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Searched refs:net_loss (Results 1 – 13 of 13) sorted by relevance

/third_party/mindspore/mindspore/nn/probability/toolbox/
Duncertainty_evaluation.py123 net_loss = SoftmaxCrossEntropyWithLogits(
126 model = Model(self.epi_uncer_model, net_loss,
129 net_loss = MSELoss()
131 model = Model(self.epi_uncer_model, net_loss,
178 net_loss = AleatoricLoss(self.task_type)
181 model = Model(self.ale_uncer_model, net_loss,
184 model = Model(self.ale_uncer_model, net_loss,
Danomaly_detection.py58 net_loss = ELBO()
60 net_with_loss = WithLossCell(self.vae, net_loss)
/third_party/mindspore/tests/st/quantization/lenet_quant/
Dtest_lenet_quant.py72 net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
82 model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
116 net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
121 model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
/third_party/mindspore/tests/st/broadcast/
Dlenet_broadcast_auto_parallel.py54 net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
58 model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
/third_party/mindspore/tests/st/probability/dpn/
Dtest_gpu_svi_vae.py98 net_loss = ELBO(latent_prior='Normal', output_prior='Normal')
103 net_with_loss = nn.WithLossCell(vae, net_loss)
Dtest_gpu_svi_cvae.py108 net_loss = ELBO(latent_prior='Normal', output_prior='Normal')
114 net_with_loss = CVAEWithLossCell(cvae, net_loss)
Dtest_gpu_vae_gan.py160 net_loss = VaeGanLoss()
163 net_with_loss = nn.WithLossCell(vae_gan, net_loss)
/third_party/mindspore/tests/st/ps/full_ps/
Dtest_full_ps_lenet.py127 net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") variable
129 model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
/third_party/mindspore/tests/st/probability/zhusuan/vae/
Dvae_mnist.py135 net_loss = ReduceMeanLoss()
141 model = Model(network, net_loss, net_opt)
/third_party/mindspore/tests/st/networks/
Dtest_gradient_accumulation.py215 net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
217 model = GradientAccumulation(network, net_loss, net_opt)
Dtest_gpu_lenet.py196 net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
198 model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
/third_party/mindspore/tests/st/fl/cross_silo_femnist/
Dtest_cross_silo_femnist.py306 net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
309 model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy(), 'Loss': nn.Loss()})
/third_party/mindspore/mindspore/nn/probability/
DREADME.md225 net_loss = ELBO(latent_prior='Normal', output_prior='Normal')
227 net_with_loss = nn.WithLossCell(vae, net_loss)