Question

我正在训练分类器以获得优化的因素。我的数据集一开始包含800个样本（有些相似，只是稍作修改）。

我在GoogleColab环境下使用TensorFlow开发了模型。

针对此问题，我使用了一个简单的 MLP ，每个阶段有 3个隐藏层，每个层都有256个节点。我也有 64个课程。

我有可变长度输入，并且已经通过“-1”填充解决了此问题。

根据我的实际特征，我知道我会得到较差的准确度，但是我没想到零准确度和很大的损失。

这是我的数据集，其中省略了一些我注意到会对精度产生负面影响的功能：

     0  1   2   4  5  6  8  9   11  13    15      17    19      21
805  6  10  11  1  3  1  6  64  2   1.0   64.0    64.0  64.0    -1.0
334  6  12  18  0  2  4  7  2   1   32.0  128.0   64.0  128.0   -1.0
781  7  10  11  1  3  1  6  2   2   2.0   64.0    32.0  32.0    64.0

[编辑]：这是我标签的几行：

这是我的一组结果：

epoch[0] step [0] train -- loss : 50751.734375, accuracy : 0.0
epoch[0] step [100] train -- loss : 27310.064453125, accuracy : 0.0
epoch[0] step [200] train -- loss : 58120.6015625, accuracy : 0.0
epoch[0] step [300] train -- loss : 31801.9453125, accuracy : 0.0
epoch[0] step [400] train -- loss : 54360.76171875, accuracy : 0.0
epoch[0] step [500] train -- loss : 59946.67578125, accuracy : 0.0
epoch[1] step [0] train -- loss : 40612.06640625, accuracy : 0.0
epoch[1] step [100] train -- loss : 43229.734375, accuracy : 0.0
epoch[1] step [200] train -- loss : 36951.84375, accuracy : 0.0
epoch[1] step [300] train -- loss : 45225.828125, accuracy : 0.0
epoch[1] step [400] train -- loss : 47055.1796875, accuracy : 0.0
epoch[1] step [500] train -- loss : 54023.23046875, accuracy : 0.0

我想知道为什么我会遭受这么大的损失，而且我的损失也也没有收敛：（。

那是我得到的准确性和损失的怪异图：

我的代码工作得很好，但是我仍然怀疑也许我写得不好，所以弄得一团糟。

这是我的代码的重要部分：

class MLP():
    '''
      This is the implementation of the Multi Layer Perceptron
    '''
    def __init__(self, x_train, y_train, n_classes, n_hiddens=3, activation=tf.nn.relu):
      ...       
    ################################ Create the model ##############################    
    def multilayer_perceptron(self,X):      
        # Hidden fully connected layer with n_hidden_1 neurons
        layer_1 = tf.layers.dense(inputs=X, units= self.n_hidden_1, use_bias=True, kernel_initializer=self._init, name= 'layer_1')
        layer_1 = tf.layers.batch_normalization(layer_1,training=self.is_train)
        layer_1 = self.activation(layer_1)     

        # Hidden fully connected layer with n_hidden_2 neurons
        layer_2 = tf.layers.dense(inputs=layer_1, units= self.n_hidden_2, use_bias=True, kernel_initializer=self._init, name= 'layer_2')
        layer_2 = tf.layers.batch_normalization(layer_2,training=self.is_train)
        layer_2 = self.activation(layer_2)   

        # Hidden fully connected layer with n_hidden_3 neurons
        layer_3 = tf.layers.dense(inputs=layer_2, units= self.n_hidden_3, use_bias=True, kernel_initializer=self._init, name= 'layer_3')
        layer_3 = tf.layers.batch_normalization(layer_3, training=self.is_train)
        layer_3 = self.activation(layer_3)  

        # Output fully connected layer with the output
        out_layer = tf.layers.dense(inputs=layer_3, units= self.n_classes, use_bias=True, kernel_initializer=self._init, name= 'out_layer')

        tf.summary.histogram('pre-activations', out_layer) 
        return layer_1, layer_2, layer_3, out_layer

在这里我计算训练集的损失和准确性：

 def loss(self, X, Y):
        _, _, _, self.predicted_out = self.multilayer_perceptron(X) 
        print("Predicted out", self.predicted_out)
        with tf.name_scope('loss'):          
                loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.predicted_out, labels=Y))
        tf.summary.scalar('loss', loss) 

        with tf.name_scope('accuracy'):
                predicted_class = tf.nn.softmax(self.predicted_out)
                with tf.name_scope('correct_prediction'):
                    correct_prediction = tf.equal(tf.argmax(predicted_class, 1), tf.argmax(Y, 1))
                with tf.name_scope('accuracy'):
                    accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
        tf.summary.scalar('accuracy', accuracy)
        self.merged =  tf.summary.merge_all()

        return loss, accuracy

此功能的培训：

def train(self):
        self.train_writer = tf.summary.FileWriter('./Graph', self.sess.graph) 
    # training data
        train_input = self.Normalize(self.x_train)
        train_output = self.y_train.copy()

        save_sess=self.sess

      #costs history :
        costs = []
        costs_inter=[] 
      #for early stopping :
        best_cost=1000000
        stop = False
        last_improvement=0

        n_samples = train_input.shape[0] # size of the training set

       #train the mini_batches model using the early stopping criteria
        epoch = 0
        while epoch < self.max_epochs and stop == False:
            #train the model on the traning set by mini batches
            #suffle then split the training set to mini-batches of size self.batch_size
            seq =list(range(n_samples))
            random.shuffle(seq)
            mini_batches = [
                seq[k:k+self.batch_size]
                for k in range(0,n_samples, self.batch_size)
            ]

            avg_cost = 0. # The average cost of mini_batches
            step= 0

            for sample in mini_batches:                  
                batch_x = x_train.iloc[sample, :]
                batch_y =train_output.iloc[sample, :]
                feed_dict={self.X: batch_x,self.Y:batch_y, self.is_train:True}
                self.train_summary, _, cost,acc=self.sess.run([self.merged, self.train_step, self.loss_, self.accuracy_], feed_dict=feed_dict)
                avg_cost += cost *len(sample)/n_samples      
                print('epoch[{}] step [{}] train -- loss : {}, accuracy : {}'.format(epoch,step, cost, acc))
                self.train_writer.add_summary(self.train_summary, global_step=step)
                step += 100

            #cost history since the last best cost
            costs_inter.append(avg_cost)
            epoch +=1

        #Test the model 
        pred = tf.nn.softmax(self.predicted_out)  # Apply softmax to logits
        correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(self.Y, 1))
        # Calculate accuracy
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
        accuracy_test = self.sess.run([accuracy],feed_dict={self.X: x_test, self.Y: y_test,self.is_train:False})
        #print("Accuracy:", accuracy.eval({self.X: x_test, self.Y: y_test}))
        print("Accuracy_test : ", accuracy_test)

        # Writes the summaries to disk
        self.train_writer.flush()
        # Flushes the summaries to disk and closes the SummaryWriter
        self.train_writer.close()                              
        return costs

在这里我称之为方法训练，我打算应用交叉验证以查看是否可以提高模型准确性，但我还没有：

def cross_validation(self,batch_size, n_hidden_1 , n_hidden_2, n_hidden_3, learning_rate):
        ##### Other parameter 
        self.batch_size = batch_size
        self.n_hidden_1 = n_hidden_1
        self.n_hidden_2 = n_hidden_2
        self.n_hidden_3 = n_hidden_3
        self.learning_rate = learning_rate
        self.require_improvement= 20
        self.max_epochs = 80
        self._init = tf.random_normal_initializer
        self.optimizer=tf.train.AdamOptimizer      
        loss = 0   
        tf.reset_default_graph()

        with tf.name_scope('input'):
          self.X=tf.placeholder("float",shape=[None,self.x_train.shape[1]])
          self.Y=tf.placeholder("float",shape=[None,self.y_train.shape[1]])
          self.is_train = tf.placeholder(tf.bool, name="is_train") 
        self.loss_, self.accuracy_ = self.loss(self.X, self.Y)
        self.train_step = self.optimizer(self.learning_rate).minimize(self.loss_)

        # Initiate a tensor session
        init = tf.global_variables_initializer()
        self.sess = tf.Session()
        self.sess.run(init)

        #train the model 
        loss = self.train()

        self.sess.close()
        del self.sess
        return  loss

最后这是我的主要爱好：

  if __name__=='__main__':
  tbc = tb.TensorBoardColab()
  mlp =   MLP(x_train, y_train, n_classes)
  loss= mlp.cross_validation(batch_size, n_hidden_1 , n_hidden_2, n_hidden_3, learning_rate)

我原本希望得到的精度会很差，但不会达到“ 0”，并且没有任何收敛的巨大损失震惊了我。有人可以告诉我这里的问题在哪里吗？

Answer 1

您需要注意很多要点

您应该在每次训练开始之前删除tf摘要文件，因为根据您的代码，全局步骤将从0重新开始
您的损失函数是function override_single_template( $template ) { global $post; if ( has_category( "cat_name", $post ) ) { // set $template to file location of custom `single` template // NOTE: file name for template does not have to follow WP post template // naming convention BUT is preferred } return $template; } add_filter( 'single_template', 'override_single_templates' );，要使用此函数，您可能需要在onehot中对标签进行编码，并使用此函数中的内部softmax函数尝试最小化接近那个onehot标签的logit层。如果您想保留当前的地面真理标签，请检查sparse_softmax_cross_entropy_with_logits。用法相似，但其中一些需要成为一个热门标签。查看详细说明here

我的分类器损失很大，准确度始终为0

1 个答案: