10 matrix, 1st layer
- Build neural network
- phidnet.set.build_network(layer)
- The number of layers is the total number of layers, excluding the input layer. For example, a network with one input layer, one output layer, and one hidden layer in between is a two-layer.
- Set activation function of neural network
- phidnet.set.activation_function(function_list)
- phidnet.set.activation_function([Sigmoid, Sigmoid]) # 1st layer: Sigmoid, 2nd layer: Sigmoid
- The example is the activation functions of the two-layer and Sigmoid, an element of list, is the instance of phid.activation.Sigmoid() class
- Fit model
- phidnet.model.fit(epoch=1000, optimizer=SGD, print_rate=100, save=True)
- In the example, train the model for epoch. SGD is the instance of phid.optimizer.SGD() class. Every 100 epoch, print the loss, accuracy of model(print rate). If save= is true, save weight and bias in pickle. Default: save=False
- Predict
- predicted = phidnet.model.predict(input, exponential=True, precision=2)
- In the example, the model returns the predicted value in the predicted variable. If exponential= is True, the model returns exponential representation value like 1e-6. When exponential=False, The model returns the value represented by the decimal like 0.018193. The model returns precise values as set to precision. When output is 0.27177211, precision=3, output is 0.271.
- Load
- phidnet.load.model('C:\examples')
- If you set it to save=True and trained the model, there would be a file called saved_weight, saved_bias. If the file is in C:\examples\saved_... , you can load trained weight and bias as in the example.
- View fitting
- phid.model.show_fit()
- It shows a change in loss and accuracy.
- One hot encoding
- phidnet.one_hot_encode.encode(number, length=length)
- phidnet.one_hot_encode.encode(3, length=5) # [0, 0, 0, 1, 0]
- phidnet.one_hot_encode.encode_array(array, length=length)
- phidnet.one_hot_encode.encode_array([[1], [2], [3]], length=5) # [[0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0]]
- phidnet.one_hot_encode.get_number(one_hot_encoded)
- phidnet.one_hot_encode.get_number([0, 0, 1, 0, 0]) # 2
- Matrix operations
- m = phid.matrix.matrix(list) # It converts the list into a matrix (※ phidnet matrix object. not numpy object)
- ect.
5. Use phidnet's convolution neural network
- ect.
6. Example phidnet
- Refer to examples for details.
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