We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
FM.py中 elif self.loss_type == 'log_loss': self.out = tf.sigmoid(self.out) if self.lambda_bilinear > 0: self.loss = tf.contrib.losses.log_loss(self.out, self.train_labels, weight=1.0, epsilon=1e-07, scope=None) + tf.contrib.layers.l2_regularizer(self.lamda_bilinear)(self.weights['feature_embeddings']) # regulizer else: self.loss = tf.contrib.losses.log_loss(self.out, self.train_labels, weight=1.0, epsilon=1e-07, scope=None)
self.lambda_bilinear 变量有误,应该是 self.lamda_bilinear
The text was updated successfully, but these errors were encountered:
No branches or pull requests
FM.py中
elif self.loss_type == 'log_loss':
self.out = tf.sigmoid(self.out)
if self.lambda_bilinear > 0:
self.loss = tf.contrib.losses.log_loss(self.out, self.train_labels, weight=1.0, epsilon=1e-07, scope=None) + tf.contrib.layers.l2_regularizer(self.lamda_bilinear)(self.weights['feature_embeddings']) # regulizer
else:
self.loss = tf.contrib.losses.log_loss(self.out, self.train_labels, weight=1.0, epsilon=1e-07, scope=None)
self.lambda_bilinear 变量有误,应该是 self.lamda_bilinear
The text was updated successfully, but these errors were encountered: