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| | | typedef struct{ |
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| | | int inputs; |
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| | | int outputs; |
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| | | double *weights; |
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| | | double *biases; |
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| | | float *weights; |
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| | | float *biases; |
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| | | |
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| | | double *weight_updates; |
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| | | double *bias_updates; |
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| | | float *weight_updates; |
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| | | float *bias_updates; |
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| | | |
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| | | double *weight_momentum; |
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| | | double *bias_momentum; |
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| | | float *weight_momentum; |
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| | | float *bias_momentum; |
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| | | |
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| | | double *output; |
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| | | double *delta; |
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| | | float *output; |
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| | | float *delta; |
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| | | |
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| | | ACTIVATION activation; |
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| | | |
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| | |
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| | | |
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| | | connected_layer *make_connected_layer(int inputs, int outputs, ACTIVATION activation); |
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| | | |
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| | | void forward_connected_layer(connected_layer layer, double *input); |
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| | | void backward_connected_layer(connected_layer layer, double *input, double *delta); |
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| | | void learn_connected_layer(connected_layer layer, double *input); |
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| | | void update_connected_layer(connected_layer layer, double step, double momentum, double decay); |
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| | | void forward_connected_layer(connected_layer layer, float *input); |
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| | | void backward_connected_layer(connected_layer layer, float *input, float *delta); |
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| | | void learn_connected_layer(connected_layer layer, float *input); |
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| | | void update_connected_layer(connected_layer layer, float step, float momentum, float decay); |
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| | | |
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| | | |
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| | | #endif |
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