def test_cost_layer(self):
cost1 = layer.classification_cost(input=inference, label=label)
cost2 = layer.classification_cost(input=inference,
label=label,
weight=weight)
cost3 = layer.cross_entropy_cost(input=inference, label=label)
cost4 = layer.cross_entropy_with_selfnorm_cost(input=inference,
label=label)
cost5 = layer.mse_cost(input=inference, label=label)
cost6 = layer.mse_cost(input=inference, label=label, weight=weight)
cost7 = layer.multi_binary_label_cross_entropy_cost(input=inference,
label=label)
cost8 = layer.rank_cost(left=score, right=score, label=score)
cost9 = layer.lambda_cost(input=inference, score=score)
cost10 = layer.sum_cost(input=inference)
cost11 = layer.huber_cost(input=score, label=label)
print layer.parse_network(cost1, cost2)
print layer.parse_network(cost3, cost4)
print layer.parse_network(cost5, cost6)
print layer.parse_network(cost7, cost8, cost9, cost10, cost11)
crf = layer.crf(input=inference, label=label)
crf_decoding = layer.crf_decoding(input=inference, size=3)
ctc = layer.ctc(input=inference, label=label)
warp_ctc = layer.warp_ctc(input=pixel, label=label)
nce = layer.nce(input=inference, label=label, num_classes=3)
hsigmoid = layer.hsigmoid(input=inference, label=label, num_classes=3)
print layer.parse_network(crf, crf_decoding, ctc, warp_ctc, nce,
hsigmoid)
def test_cost_layer(self):
cost1 = layer.classification_cost(input=inference, label=label)
cost2 = layer.classification_cost(
input=inference, label=label, weight=weight)
cost3 = layer.cross_entropy_cost(input=inference, label=label)
cost4 = layer.cross_entropy_with_selfnorm_cost(
input=inference, label=label)
cost5 = layer.square_error_cost(input=inference, label=label)
cost6 = layer.square_error_cost(
input=inference, label=label, weight=weight)
cost7 = layer.multi_binary_label_cross_entropy_cost(
input=inference, label=label)
cost8 = layer.rank_cost(left=score, right=score, label=score)
cost9 = layer.lambda_cost(input=inference, score=score)
cost10 = layer.sum_cost(input=inference)
cost11 = layer.huber_regression_cost(input=score, label=label)
cost12 = layer.huber_classification_cost(input=score, label=label)
print layer.parse_network([cost1, cost2])
print layer.parse_network([cost3, cost4])
print layer.parse_network([cost5, cost6])
print layer.parse_network([cost7, cost8, cost9, cost10, cost11, cost12])
crf = layer.crf(input=inference, label=label)
crf_decoding = layer.crf_decoding(input=inference, size=3)
ctc = layer.ctc(input=inference, label=label)
warp_ctc = layer.warp_ctc(input=pixel, label=label)
nce = layer.nce(input=inference, label=label, num_classes=3)
hsigmoid = layer.hsigmoid(input=inference, label=label, num_classes=3)
print layer.parse_network(
[crf, crf_decoding, ctc, warp_ctc, nce, hsigmoid])