losses.py 文件源码

def calculate_loss_mix2(self, predictions, predictions_class, predictions_encoder, labels, **unused_params):
    with tf.name_scope("loss_mix2"):
      float_labels = tf.cast(labels, tf.float32)
      float_encoders = float_labels
      for i in range(FLAGS.encoder_layers):
        var_i = np.loadtxt(FLAGS.autoencoder_dir+'autoencoder_layer%d.model' % i)
        weight_i = tf.constant(var_i[:-1,:],dtype=tf.float32)
        bias_i = tf.reshape(tf.constant(var_i[-1,:],dtype=tf.float32),[-1])
        float_encoders = tf.nn.xw_plus_b(float_encoders,weight_i,bias_i)
        if i<FLAGS.encoder_layers-1:
          float_encoders = tf.nn.relu(float_encoders)
        else:
          hidden_mean = tf.reduce_mean(float_encoders,axis=1,keep_dims=True)
          hidden_std = tf.sqrt(tf.reduce_mean(tf.square(float_encoders-hidden_mean),axis=1,keep_dims=True))
          float_encoders = (float_encoders-hidden_mean)/(hidden_std+1e-6)
          #float_encoders = tf.nn.sigmoid(float_encoders)
      cross_entropy_encoder = 0.1*self.calculate_mseloss(predictions_encoder,float_encoders)
      cross_entropy_loss = self.calculate_loss(predictions,labels)
      return cross_entropy_encoder+cross_entropy_loss, float_encoders
      #return cross_entropy_encoder, float_encoders