Metadata-Version: 2.1
Name: phidnet
Version: 0.0.3
Summary: Phidnet
Home-page: https://github.com/Intipy/phidnet.git
Author: Intipy
Author-email: jios6790@gmail.com
License: UNKNOWN
Description: # Phidnet
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        ## 1. Introduction to phidnet
          * Phidnet is a library developed for neural network construction and deep learning.
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        ## 2. Install phidnet
          * `pip install phidnet`
          * PyPI url: https://pypi.org/project/phidnet/
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        ## 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=0.5)
            + ect
          * Set data
            + Set input data
              + phidnet.set.input_data(X)
            + Set output data
              + phidnet.set.target_data(T)
          * Set weight and bias
            + phidnet.set.weight(row, column, layer)
            + phidnet.set.bias(column, layer)
            + phidnet.set.weight(2, 10, 1)  # 2×10 matrix, 1st layer
            + phidnet.set.bias(10, 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.
          * Matrix operations 
            + m = phid.matrix.matrix(list)  # It converts the list into a matrix (※ phidnet matrix object. not numpy object)
            + K.W.
        
        
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        ## 4. Example phidnet
          * Refer to examples for details.
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
