•添加tf.contrib.nn.rank_sampled_softmax_loss,这是一个可以提高秩损失(rank loss)的采样softmax变体。 •当他们看到小于或等于1个单位的权重时,tf.contrib.metrics {streaming_covariance,streaming_pearson_correlation}修改为返回nan。 •在contrib
添加tf.contrib.nn.rank_sampled_softmax_loss,这是一个可以提高秩损失(rank loss)的采样softmax变体。 ?当他们看到小于或等于1个单位的权重时,tf.contrib.metrics {streaming_covariance,streaming_pearson_correlation}修改为返回nan。 ?在contrib中添加时间序列模型。有关详细信息,请参阅contrib / timeseries / REA...
on given datasets. We do not reportKendall rank correlation coefficient(KRCC) as it is highly correlated with SRCC and provides limited additional information. We do not reportPearson linear correlation coefficient(PLCC) as it's highly dependent on fitting method and is biased towards simple ...
cuda() loss = model( seq, head = 'human', target = target ) loss.backward() # after much training corr_coef = model( seq, head = 'human', target = target, return_corr_coef = True ) corr_coef # pearson R, used as a metric in the paper Pretrained Model Deepmind has released ...
添加tf.contrib.nn.rank_sampled_softmax_loss,这是一个可以提高秩损失(rank loss)的采样softmax变体。 当他们看到小于或等于1个单位的权重时,tf.contrib.metrics {streaming_covariance,streaming_pearson_correlation}修改为返回nan。 在contrib中添加时间序列模型。有关详细信息,请参阅contrib / timeseries / README...
•添加tf.contrib.nn.rank_sampled_softmax_loss,这是一个可以提高秩损失(rank loss)的采样softmax变体。 •当他们看到小于或等于1个单位的权重时,tf.contrib.metrics {streaming_covariance,streaming_pearson_correlation}修改为返回nan。 •在contrib中添加时间序列模型。有关详细信息,请参阅contrib / timeserie...
tf.contrib.nn.rank_sampled_softmax_loss,这是一个可以提高秩损失(rank loss)的采样softmax变体。 •当他们看到小于或等于1个单位的权重时,tf.contrib.metrics {streaming_covariance,streaming_pearson_correlation}修改为返回nan。 •在contrib中添加时间序列模型。有关详细信息,请参阅contrib / timeseries / RE...
on given datasets. We do not reportKendall rank correlation coefficient(KRCC) as it is highly correlated with SRCC and provides limited additional information. We do not reportPearson linear correlation coefficient(PLCC) as it's highly dependent on fitting method and is biased towards simple ...
cuda() loss = model( seq, head = 'human', target = target ) loss.backward() # after much training corr_coef = model( seq, head = 'human', target = target, return_corr_coef = True ) corr_coef # pearson R, used as a metric in the paper Pretrained Model Deepmind has released ...
on given datasets. We do not reportKendall rank correlation coefficient(KRCC) as it is highly correlated with SRCC and provides limited additional information. We do not reportPearson linear correlation coefficient(PLCC) as it's highly dependent on fitting method and is biased towards simple ...