p8_TextRNN_model.py 文件源码

def loss_nce(self,l2_lambda=0.0001): #0.0001-->0.001
        """calculate loss using (NCE)cross entropy here"""
        # Compute the average NCE loss for the batch.
        # tf.nce_loss automatically draws a new sample of the negative labels each
        # time we evaluate the loss.
        if self.is_training: #training
            #labels=tf.reshape(self.input_y,[-1])               #[batch_size,1]------>[batch_size,]
            labels=tf.expand_dims(self.input_y,1)                   #[batch_size,]----->[batch_size,1]
            loss = tf.reduce_mean( #inputs: A `Tensor` of shape `[batch_size, dim]`.  The forward activations of the input network.
                tf.nn.nce_loss(weights=tf.transpose(self.W_projection),#[hidden_size*2, num_classes]--->[num_classes,hidden_size*2]. nce_weights:A `Tensor` of shape `[num_classes, dim].O.K.
                               biases=self.b_projection,                 #[label_size]. nce_biases:A `Tensor` of shape `[num_classes]`.
                               labels=labels,                 #[batch_size,1]. train_labels, # A `Tensor` of type `int64` and shape `[batch_size,num_true]`. The target classes.
                               inputs=self.output_rnn_last,# [batch_size,hidden_size*2] #A `Tensor` of shape `[batch_size, dim]`.  The forward activations of the input network.
                               num_sampled=self.num_sampled,  #scalar. 100
                               num_classes=self.num_classes,partition_strategy="div"))  #scalar. 1999
        l2_losses = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'bias' not in v.name]) * l2_lambda
        loss = loss + l2_losses
        return loss