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  • cs224d 作业 problem set2 (二) TensorFlow 实现命名实体识别

    神经网络在命名实体识别中的应用

    所有的这些包括之前的两篇都可以通过tensorflow 模型的托管部署到 google cloud 上面,发布成restful接口,从而与任何的ERP,CRM系统集成、

    天呀,这就是赤果果的钱呀。好血腥。感觉tensorflow的革命性意义就是能够将学校学到的各种数学算法成功地与各种系统结合起来。

    实现了matlab一直不能与其他系统结合的功能,并且提供GPU并行计算的功能,简直屌爆了

    理论上来讲像啥 运输问题,规划问题,极值问题。都可以通过tensorflow来进行解决,最主要的是能成功地与其他系统进行结合

    练习反向传播算法和训练深度神经网络,通过它们来实现命名实体识别 (人名,地名,机构名,专有名词)

    模型是一个单隐藏层神经网络,有一个类似在word2vec中看到的表现层,这里不需要求平均值,或者抽样,

    而是明确地将上下文定义为一个“窗口”。包含目标词和它左右紧邻的词,是一个3d维度的行向量

    xt-1,xt,xt+1是one-hot行向量,

    是嵌入矩阵

    每一行Li其实就代表一个特定的词,然后做如下预测

    定义的交叉熵损失函数:

    下面初始化各变量的值(Random initialize)然后求损失函数对各个需要更新变量的导数(梯度)

    用来进行梯度下降寻找最优解

    (1)训练的过程就是先在上一篇文章中训练出词向量,

    (2)然后对于每一个词构造成三元组的形式

    (3)然后初始化下面公式中所有需要随机生成的变量

      

        

    (4)然后根据上面的每个变量的梯度公式,求出每个变量的梯度值

    (5)然后应用梯度下降方法,  新变量值=初始值+步长*梯度值  来更新每一个变量的值

    (6)将新的变量代入到上面的公式中,如果交叉熵损失小于固定值则停止学习,否则继续学习

    (7)对于每一个词的向量应用上面的迭代方法,直至训练完毕,便得到了命名实体识别的神经网络模型

    这里边这个网络的结构如下图所示:

    '''
    Created on 2017年9月22日
    
    @author: weizhen
    '''
    import os
    import getpass
    import sys
    import time
    import struct
    
    import numpy as np
    import tensorflow as tf
    from q2_initialization import xavier_weight_init
    import data_utils.utils as du
    import data_utils.ner as ner
    from utils import data_iterator
    from model import LanguageModel
    
    class Config(object):
        """
                        配置模型的超参数和数据信息
                 这个配置类是用来存储超参数和数据信息,模型对象被传进Config() 实例对象在初始化的时候
        """
        embed_size = 50
        batch_size = 64
        label_size = 5
        hidden_size = 100
        max_epochs = 50
        early_stopping = 2
        dropout = 0.9
        lr = 0.001
        l2 = 0.001
        window_size = 3
    
    class NERModel(LanguageModel):
        """
        Implements a NER (Named Entity Recognition) model.
              实现命名实体识别的模型
              这个类实现了一个深度的神经网络用来进行命名实体识别
            它继承自LanguageModel 一个有着add_embedding 方法,除了标准的模型方法
        """
        def load_data(self, debug=False):
            """
                        加载开始的word-vectors 并且开始训练 train/dev/test data
            """
            # Load the starter word vectors
            self.wv, word_to_num, num_to_word = ner.load_wv('data/ner/vocab.txt', 'data/ner/wordVectors.txt')
            tagnames = ['O', 'LOC', 'MISC', 'ORG', 'PER']
            self.num_to_tag = dict(enumerate(tagnames))
            tag_to_num = {v:k for k, v in self.num_to_tag.items()}
            
            # Load the training set
            docs = du.load_dataset("data/ner/train")
            self.X_train, self.y_train = du.docs_to_windows(docs, word_to_num, tag_to_num, wsize=self.config.window_size)
            if debug:
                self.X_train = self.X_train[:1024]
                self.y_train = self.y_train[:1024]
            
            # Load the dev set (for tuning hyperparameters)
            docs = du.load_dataset('data/ner/dev')
            self.X_dev, self.y_dev = du.docs_to_windows(docs, word_to_num, tag_to_num, wsize=self.config.window_size)
            if debug:
                self.X_dev = self.X_dev[:1024]
                self.y_dev = self.y_dev[:1024]
            
            # Load the test set (dummy labels only)
            docs = du.load_dataset("data/ner/test.masked")
            self.X_test, self.y_test = du.docs_to_windows(docs, word_to_num, tag_to_num, wsize=self.config.window_size)
            
        def add_placeholders(self):
            """
                        生成placeholder 变量去接收输入的 tensors
                        这些placeholder 被用作输入在模型的其他地方调用,并且会在训练的时候被填充数据
                        当"None"在placeholder的大小当中的时候 ,是非常灵活的
                        在计算图中填充如下节点:
                        input_placeholder: tensor(None,window_size) . type:tf.int32
                        labels_placeholder: tensor(None,label_size) . type:tf.float32
                        dropout_placeholder: Dropout value placeholder (scalar), type: tf.float32
                        把这些placeholders 添加到 类对象自己作为    常量
            """
            self.input_placeholder = tf.placeholder(tf.int32, shape=[None, self.config.window_size], name='Input')
            self.labels_placeholder = tf.placeholder(tf.float32, shape=[None, self.config.label_size], name='Target')
            self.dropout_placeholder = tf.placeholder(tf.float32, name='Dropout')
        
        def create_feed_dict(self, input_batch, dropout, label_batch=None):
            """
                        为softmax分类器创建一个feed字典
                        feed_dict={
                            <placeholder>:<tensor of values to be passed for placeholder>,
                        }
                        
                        Hint:The keys for the feed_dict should be a subset of the placeholder
                             tensors created in add_placeholders.
                        Hint:When label_batch is None,don't add a labels entry to the feed_dict
                        
                        Args:
                            input_batch:A batch of input data
                            label_batch:A batch of label data
                        Returns:
                            feed_dict: The feed dictionary mapping from placeholders to values.
            """
            feed_dict = {
                    self.input_placeholder:input_batch,
                }
            if label_batch is not None:
                feed_dict[self.labels_placeholder] = label_batch
            if dropout is not None:
                feed_dict[self.dropout_placeholder] = dropout
            return feed_dict
        
        def add_embedding(self):
            # The embedding lookup is currently only implemented for the CPU
            with tf.device('/cpu:0'):
                embedding = tf.get_variable('Embedding', [len(self.wv), self.config.embed_size])
                window = tf.nn.embedding_lookup(embedding, self.input_placeholder)
                window = tf.reshape(
                    window, [-1, self.config.window_size * self.config.embed_size])
                # ## END YOUR CODE
                return window
        
        def add_model(self, window):
            """Adds the 1-hidden-layer NN
            Hint:使用一个variable_scope ("layer") 对于第一个隐藏层
                                    另一个("Softmax")用于线性变换在最后一个softmax层之前
                                    确保使用xavier_weight_init 方法,你之前定义好的
            Hint:确保添加了正则化和dropout在这个网络中
                                    正则化应该被添加到损失函数上,
                 dropout应该被添加到每一个变量的梯度上面
            Hint:可以考虑使用tensorflow Graph 集合 例如(total_loss)来收集正则化
                                     和损失项,你之后会在损失函数中添加的
            Hint:这里会需要创建不同维度的变量,如下所示:
                W:(window_size*embed_size,hidden_size)
                b1:(hidden_size,)
                U:(hidden_size,label_size)
                b2:(label_size)
            Args:
                window: tf.Tensor of shape(-1,window_size*embed_size)
            Returns:
                output: tf.Tensor of shape(batch_size,label_size)
            """
            with tf.variable_scope('layer1', initializer=xavier_weight_init()) as scope:
                W = tf.get_variable('w', [self.config.window_size * self.config.embed_size, self.config.hidden_size])
                b1 = tf.get_variable('b1', [self.config.hidden_size])
                h = tf.nn.tanh(tf.matmul(window, W) + b1)
                if self.config.l2:
                    tf.add_to_collection('total_loss', 0.5 * self.config.l2 * tf.nn.l2_loss(W))
            
            with tf.variable_scope('layer2', initializer=xavier_weight_init()) as scope:
                U = tf.get_variable('U', [self.config.hidden_size, self.config.label_size])
                b2 = tf.get_variable('b2', [self.config.label_size])
                y = tf.matmul(h, U) + b2
                if self.config.l2:
                    tf.add_to_collection('total_loss', 0.5 * self.config.l2 * tf.nn.l2_loss(U))
            output = tf.nn.dropout(y, self.dropout_placeholder)
            return output
        
        def add_loss_op(self, y):
            """将交叉熵损失添加到计算图上
            Hint:你或许可以使用tf.nn.softmax_cross_entropy_with_logits 方法来简化你的
                                    实现,
                                    或许可以使用tf.reduce_mean
                                    参数:
                   pred:A tensor shape:(batch_size,n_classes)
                                    返回值:
                    loss:A 0-d tensor (数值类型)
            """
            cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=self.labels_placeholder))
            tf.add_to_collection('total_loss', cross_entropy)
            loss = tf.add_n(tf.get_collection('total_loss'))
            return loss
    
        def add_training_op(self, loss):
            """设置训练目标
                                创建一个优化器并且将梯度下降应用到所有变量的更新上面
               Hint:对于这个模型使用tf.train.AdamOptimizer优化方法
                                           调用optimizer.minimize()会返回一个train_op的对象
                Args:
                    loss:Loss tensor,from cross entropy_loss
                Returns:
                    train_op:The Op for training
            """
            optimizer = tf.train.AdamOptimizer(self.config.lr)
            global_step = tf.Variable(0, name='global_step', trainable=False)
            train_op = optimizer.minimize(loss, global_step=global_step)
            return train_op
        
        def __init__(self, config):
            """使用上面定义好的函数来构造神经网络"""
            self.config = config
            self.load_data(debug=False)
            self.add_placeholders()
            window = self.add_embedding()
            y = self.add_model(window)
            
            self.loss = self.add_loss_op(y)
            self.predictions = tf.nn.softmax(y)
            one_hot_prediction = tf.arg_max(self.predictions, 1)
            correct_prediction = tf.equal(tf.arg_max(self.labels_placeholder, 1), one_hot_prediction)
            self.correct_predictions = tf.reduce_sum(tf.cast(correct_prediction, 'int32'))
            self.train_op = self.add_training_op(self.loss)
        
        def run_epoch(self, session, input_data, input_labels, shuffle=True, verbose=True):
            orig_X, orig_y = input_data, input_labels
            dp = self.config.dropout
            # We 're interested in keeping track of the loss and accuracy during training 
            total_loss = []
            total_correct_examples = 0
            total_processed_examples = 0
            total_steps = len(orig_X) / self.config.batch_size
            for step, (x, y) in enumerate(data_iterator(orig_X, orig_y, batch_size=self.config.batch_size, label_size=self.config.label_size, shuffle=shuffle)):
                feed = self.create_feed_dict(input_batch=x, dropout=dp, label_batch=y)
                loss, total_correct, _ = session.run(
                    [self.loss, self.correct_predictions, self.train_op],
                    feed_dict=feed)
                total_processed_examples += len(x)
                total_correct_examples += total_correct
                total_loss.append(loss)
                
                if verbose and step % verbose == 0:
                    sys.stdout.write('
    {}/{} : loss = {}'.format(step, total_steps, np.mean(total_loss)))
                if verbose:
                    sys.stdout.write('
    ')
                    sys.stdout.flush()
                return np.mean(total_loss), total_correct_examples / float(total_processed_examples)
        
        def predict(self, session, X, y=None):
            """从提供的模型中进行预测"""
            # 如果y已经给定,loss也已经计算出来了
            # 我们对dropout求导数通过把他设置为1
            dp = 1
            losses = []
            results = []
            if np.any(y):
                data = data_iterator(X, y, batch_size=self.config.batch_size,
                                    label_size=self.config.label_size, shuffle=False)
            else:
                data = data_iterator(X, batch_size=self.config.batch_size,
                                     label_size=self.config.label_size, shuffle=False)
            for step, (x, y) in enumerate(data):
                feed = self.create_feed_dict(input_batch=x, dropout=dp)
                if np.any(y):
                    feed[self.labels_placeholder] = y
                    loss, preds = session.run([self.loss, self.predictions], feed_dict=feed)
                    losses.append(loss)
                else:
                    preds = session.run(self.predictions, feed_dict=feed)
                predicted_indices = preds.argmax(axis=1)
                results.extend(predicted_indices)
            return np.mean(losses), results
                
    def print_confusion(confusion, num_to_tag):
        """Helper method that prints confusion matrix"""
        # Summing top to bottom gets the total number of tags guessed as T
        total_guessed_tags = confusion.sum(axis=0)
        # Summing left to right gets the total number of true tags
        total_true_tags = confusion.sum(axis=1)
        print("")
        print(confusion)
        for i, tag in sorted(num_to_tag.items()):
            print(i, "-----", tag)
            prec = confusion[i, i] / float(total_guessed_tags[i])
            recall = confusion[i, i] / float(total_true_tags[i])
            print("Tag: {} - P {:2.4f} / R {:2.4f}".format(tag, prec, recall))
        
    def calculate_confusion(config, predicted_indices, y_indices):
        """帮助方法计算混淆矩阵"""
        confusion = np.zeros((config.label_size, config.label_size), dtype=np.int32)
        for i in range(len(y_indices)):
            correct_label = y_indices[i]
            guessed_label = predicted_indices[i]
            confusion[correct_label, guessed_label] += 1
        return confusion
    
    def save_predictions(predictions, filename):
        """保存predictions 到 提供的文件中"""
        with open(filename, "w") as f:
            for prediction in predictions:
                f.write(str(prediction) + "
    ")
    
    def test_NER():
        """测试NER模型的实现
                你可以使用这个函数来测试你实现了的命名实体识别的神经网络
                当调试的时候,设置最大的max_epochs 在 Config 对象里边为1
                这样便可以快速地进行迭代
        """
        config = Config()
        with tf.Graph().as_default():
            model = NERModel(config)
            
            init = tf.initialize_all_variables()
            saver = tf.train.Saver()
            
            with tf.Session() as session:
                best_val_loss = float('inf')
                best_val_epoch = 0
                
                session.run(init)
                for epoch in range(config.max_epochs):
                    print('Epoch {}'.format(epoch))
                    start = time.time()
                    # ##
                    train_loss, train_acc = model.run_epoch(session, model.X_train, model.y_train)
                    val_loss, predictions = model.predict(session, model.X_dev, model.y_dev)
                    print('Training loss : {}'.format(train_loss))
                    print('Training acc : {}'.format(train_acc))
                    print('Validation loss : {}'.format(val_loss))
                    if val_loss < best_val_loss:
                        best_val_loss = val_loss
                        best_val_epoch = epoch
                        if not os.path.exists("./weights"):
                            os.makedirs("./weights")
                        saver.save(session, './weights/ner.weights')
                    if epoch - best_val_epoch > config.early_stopping:
                        break
                    confusion = calculate_confusion(config, predictions, model.y_dev)
                    print_confusion(confusion, model.num_to_tag)
                    print('Total time: {}'.format(time.time() - start))
    
                #saver.restore(session, './weights/ner.weights')
                #print('Test')
                #print('=-=-=')
                #print('Writing predictions t o q2_test.predicted')
                #_, predictions = model.predict(session, model.X_test, model.y_test)
                #save_predictions(predictions, "q2_test.predicted")
    
    if __name__ == "__main__":
        test_NER()    
        

    下面是训练完的log

    WARNING:tensorflow:From C:UsersweizhenDocumentsGitHubTflinearClassifierq2_NER.py:291: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
    Instructions for updating:
    Use `tf.global_variables_initializer` instead.
    2017-10-02 16:31:40.821644: W c:	f_jenkinshomeworkspace
    elease-windevicegpuoswindows	ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.
    2017-10-02 16:31:40.822256: W c:	f_jenkinshomeworkspace
    elease-windevicegpuoswindows	ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations.
    2017-10-02 16:31:40.822842: W c:	f_jenkinshomeworkspace
    elease-windevicegpuoswindows	ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
    2017-10-02 16:31:40.823263: W c:	f_jenkinshomeworkspace
    elease-windevicegpuoswindows	ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
    2017-10-02 16:31:40.823697: W c:	f_jenkinshomeworkspace
    elease-windevicegpuoswindows	ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
    2017-10-02 16:31:40.824035: W c:	f_jenkinshomeworkspace
    elease-windevicegpuoswindows	ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
    2017-10-02 16:31:40.824464: W c:	f_jenkinshomeworkspace
    elease-windevicegpuoswindows	ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
    2017-10-02 16:31:40.824850: W c:	f_jenkinshomeworkspace
    elease-windevicegpuoswindows	ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
    2017-10-02 16:31:42.184267: I c:	f_jenkinshomeworkspace
    elease-windevicegpuoswindows	ensorflowcorecommon_runtimegpugpu_device.cc:887] Found device 0 with properties: 
    name: GeForce 940MX
    major: 5 minor: 0 memoryClockRate (GHz) 1.189
    pciBusID 0000:01:00.0
    Total memory: 2.00GiB
    Free memory: 1.66GiB
    2017-10-02 16:31:42.184794: I c:	f_jenkinshomeworkspace
    elease-windevicegpuoswindows	ensorflowcorecommon_runtimegpugpu_device.cc:908] DMA: 0 
    2017-10-02 16:31:42.185018: I c:	f_jenkinshomeworkspace
    elease-windevicegpuoswindows	ensorflowcorecommon_runtimegpugpu_device.cc:918] 0:   Y 
    2017-10-02 16:31:42.185582: I c:	f_jenkinshomeworkspace
    elease-windevicegpuoswindows	ensorflowcorecommon_runtimegpugpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce 940MX, pci bus id: 0000:01:00.0)
    Epoch 0
    
    0/3181.578125 : loss = 1.6745071411132812
    Training loss : 1.6745071411132812
    Training acc : 0.046875
    Validation loss : 1.6497892141342163
    
    [[    0     0     0     0 42759]
     [    0     0     0     0  2094]
     [    0     0     0     0  1268]
     [    0     0     0     0  2092]
     [    0     0     0     0  3149]]
    0 ----- O
    C:UsersweizhenDocumentsGitHubTflinearClassifierq2_NER.py:262: RuntimeWarning: invalid value encountered in true_divide
      prec = confusion[i, i] / float(total_guessed_tags[i])
    Tag: O - P nan / R 0.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P 0.0613 / R 1.0000
    Total time: 3.293267250061035
    Epoch 1
    
    0/3181.578125 : loss = 1.6299598217010498
    Training loss : 1.6299598217010498
    Training acc : 0.0625
    Validation loss : 1.6258254051208496
    
    [[    0     0     0     0 42759]
     [    0     0     0     0  2094]
     [    0     0     0     0  1268]
     [    0     0     0     0  2092]
     [    0     0     0     0  3149]]
    0 ----- O
    Tag: O - P nan / R 0.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P 0.0613 / R 1.0000
    Total time: 3.019239664077759
    Epoch 2
    
    0/3181.578125 : loss = 1.6292331218719482
    Training loss : 1.6292331218719482
    Training acc : 0.078125
    Validation loss : 1.6021082401275635
    
    [[    0     0     0     0 42759]
     [    0     0     0     0  2094]
     [    0     0     0     0  1268]
     [    0     0     0     0  2092]
     [    0     0     0     0  3149]]
    0 ----- O
    Tag: O - P nan / R 0.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P 0.0613 / R 1.0000
    Total time: 2.9794013500213623
    Epoch 3
    
    0/3181.578125 : loss = 1.6349217891693115
    Training loss : 1.6349217891693115
    Training acc : 0.015625
    Validation loss : 1.5785211324691772
    
    [[    0     0     0     0 42759]
     [    0     0     0     0  2094]
     [    0     0     0     0  1268]
     [    0     0     0     0  2092]
     [    0     0     0     0  3149]]
    0 ----- O
    Tag: O - P nan / R 0.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P 0.0613 / R 1.0000
    Total time: 2.4377009868621826
    Epoch 4
    
    0/3181.578125 : loss = 1.5779037475585938
    Training loss : 1.5779037475585938
    Training acc : 0.09375
    Validation loss : 1.5549894571304321
    
    [[    0     0     0     0 42759]
     [    0     0     0     0  2094]
     [    0     0     0     0  1268]
     [    0     0     0     0  2092]
     [    0     0     0     0  3149]]
    0 ----- O
    Tag: O - P nan / R 0.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P 0.0613 / R 1.0000
    Total time: 2.4941294193267822
    Epoch 5
    
    0/3181.578125 : loss = 1.5726330280303955
    Training loss : 1.5726330280303955
    Training acc : 0.078125
    Validation loss : 1.5313135385513306
    
    [[    0     0     0     0 42759]
     [    0     0     0     0  2094]
     [    0     0     1     0  1267]
     [    0     0     0     0  2092]
     [    0     0     0     0  3149]]
    0 ----- O
    Tag: O - P nan / R 0.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P 1.0000 / R 0.0008
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P 0.0613 / R 1.0000
    Total time: 2.4369616508483887
    Epoch 6
    
    0/3181.578125 : loss = 1.530135989189148
    Training loss : 1.530135989189148
    Training acc : 0.046875
    Validation loss : 1.5071308612823486
    
    [[    0     0     0     0 42759]
     [    0     0     0     0  2094]
     [    0     0     6     0  1262]
     [    0     0     0     0  2092]
     [    0     0     0     0  3149]]
    0 ----- O
    Tag: O - P nan / R 0.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P 1.0000 / R 0.0047
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P 0.0613 / R 1.0000
    Total time: 2.4289004802703857
    Epoch 7
    
    0/3181.578125 : loss = 1.4907350540161133
    Training loss : 1.4907350540161133
    Training acc : 0.0625
    Validation loss : 1.482757806777954
    
    [[  789     0     0     0 41970]
     [   19     0     0     0  2075]
     [    1     0     7     0  1260]
     [   45     0     1     0  2046]
     [   48     0     0     0  3101]]
    0 ----- O
    Tag: O - P 0.8747 / R 0.0185
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P 0.8750 / R 0.0055
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P 0.0615 / R 0.9848
    Total time: 3.0616846084594727
    Epoch 8
    
    0/3181.578125 : loss = 1.474185824394226
    Training loss : 1.474185824394226
    Training acc : 0.046875
    Validation loss : 1.4580132961273193
    
    [[ 7684     0     0     0 35075]
     [  364     0     0     0  1730]
     [   51     0    11     0  1206]
     [  445     0     1     0  1646]
     [  500     0     0     0  2649]]
    0 ----- O
    Tag: O - P 0.8496 / R 0.1797
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P 0.9167 / R 0.0087
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P 0.0626 / R 0.8412
    Total time: 2.969224214553833
    Epoch 9
    
    0/3181.578125 : loss = 1.498674988746643
    Training loss : 1.498674988746643
    Training acc : 0.28125
    Validation loss : 1.4329923391342163
    
    [[20553     0     1     0 22205]
     [ 1273     0     0     0   821]
     [  364     0     7     0   897]
     [  980     0     2     0  1110]
     [ 1426     0     0     0  1723]]
    0 ----- O
    Tag: O - P 0.8356 / R 0.4807
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P 0.7000 / R 0.0055
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P 0.0644 / R 0.5472
    Total time: 2.5193586349487305
    Epoch 10
    
    0/3181.578125 : loss = 1.4385690689086914
    Training loss : 1.4385690689086914
    Training acc : 0.421875
    Validation loss : 1.4074962139129639
    
    [[34564     0     4     0  8191]
     [ 1764     0     0     0   330]
     [  767     0     7     0   494]
     [ 1594     0     2     0   496]
     [ 2355     0     0     0   794]]
    0 ----- O
    Tag: O - P 0.8421 / R 0.8083
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P 0.5385 / R 0.0055
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P 0.0770 / R 0.2521
    Total time: 2.3797054290771484
    Epoch 11
    
    0/3181.578125 : loss = 1.4594019651412964
    Training loss : 1.4594019651412964
    Training acc : 0.546875
    Validation loss : 1.3817591667175293
    
    [[40966     0     2     0  1791]
     [ 1976     0     0     0   118]
     [ 1088     0     4     0   176]
     [ 1900     0     0     0   192]
     [ 2814     0     0     0   335]]
    0 ----- O
    Tag: O - P 0.8404 / R 0.9581
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P 0.6667 / R 0.0032
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P 0.1283 / R 0.1064
    Total time: 2.4073784351348877
    Epoch 12
    
    0/3181.578125 : loss = 1.3720815181732178
    Training loss : 1.3720815181732178
    Training acc : 0.78125
    Validation loss : 1.3555692434310913
    
    [[42709     0     0     0    50]
     [ 2085     0     0     0     9]
     [ 1266     0     0     0     2]
     [ 2086     0     0     0     6]
     [ 3115     0     0     0    34]]
    0 ----- O
    Tag: O - P 0.8332 / R 0.9988
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P 0.3366 / R 0.0108
    Total time: 2.4379138946533203
    Epoch 13
    
    0/3181.578125 : loss = 1.3634321689605713
    Training loss : 1.3634321689605713
    Training acc : 0.828125
    Validation loss : 1.328884482383728
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.8378336429595947
    Epoch 14
    
    0/3181.578125 : loss = 1.3688112497329712
    Training loss : 1.3688112497329712
    Training acc : 0.75
    Validation loss : 1.302013635635376
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.7120652198791504
    Epoch 15
    
    0/3181.578125 : loss = 1.3235018253326416
    Training loss : 1.3235018253326416
    Training acc : 0.78125
    Validation loss : 1.2748615741729736
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.6104214191436768
    Epoch 16
    
    0/3181.578125 : loss = 1.3185033798217773
    Training loss : 1.3185033798217773
    Training acc : 0.765625
    Validation loss : 1.2475427389144897
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.3700265884399414
    Epoch 17
    
    0/3181.578125 : loss = 1.3193732500076294
    Training loss : 1.3193732500076294
    Training acc : 0.703125
    Validation loss : 1.2201541662216187
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.439958095550537
    Epoch 18
    
    0/3181.578125 : loss = 1.2185351848602295
    Training loss : 1.2185351848602295
    Training acc : 0.75
    Validation loss : 1.1924999952316284
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.8921308517456055
    Epoch 19
    
    0/3181.578125 : loss = 1.2128124237060547
    Training loss : 1.2128124237060547
    Training acc : 0.75
    Validation loss : 1.164793610572815
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.3643531799316406
    Epoch 20
    
    0/3181.578125 : loss = 1.174509882926941
    Training loss : 1.174509882926941
    Training acc : 0.71875
    Validation loss : 1.137137770652771
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.7417917251586914
    Epoch 21
    
    0/3181.578125 : loss = 1.056962490081787
    Training loss : 1.056962490081787
    Training acc : 0.84375
    Validation loss : 1.1092265844345093
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.5229830741882324
    Epoch 22
    
    0/3181.578125 : loss = 1.1316486597061157
    Training loss : 1.1316486597061157
    Training acc : 0.796875
    Validation loss : 1.0816625356674194
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.7065508365631104
    Epoch 23
    
    0/3181.578125 : loss = 1.073209524154663
    Training loss : 1.073209524154663
    Training acc : 0.78125
    Validation loss : 1.0542036294937134
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.6912477016448975
    Epoch 24
    
    0/3181.578125 : loss = 1.0102397203445435
    Training loss : 1.0102397203445435
    Training acc : 0.859375
    Validation loss : 1.0271364450454712
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.5654654502868652
    Epoch 25
    
    0/3181.578125 : loss = 1.0918526649475098
    Training loss : 1.0918526649475098
    Training acc : 0.734375
    Validation loss : 1.001003623008728
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.663228750228882
    Epoch 26
    
    0/3181.578125 : loss = 1.0216875076293945
    Training loss : 1.0216875076293945
    Training acc : 0.84375
    Validation loss : 0.9754133820533752
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.5872113704681396
    Epoch 27
    
    0/3181.578125 : loss = 1.0990902185440063
    Training loss : 1.0990902185440063
    Training acc : 0.78125
    Validation loss : 0.9509017467498779
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.443047285079956
    Epoch 28
    
    0/3181.578125 : loss = 0.9783419966697693
    Training loss : 0.9783419966697693
    Training acc : 0.8125
    Validation loss : 0.9272997379302979
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.6797432899475098
    Epoch 29
    
    0/3181.578125 : loss = 1.0568724870681763
    Training loss : 1.0568724870681763
    Training acc : 0.765625
    Validation loss : 0.904884934425354
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.3945367336273193
    Epoch 30
    
    0/3181.578125 : loss = 1.0237849950790405
    Training loss : 1.0237849950790405
    Training acc : 0.78125
    Validation loss : 0.883781909942627
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.7488887310028076
    Epoch 31
    
    0/3181.578125 : loss = 1.0338774919509888
    Training loss : 1.0338774919509888
    Training acc : 0.765625
    Validation loss : 0.8637697696685791
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.381608247756958
    Epoch 32
    
    0/3181.578125 : loss = 0.9260292649269104
    Training loss : 0.9260292649269104
    Training acc : 0.78125
    Validation loss : 0.8448039889335632
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.5534446239471436
    Epoch 33
    
    0/3181.578125 : loss = 0.8264249563217163
    Training loss : 0.8264249563217163
    Training acc : 0.875
    Validation loss : 0.8267776370048523
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.558243751525879
    Epoch 34
    
    0/3181.578125 : loss = 0.9866911768913269
    Training loss : 0.9866911768913269
    Training acc : 0.8125
    Validation loss : 0.8101168274879456
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.464554786682129
    Epoch 35
    
    0/3181.578125 : loss = 0.8703485727310181
    Training loss : 0.8703485727310181
    Training acc : 0.8125
    Validation loss : 0.7947656512260437
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.3714654445648193
    Epoch 36
    
    0/3181.578125 : loss = 0.8071379661560059
    Training loss : 0.8071379661560059
    Training acc : 0.84375
    Validation loss : 0.7804707288742065
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.8228049278259277
    Epoch 37
    
    0/3181.578125 : loss = 0.6435794234275818
    Training loss : 0.6435794234275818
    Training acc : 0.875
    Validation loss : 0.7670522928237915
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.6811318397521973
    Epoch 38
    
    0/3181.578125 : loss = 0.6902540326118469
    Training loss : 0.6902540326118469
    Training acc : 0.890625
    Validation loss : 0.7546741962432861
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.596187114715576
    Epoch 39
    
    0/3181.578125 : loss = 0.6969885230064392
    Training loss : 0.6969885230064392
    Training acc : 0.859375
    Validation loss : 0.7434151768684387
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.6147048473358154
    Epoch 40
    
    0/3181.578125 : loss = 0.87004554271698
    Training loss : 0.87004554271698
    Training acc : 0.78125
    Validation loss : 0.7334315776824951
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.3841097354888916
    Epoch 41
    
    0/3181.578125 : loss = 0.95426344871521
    Training loss : 0.95426344871521
    Training acc : 0.78125
    Validation loss : 0.7244532704353333
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.304476022720337
    Epoch 42
    
    0/3181.578125 : loss = 0.8543925285339355
    Training loss : 0.8543925285339355
    Training acc : 0.78125
    Validation loss : 0.7163312435150146
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.6401660442352295
    Epoch 43
    
    0/3181.578125 : loss = 0.6948934197425842
    Training loss : 0.6948934197425842
    Training acc : 0.859375
    Validation loss : 0.7088930606842041
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.954803228378296
    Epoch 44
    
    0/3181.578125 : loss = 0.8735166192054749
    Training loss : 0.8735166192054749
    Training acc : 0.796875
    Validation loss : 0.7022351622581482
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.6469264030456543
    Epoch 45
    
    0/3181.578125 : loss = 0.8812070488929749
    Training loss : 0.8812070488929749
    Training acc : 0.828125
    Validation loss : 0.695988118648529
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.8571887016296387
    Epoch 46
    
    0/3181.578125 : loss = 0.5007133483886719
    Training loss : 0.5007133483886719
    Training acc : 0.90625
    Validation loss : 0.690228283405304
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.360257625579834
    Epoch 47
    
    0/3181.578125 : loss = 0.8069882988929749
    Training loss : 0.8069882988929749
    Training acc : 0.8125
    Validation loss : 0.6848161220550537
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.2997841835021973
    Epoch 48
    
    0/3181.578125 : loss = 0.6994635462760925
    Training loss : 0.6994635462760925
    Training acc : 0.8125
    Validation loss : 0.6798946857452393
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.5274598598480225
    Epoch 49
    
    0/3181.578125 : loss = 0.7816348075866699
    Training loss : 0.7816348075866699
    Training acc : 0.8125
    Validation loss : 0.6751761436462402
    
    [[42759     0     0     0     0]
     [ 2094     0     0     0     0]
     [ 1268     0     0     0     0]
     [ 2092     0     0     0     0]
     [ 3149     0     0     0     0]]
    0 ----- O
    Tag: O - P 0.8325 / R 1.0000
    1 ----- LOC
    Tag: LOC - P nan / R 0.0000
    2 ----- MISC
    Tag: MISC - P nan / R 0.0000
    3 ----- ORG
    Tag: ORG - P nan / R 0.0000
    4 ----- PER
    Tag: PER - P nan / R 0.0000
    Total time: 2.7655985355377197
    View Code

     更完整的代码请参考:

     https://github.com/weizhenzhao/cs224d_nlp_problem_set2
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  • 原文地址:https://www.cnblogs.com/weizhen/p/7580788.html
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