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layer make_activation_layer(int batch, int inputs, ACTIVATION activation) | |
{ | |
layer l = {0}; | |
l.type = ACTIVE; | |
l.inputs = inputs; | |
l.outputs = inputs; | |
l.batch=batch; | |
l.output = calloc(batch*inputs, sizeof(float*)); | |
l.delta = calloc(batch*inputs, sizeof(float*)); | |
l.forward = forward_activation_layer; | |
l.backward = backward_activation_layer; | |
l.forward_gpu = forward_activation_layer_gpu; | |
l.backward_gpu = backward_activation_layer_gpu; | |
l.output_gpu = cuda_make_array(l.output, inputs*batch); | |
l.delta_gpu = cuda_make_array(l.delta, inputs*batch); | |
l.activation = activation; | |
fprintf(stderr, "Activation Layer: %d inputs\n", inputs); | |
return l; | |
} | |
void forward_activation_layer(layer l, network net) | |
{ | |
copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1); | |
activate_array(l.output, l.outputs*l.batch, l.activation); | |
} | |
void backward_activation_layer(layer l, network net) | |
{ | |
gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); | |
copy_cpu(l.outputs*l.batch, l.delta, 1, net.delta, 1); | |
} | |
void forward_activation_layer_gpu(layer l, network net) | |
{ | |
copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1); | |
activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation); | |
} | |
void backward_activation_layer_gpu(layer l, network net) | |
{ | |
gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); | |
copy_gpu(l.outputs*l.batch, l.delta_gpu, 1, net.delta_gpu, 1); | |
} | |