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Phidnet

Project description

Phidnet


1. Introduction to phidnet

  • Phidnet is a library developed for neural network construction for deep learning, machine learning, and statistics.

2. Install phidnet


3. Requirements of phidnet

  • numpy
  • matplotlib

4. Use phidnet

  • Import phidnet

    • import phidnet
  • Numpy

    • All data, such as matrix and vector, must be converted to numpy array object.
    • Will be replaced by the built-in matrix library of the phidnet.
  • Configuration of the Phidnet

    • 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 layer

    • phidnet.set.layer(784)
    • phidnet.set.layer(200, activation=Sigmoid)
    • phidnet.set.layer(10, activation=Sigmoid)
    • If you did not set the activation function, that layer becomes input layer(Input layer does not have activation function.) and if you want to build hidden & output layer, you need to set activation function.
  • Compile neural network

    • phidnet.set.compile(input=X, target=T)
    • If you built the model, you can compile that model with setting input and output data.
  • Set test dataset

    • phidnet.set.test(input=X_test, target=T_test)
    • If you want to calculate loss of test dataset(val_loss=True), you need to set this.
  • Fit model

    • phidnet.model.fit(epoch=30, optimizer=SGD, batch=5000, val_loss=True, print_rate=2, save=True)
    • In the example, train the model for epoch.
    • SGD is the instance of phidnet.optimizer.SGD() class.
    • Batch size is 5000.
    • val_loss is loss of test dataset. This helps prevent overfitting. but, calculating this makes the fitting slow.
    • Every 2 epoch, print the loss and accuracy of model(print rate).
  • 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.
  • Save

    • You can save the model with .pickle file.
    • phidnet.save.model('saved_model')
    • It saves trained model in current directory.
    • phidnet.save.model('saved_model', dir='C:\examples')
    • It saves trained model in C:\examples directory.
  • Load

    • phidnet.load.model('C:\examples\saved_model.pickle')
    • You can load trained model.
  • View fitting

    • phidnet.model.show_loss()
    • It shows a change in loss and validation loss.
    • phidnet.model.show_accuracy()
    • It shows a change in 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
  • Pre-prepared datasets

    • X, T, X_test, T_test = phidnet.datasets.mnist.load()
    • It loads mnist dataset with 1d shape. (784)
    • X, T, X_test, T_test = phidnet.datasets.mnist.load_2d()
    • It loads mnist dataset with 2d shape. (28, 28)

5. Use phidnet matrix

  • Converting to matrix

    • mat = phidnet.array(list)
  • Add, Multiplication, Subtraction

    • Equal to other classes of operations
    • mat1 + mat2, mat1 - mat2, mat1 * mat2
    • mat + 1, mat * 2, mat / 3
  • Dot product

    • phidnet.matrix.dot(mat1, mat2)
  • Index of matrix

    • If you used a regular Python index, it is not suitable for two-dimensional arrays. For example, [1][2] does not point to row 1 and column 2. A two-dimensional array is a shape with an array in array, and Python views the array as one element.
    • The solution is to use the indexing, slicing functions built into the phidnet.
    • Python index: mat[1][2] (does not point to row 1, column 2)
    • Phidnet index: mat("1,2") (point to row 1, column 2)
    • Python slicing: mat[1:3][:8] (does not point row 1-2, column 0-7)
    • Phidnet slicing: mat["1:3,:8"] (point row 1-2, column 0-7)
  • Slicing of matrix(by index)

    • sliced_matrix = phidnet.matrix.slice_full(mat, row_start, row_end, column_start, column_end)
    • sliced_matrix = phidnet.matrix.slice_full(mat, 1, 2, 1, 1)
    • 1-2 row, 1-1 column (0 based index)
  • Slicing of matrix(by python slicing)

    • sliced_matrix = mat["slicing with string"]
    • sliced_matrix = mat["1:3,1:2"]
    • 1-2 row, 1-1 column (0 based index)
    • sliced_matrix = mat[",1:2"]
    • all row, 1-1 column (0 based index)
  • Transpose matrix

    • transposed_matrix = phidnet.Matrix.trans(mat)
    • transposed_matrix = mat.trans()
  • Map

    • def function(x): return 2*x
    • mapped_matrix = phidnet.Matrix.map(mat, function)
  • Power

    • pow_matrix = mat ** n
    • mat^n (for every element in matrix)
  • Else

    • .
    • .

6. Use phidnet convolution neural network

  • Set layer
    • .
    • .
  • writing
    • .
    • .

7. Use phidnet recurrent neural network

  • Set layer
    • .
    • .
  • writing
    • .
    • .

8. Example phidnet

  • Refer to examples for details.

Project details


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