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Fixed nan issue for training with CUDNN_HALF=1 by using Tensor Cores

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AlexeyAB
2018-12-07 22:40:10 +03:00
parent 21a4ec9390
commit 7c2f302321
9 changed files with 318 additions and 249 deletions
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6
src/crnn_layer.c
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@ -48,17 +48,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou
l.input_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.input_layer) = make_convolutional_layer(batch*steps, h, w, c, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0);
*(l.input_layer) = make_convolutional_layer(batch*steps, h, w, c, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0);
l.input_layer->batch = batch;
l.self_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.self_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0);
*(l.self_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0);
l.self_layer->batch = batch;
l.output_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.output_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, output_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0);
*(l.output_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, output_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0);
l.output_layer->batch = batch;
l.output = l.output_layer->output;