ncxlib
  • ⚡Welcome
  • Getting Started
    • Quickstart
    • API Documentation
      • Overview
        • Neural Network
          • _compile
          • add_layer
          • forward_propagate_all
          • forward_propagate_all_no_save
          • back_propagation
          • train
          • predict
          • evaluate
          • save_model
          • load_model
        • Activation
          • ReLU
          • LeakyReLU
          • Sigmoid
          • Softmax
          • Tanh
        • Layer
          • InputLayer
          • FullyConnectedLayer
          • OutputLayer
        • LossFunction
          • MeanSquaredError
          • BinaryCrossEntropy
          • CategoricalCrossEntropy
        • Optimizer
          • SGD
          • SGDMomentum
          • RMSProp
          • Adam
        • Initializer
          • HeNormal
          • Zero
        • PreProcessor
          • OneHotEncoder
          • MinMaxScaler
          • Scaler
          • ImageRescaler
          • ImageGrayscaler
        • DataLoader
          • CSVDataLoader
          • ImageDataLoader
        • Generators
          • random_array
          • integer_array
          • generate_training_data
        • Utils
          • train_test_split
          • k_fold_cross_validation
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  4. Layer

InputLayer

class InputLayer(Layer):
    def __init__(
        self, n_neurons=None, n_inputs=None,  activation=..., optimizer=...
    ):
        super().__init__(
            n_neurons, n_inputs, name="input"
        )
    
    def initialize_params(self, inputs):
        self.layer.initialize_params()

    def forward_propagation(self, inputs):
        return inputs
    
    def back_propagation(self, y_orig, y_pred):
        return super().back_propagation(y_orig, y_pred)
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Last updated 7 months ago