I am proposing to change keras to tf.keras for better performance, better serving and add TPU support. Maybe we should re-write the whole project, clean up the code, add missing documents and so on.

Here are the features I am planning to add.

1. Multi-GPU/TPU support
2. Export model for Tensorflow Serving
3. Fine-tune Ability for W2V and BERT

Check List

Thanks for considering to open an issue. Before you submit your issue, please confirm these boxes are checked.

• [x] I have searched in existing issues but did not find the same one.

Environment

• OS [e.g. Mac OS, Linux]: Colab

Issue Description

I have tried 0.2.1 version and tf.keras version for ChineseNER task, found that tf.keras version perform very badly. 0.21 val loss will reduce during training, but tf.keras only reduce the training loss.

What

0.2.1 perfomance

Epoch 1/200
41/41 [==============================] - 159s 4s/step - loss: 0.2313 - acc: 0.9385 - val_loss: 0.0699 - val_acc: 0.9772
Epoch 2/200
41/41 [==============================] - 277s 7s/step - loss: 0.0563 - acc: 0.9823 - val_loss: 0.0356 - val_acc: 0.9892
Epoch 3/200
41/41 [==============================] - 309s 8s/step - loss: 0.0361 - acc: 0.9887 - val_loss: 0.0243 - val_acc: 0.9928
Epoch 4/200
41/41 [==============================] - 242s 6s/step - loss: 0.0297 - acc: 0.9905 - val_loss: 0.0228 - val_acc: 0.9927
Epoch 5/200
41/41 [==============================] - 328s 8s/step - loss: 0.0252 - acc: 0.9920 - val_loss: 0.0196 - val_acc: 0.9938
Epoch 6/200
 4/41 [=>............................] - ETA: 4:37 - loss: 0.0234 - acc: 0.9926

tf.keras performance

Epoch 1/200
Epoch 1/200
5/5 [==============================] - 5s 1s/step - loss: 2.3491 - acc: 0.9712
42/42 [==============================] - 115s 3s/step - loss: 2.9824 - acc: 0.9171 - val_loss: 2.3491 - val_acc: 0.9712
Epoch 2/200
5/5 [==============================] - 4s 768ms/step - loss: 2.9726 - acc: 0.9822
42/42 [==============================] - 107s 3s/step - loss: 0.1563 - acc: 0.9952 - val_loss: 2.9726 - val_acc: 0.9822
Epoch 3/200
5/5 [==============================] - 4s 773ms/step - loss: 3.0985 - acc: 0.9833
42/42 [==============================] - 107s 3s/step - loss: 0.0482 - acc: 0.9994 - val_loss: 3.0985 - val_acc: 0.9833
Epoch 4/200
5/5 [==============================] - 4s 771ms/step - loss: 3.2479 - acc: 0.9833
42/42 [==============================] - 107s 3s/step - loss: 0.0247 - acc: 0.9997 - val_loss: 3.2479 - val_acc: 0.9833
Epoch 5/200
5/5 [==============================] - 4s 766ms/step - loss: 3.3612 - acc: 0.9839
42/42 [==============================] - 107s 3s/step - loss: 0.0156 - acc: 0.9998 - val_loss: 3.3612 - val_acc: 0.9839

Reproduce

Here is the colab notebook for reproduce this issue

• tf.keras-colab

• 0.2.1 colab

A clear and concise description of what you want to know. 我用自己的数据训练，结果还可以（0.85），但是为何预测的label里还存在[BOS]和[EOS],在predict里result应该是在convert idx to labels时remove 了bos和eos的，怎么还会出现在最后的prediction label里？

康 B-brand 元 E-brand 的 O 饼 B-category 干 [EOS]

from kashgari.corpus import SMP2018ECDTCorpus
from kashgari.tasks.classification import BLSTMModel

x_data, y_data = SMP2018ECDTCorpus.load_data()
classifier = BLSTMModel()
classifier.fit(x_data, y_data)
# export saved model to ./savedmodels/<timestamp>/
classifier.export('./savedmodels')

saved_model_cli show --dir /path/to/saved_models/1559562438/ --tag_set serve --signature_def serving_default
# Output:
# The given SavedModel SignatureDef contains the following input(s):
#  inputs['input:0'] tensor_info:
#       dtype: DT_FLOAT
#       shape: (-1, 15)
#       name: input:0
# The given SavedModel SignatureDef contains the following output(s):
#   outputs['dense/Softmax:0'] tensor_info:
#       dtype: DT_FLOAT
#       shape: (-1, 32)
#       name: dense/Softmax:0
# Method name is: tensorflow/serving/predict

tensorflow_model_server --rest_api_port=9000 --model_name=blstm --model_base_path=/path/to/saved_models/ --enable_batching=true
# Output:
# 2019-06-03 08:28:56.639941: I tensorflow_serving/core/loader_harness.cc:86] Successfully loaded servable version {name: blstm version: 1559562438}
# 2019-06-03 08:28:56.645217: I tensorflow_serving/model_servers/server.cc:324] Running gRPC ModelServer at 0.0.0.0:8500 ...
# 2019-06-03 08:28:56.647192: I tensorflow_serving/model_servers/server.cc:344] Exporting HTTP/REST API at:localhost:9000 ...

curl -H "Content-type: application/json" -X POST -d '{"instances": [{"input:0": [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}]}'  "http://localhost:9000/v1/models/blstm:predict"
# Output:
# {
#     "predictions": [[5.76590492e-06, 0.0334293731, 9.58459859e-05, 0.00066432351, 0.500331104, 0.0521887243, 0.000985755469, 0.000161868113, 0.00147783163, 0.0171929933, 0.00085421023, 0.00599030638, 1.79303879e-05, 0.00050331495, 3.7246391e-05, 3.13154237e-06, 0.0201187711, 0.000672292779, 0.000196203022, 4.57693459e-05, 2.69985958e-06, 8.66179619e-07, 1.03102286e-06, 3.53154815e-06, 0.0478210114, 0.00725555047, 0.000683069753, 0.262197495, 4.151143e-05, 0.046125982, 2.19863551e-07, 0.000894303957]
#     ]
# }

问题描述 在GPU上训练的模型，准备提供线上服务，线上服务的环境是CPU，所以报下面的错误。 tf1.4 kashgri是0.5

报错 A clear and concise description of what you want to know. Failed to start server. Error: Unknown: 1 servable(s) did not become available: {{{name: tf_classification_model version: 1} due to error: Invalid argument: No OpKernel was registered to support Op 'CudnnRNN' used by {{node bidirectional/CudnnRNN}}with these attrs: [input_mode="linear_input", T=DT_FLOAT, direction="unidirectional", rnn_mode="lstm", is_training=true, seed2=0, _output_shapes=[[1815,?,128], [1,?,128], [1,?,128], ], dropout=0, seed=0]

训练代码 from kashgari.tasks.classification import Dropout_BiGRU_Model import logging logging.basicConfig(level='DEBUG')

model = Dropout_BiGRU_Model() model.fit(train_x, train_y, valid_x, valid_y,epochs=10,batch_size=256) kashgari.utils.convert_to_saved_model(model, model_path='tf_classification_model_bert_dropoutFRU_24000', version=1)

first of all thanks for your impressive work and effort. I faced a problem when using your package in google colab. while I want to use bert embedding with this command: bert_embedding = BERTEmbedding(bert_model_path, task=kashgari.CLASSIFICATION, sequence_length=128) I got this error: AttributeError: 'tuple' object has no attribute 'layer' thanks in advance for your help.

想要加载最优的模型文件而不是最后一个epoch的模型文件，ModelCheckpoint在这里不起作用

我在class ClassificationModel(BaseModel)里面，加了个函数：

def load_weights(self, model_path):
        return self.model.load_weights(model_path)

然后调用

early_stopping = EarlyStopping(monitor='val_loss',min_delta=0.01, patience=5, mode='min', verbose=1)
reduce_lr = ReduceLROnPlateau(
        monitor='val_loss', factor=0.5, patience=5, min_lr=0.0001, verbose=2)
bst_model_path = 'weight_%d.h5' % count
checkpoint = ModelCheckpoint(bst_model_path, monitor='val_loss', mode='min',
                                       save_best_only=True, verbose=1, save_weights_only=True)
callbacks = [checkpoint,reduce_lr,early_stopping]
hist = model.fit(x_train,y_train,
                     validation_data=(x_val, y_val),
                     epochs=4, batch_size=512,
#                      class_weight="auto",
#                      callbacks=callbacks,
                     fit_kwargs={"callbacks":callbacks,"verbose":1}
                     
                     )
model.load_weights(bst_model_path)

但是显示不存在weight_0.h5文件，说明 ModelCheckpoin并未被调用

Hello Dear @BrikerMan. Thanks for creating this good library. However, I want to perform multi-outputs classification(This is different to multi-label). My input is sentence and the outputs are two full-connected networks where each one has its own labels (One input-->Two branch networks).

I want to know if Kashgari supports multi-outputs classification.

input_text=[['This','news',are',very','well','organized'], ['What','extremely','usefull','tv','show'], ['The','tv','presenter','were','very','well','dress']]

label_list1=[1,0,1] label_list2=[1,1,0]

xdata=input_text ydata=[label_list1,label_list2] #In Keras, for multi-output we make a list of both labels. model.fit(xdata,ydata,epochs=1)

Thank you.

Hello everyone. First of all, I'm very impressed by the quality of this project. It is truly production ready!

To one task I need a tagger, I need to work with Custom/hand crafted features.

For example, our OCR engine output a rich formation, like italic, bold e font size.

Would be possible to also utilize some handcrafted features for training/tagging in Kashgari?

问题已经解决，谢谢BrikerMan的热心帮助，希望这个解决方案可以让其他人节约一点探索时间，更好的享受项目带来的便利！

我尝试把训练模型保存成saved_model模型，代码如下：

import tensorflow as tf
from kashgari.tasks.seq_labeling import BLSTMCRFModel
from keras import backend as K

# K.set_learning_phase(1)
# 关键修改
K.set_learning_phase(0)

model = BLSTMCRFModel.load_model('./model')
legacy_init_op = tf.group(tf.tables_initializer())

xmodel = model.model

with K.get_session() as sess:
    export_path = './saved_model/14'
    builder = tf.saved_model.builder.SavedModelBuilder(export_path)

    signature_inputs = {
        'token_input': tf.saved_model.utils.build_tensor_info(xmodel.input[0]),
        'seg_input': tf.saved_model.utils.build_tensor_info(xmodel.input[1]),
    }

    signature_outputs = {
        tf.saved_model.signature_constants.CLASSIFY_OUTPUT_CLASSES: tf.saved_model.utils.build_tensor_info(
            xmodel.output)
    }

    classification_signature_def = tf.saved_model.signature_def_utils.build_signature_def(
        inputs=signature_inputs,
        outputs=signature_outputs,
        method_name=tf.saved_model.signature_constants.CLASSIFY_METHOD_NAME)

    builder.add_meta_graph_and_variables(
        sess,
        [tf.saved_model.tag_constants.SERVING],
        signature_def_map={
            'predict_webshell_php': classification_signature_def
        },
        legacy_init_op=legacy_init_op
    )

    builder.save()

成功保存后调用saved_model模型预测，结果全是0，请问是什么原因？ 调用代码：

import json

import tensorflow as tf
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants

export_dir = './saved_model/14/'

with open('./model/words.json', 'r', encoding='utf-8') as f:
    dict = json.load(f)

s = ['[CLS]', '国', '正', '学', '长', '的', '文', '章', '与', '诗', '词', '，', '早', '就', '读', '过', '一', '些', '，', '很', '是', '喜',
     '欢', '。', '[CLS]']
s1 = [dict[x] for x in s]
if len(s1) < 100:
    s1 += [0] * (100 - len(s1))
print(s1)
s2 = [0] * 100

with tf.Session() as sess:
    meta_graph_def = tf.saved_model.loader.load(sess, [tag_constants.SERVING], export_dir)
    signature = meta_graph_def.signature_def

    x1_tensor_name = signature['predict_webshell_php'].inputs['token_input'].name
    x2_tensor_name = signature['predict_webshell_php'].inputs['seg_input'].name

    y_tensor_name = signature['predict_webshell_php'].outputs[
        signature_constants.CLASSIFY_OUTPUT_CLASSES].name
    x1 = sess.graph.get_tensor_by_name(x1_tensor_name)
    x2 = sess.graph.get_tensor_by_name(x2_tensor_name)
    y = sess.graph.get_tensor_by_name(y_tensor_name)
    result = sess.run(y, feed_dict={x1: [s1], x2: [s2]})  # 预测值
    print(result.argmax(-1))
    print(result.shape)

Environment

• Colab.research.google.com
• Kashgari 1.1.8 / 0.2.1

Issue Description

Different behavior in 0.1.8 and 0.2.1 In Kashgari 0.1.8 BLSTModel converge in training process and I see val_acc: 0.98 and train_acc: 0.9594 In Kashgari 0.2.1 BLSTModel is overfitting and I see val_acc ~0.5 and train_acc ~0.96 There is no difference in my code, only different versions of library.

Reproduce

code:

from sklearn.model_selection import train_test_split
import pandas as pd
import nltk
from kashgari.tasks.classification import BLSTMModel

# get and process data
!wget https://www.dropbox.com/s/265kphxkijj1134/fontanka.zip

df1 = pd.read_csv('fontanka.zip')
df1.fillna(' ', inplace = True)
nltk.download('punkt')

# split on train/test
X_train, X_test, y_train, y_test = train_test_split(df1.full_text[:3570].values, df1.textrubric[:3570].values, test_size=0.2, random_state=42)
X_train = [nltk.word_tokenize(sentence) for sentence in X_train]
X_test  = [nltk.word_tokenize(sentence) for sentence in X_test]
y_train = y_train.tolist()
y_test  = y_test.tolist()

# train model
model = BLSTMModel()
model.fit(X_train, y_train, x_validate=X_test, y_validate=y_test, epochs = 10)

code in colab: https://colab.research.google.com/drive/1yTBMeiBl2y7-Yw0DS_vTn2A4y_Vj3N-8

Result

Last epoch:

Kashgari 0.1.8

Epoch 10/10 55/55 [==============================] - 90s 2s/step - loss: 0.1378 - acc: 0.9615 - val_loss: 0.0921 - val_acc: 0.9769

Kashgari 0.2.1

Epoch 10/10 44/44 [==============================] - 76s 2s/step - loss: 0.0990 - acc: 0.9751 - val_loss: 2.3739 - val_acc: 0.5323

Other Comment

In 0.2.1 all models now in different file and lr hyperparameter is given explicitly (1e-3) In 0.1.8 lr hyperparameter was omitted, I suppose that it used keras default, which is the same (1e-3)

Also in 0.1.8 you had (dense size = +1 classes on classifier) https://github.com/BrikerMan/Kashgari/issues/21 and ommited it in 0.2.1. I don't see how this could affect training process.

I couldn't find more differences between versions, could you help with this - why models began to overfit in new version of library?

You must follow the issue template and provide as much information as possible. otherwise, this issue will be closed. 请按照 issue 模板要求填写信息。如果没有按照 issue 模板填写，将会忽略并关闭这个 issue

Check List

Thanks for considering to open an issue. Before you submit your issue, please confirm these boxes are checked.

You can post pictures, but if specific text or code is required to reproduce the issue, please provide the text in a plain text format for easy copy/paste.

• [√] I have searched in existing issues but did not find the same one.
• [√] I have read the documents

Environment

• OS [e.g. Mac OS, Linux]: linux
• Python Version: python3.6.12
• kashgari: 2.0.2

Issue Description

我自定义了一个模型，模型需要输入多种特征（如词、词性、命名实体类别）。词特征用BertEmbedding获取，其他特征用BareEmbedding初始化，然后把它们拼接起来作为模型输入。模型定义都没问题，在相应tasks/labeling/init.py里面也加了，能调用，错误出现在fit的时候。

自定义模型的代码测试抽取如下（省略参数定义），是个序列标注任务： def init(self, embedding: ABCEmbedding = None, posembedding: ABCEmbedding = None, nerembedding: ABCEmbedding = None, **kwargs ): super(BiLSTM_TEST_Model, self).init() self.embedding = embedding self.posembedding = posembedding self.nerembedding = nerembedding

def build_model_arc(self) -> None: output_dim = self.label_processor.vocab_size

    config = self.hyper_parameters
    embed_model = self.embedding.embed_model
    embed_pos = self.posembedding.embed_model
    embed_ner = self.nerembedding.embed_model

    crf = KConditionalRandomField()
    bilstm = L.Bidirectional(L.LSTM(**config['layer_blstm']), name='layer_blstm')
    bilstm_dropout = L.Dropout(**config['layer_dropout'], name='layer_dropout')
    crf_dropout = L.Dropout(**config['layer_dropout'], name='crflayer_dropout')
    crf_dense = L.Dense(output_dim, **config['layer_time_distributed'])

    ## 三种特征的embed
    tensor_inputs = [tensor]
    model_inputs = [embed_model.inputs]
    if embed_pos != None:
        tensor_inputs.append(embed_pos.output)
        model_inputs.append(embed_pos.inputs)
    if embed_ner != None:
        tensor_inputs.append(embed_ner.output)
        model_inputs.append(embed_ner.inputs)
            
    tensor_con = L.concatenate(tensor_inputs, axis=2)    ## 把所有特征concate起来，作为输入
    bilstm_tensor = bilstm(tensor_con)
    bilstm_dropout_tensor = bilstm_dropout(bilstm_tensor)
    
    crf_dropout_tensor = crf_dropout(bilstm_dropout_tensor)
    crf_dense_tensor = crf_dense(crf_dropout_tensor)
    output = crf(crf_dense_tensor)

    self.tf_model = keras.Model(inputs=model_inputs, outputs=[output])
    self.crf_layer = crf

我在使用数据训练的时候，代码抽取如下： def trainFunction(.....): bert_embed = BertEmbedding('./Data/路径', sequence_length=maxlength) pos_embed = BareEmbedding(embedding_size=32) ner_embed = BareEmbedding(embedding_size=32)

    selfmodel = BiLSTM_TEST_Model(bert_embed, pos_embed, ner_embed, sequence_length=maxlength)
    history = selfmodel.fit(x_train=(train_x, train_pos_x, train_ner_x,), y_train=train_y, 
                            x_validate=(valid_x, valid_pos_x, valid_ner_x), y_validate=valid_y, batch_size=batchsize, epochs=12)

Reproduce

报错信息如下： File "/venv/lib/python3.6/site-packages/kashgari/tasks/labeling/abc_model.py", line 177, in fit fit_kwargs=fit_kwargs) File "/venv/lib/python3.6/site-packages/kashgari/tasks/labeling/abc_model.py", line 208, in fit_generator self.build_model_generator([g for g in [train_sample_gen, valid_sample_gen] if g]) File "/venv/lib/python3.6/site-packages/kashgari/tasks/labeling/abc_model.py", line 85, in build_model_generator self.text_processor.build_vocab_generator(generators) File "/venv/lib/python3.6/site-packages/kashgari/processors/sequence_processor.py", line 84, in build_vocab_generator count = token2count.get(token, 0) TypeError: unhashable type: 'list'

kashgari下build_vocab_generator()报错位置： def build_vocab_generator(self, generators: List[CorpusGenerator]) -> None: if not self.vocab2idx: vocab2idx = self._initial_vocab_dic

        token2count: Dict[str, int] = {}

        for gen in generators:
            for sentence, label in tqdm.tqdm(gen, desc="Preparing text vocab dict"):
                if self.build_vocab_from_labels:
                    target = label
                else:
                    target = sentence
                for token in target:      ## 我的输入是嵌套list，这里token是每一个list，就报错了。
                    count = token2count.get(token, 0)
                    token2count[token] = count + 1

DEBUG追踪看了下：我fit输入的x_train是三个，CorpusGenerator得到的generators的x也是嵌套的三个list，在build_vocab_generator的时候，就报错了。

我需要重定义build_vocab_generator吗？ 除了这个地方，我的模型输入需要三个embedding model，这会导致我的self.vocab2idx/idx2vocab是不是也得定义三种，还有哪些地方需要我重新定义的吗？debug跟着跟着就晕了 T_T

求助！！

You must follow the issue template and provide as much information as possible. otherwise, this issue will be closed. 请按照 issue 模板要求填写信息。如果没有按照 issue 模板填写，将会忽略并关闭这个 issue

Check List

Thanks for considering to open an issue. Before you submit your issue, please confirm these boxes are checked.

You can post pictures, but if specific text or code is required to reproduce the issue, please provide the text in a plain text format for easy copy/paste.

• [ :heavy_check_mark: ] I have searched in existing issues but did not find the same one.
• [ :heavy_check_mark: ] I have read the documents

Environment

• OS [e.g. Mac OS, Linux]: linux
• Python Version: 3.9
• requirements.txt:

 !pip install tensorflow==2.5
 !pip install tensorflow_addons==0.13.0
 !pip install kashgari==2.0.2

Question Load_model Error'Keyword argument not understood:', 'center'

i got this error when I'm trying to load the checkpoints TypeError: ('Keyword argument not understood:', 'center') Note that these checkpoints, i trained 2 years ago, so i tried the last version of kashgri 2.0.2 and the old version but nothing worked any help??

The code is the same：in layer_stack = [ L.Bidirectional(L.GRU(**config['layer_bgru']), name='layer_bgru'), L.Dropout(**config['layer_dropout'], name='layer_dropout'), L.TimeDistributed(L.Dense(output_dim, **config['layer_time_distributed']), name='layer_time_distributed'), L.Activation(**config['layer_activation']) ]

You must follow the issue template and provide as much information as possible. otherwise, this issue will be closed. 请按照 issue 模板要求填写信息。如果没有按照 issue 模板填写，将会忽略并关闭这个 issue

Check List

Thanks for considering to open an issue. Before you submit your issue, please confirm these boxes are checked.

You can post pictures, but if specific text or code is required to reproduce the issue, please provide the text in a plain text format for easy copy/paste.

• [√] I have searched in existing issues but did not find the same one.
• [√] I have read the documents

Environment

• OS [e.g. Mac OS, Linux]: Win10
• Python Version: 3.7
• requirements.txt: TensorFlow 2.3 kashgari 2.0.1

[Paste requirements.txt file here]

Question

不管是使用SMP2018ECDTCorpus还是自己的数据集，在使用CNN开头的系列文本分类模型时，这个accuracy都不行，也试过改变学习率和epoch等参数，但是没啥作用，不知道不是这些模型本身有问题

from kashgari.corpus import SMP2018ECDTCorpus from kashgari.tasks.classification import CNN_Model from kashgari.callbacks import EvalCallBack

import logging logging.basicConfig(level='DEBUG')

train_x, train_y = SMP2018ECDTCorpus.load_data('train') valid_x, valid_y = SMP2018ECDTCorpus.load_data('valid') test_x, test_y = SMP2018ECDTCorpus.load_data('test')

model = CNN_Model() model.fit(train_x, train_y, valid_x, valid_y,batch_size=64,epochs=14) model.evaluate(test_x,test_y,batch_size=64)

运行结果： 2022-04-14 18:08:55,276 [DEBUG] kashgari - loaded 1881 samples from C:\Users\hwq45.kashgari\datasets\SMP2018ECDTCorpus\train.csv. Sample: x[0]: ['打', '开', '河', '南', '英', '东', '网', '站'] y[0]: website 2022-04-14 18:08:55,280 [DEBUG] kashgari - loaded 418 samples from C:\Users\hwq45.kashgari\datasets\SMP2018ECDTCorpus\valid.csv. Sample: x[0]: ['来', '一', '首', '，', '灵', '岩', '。'] y[0]: poetry 2022-04-14 18:08:55,284 [DEBUG] kashgari - loaded 770 samples from C:\Users\hwq45.kashgari\datasets\SMP2018ECDTCorpus\test.csv. Sample: x[0]: ['给', '曹', '广', '义', '打', '电', '话'] y[0]: telephone Preparing text vocab dict: 100%|██████████| 1881/1881 [00:00<00:00, 943831.30it/s] Preparing text vocab dict: 100%|██████████| 418/418 [00:00<00:00, 416936.76it/s] 2022-04-14 18:08:55,291 [DEBUG] kashgari - --- Build vocab dict finished, Total: 875 --- 2022-04-14 18:08:55,291 [DEBUG] kashgari - Top-10: ['[PAD]', '[UNK]', '[CLS]', '[SEP]', '的', '么', '我', '。', '怎', '你'] Preparing classification label vocab dict: 100%|██████████| 1881/1881 [00:00<?, ?it/s] Preparing classification label vocab dict: 100%|██████████| 418/418 [00:00<?, ?it/s] Calculating sequence length: 100%|██████████| 1881/1881 [00:00<00:00, 1894234.29it/s] Calculating sequence length: 100%|██████████| 418/418 [00:00<00:00, 419430.40it/s] 2022-04-14 18:08:55,309 [DEBUG] kashgari - Calculated sequence length = 15 2022-04-14 18:08:55,337 [DEBUG] kashgari - Model: "functional_43"

Layer (type) Output Shape Param #

input (InputLayer) [(None, None)] 0

layer_embedding (Embedding) (None, None, 100) 87500

conv1d_6 (Conv1D) (None, None, 128) 64128

global_max_pooling1d_4 (Glob (None, 128) 0

dense_14 (Dense) (None, 64) 8256

dense_15 (Dense) (None, 31) 2015

activation_10 (Activation) (None, 31) 0

Total params: 161,899 Trainable params: 161,899 Non-trainable params: 0

Epoch 1/14 29/29 [==============================] - 0s 8ms/step - loss: 3.3098 - accuracy: 0.1735 - val_loss: 3.1836 - val_accuracy: 0.1901 Epoch 2/14 29/29 [==============================] - 0s 5ms/step - loss: 3.0778 - accuracy: 0.1992 - val_loss: 3.0883 - val_accuracy: 0.1953 Epoch 3/14 29/29 [==============================] - 0s 4ms/step - loss: 3.0232 - accuracy: 0.1992 - val_loss: 3.0700 - val_accuracy: 0.2005 Epoch 4/14 29/29 [==============================] - 0s 4ms/step - loss: 3.0164 - accuracy: 0.1987 - val_loss: 3.0591 - val_accuracy: 0.1901 Epoch 5/14 29/29 [==============================] - 0s 4ms/step - loss: 3.0395 - accuracy: 0.1943 - val_loss: 3.0622 - val_accuracy: 0.1979 Epoch 6/14 29/29 [==============================] - 0s 4ms/step - loss: 3.0327 - accuracy: 0.2003 - val_loss: 3.0659 - val_accuracy: 0.1875 Epoch 7/14 29/29 [==============================] - 0s 4ms/step - loss: 3.0361 - accuracy: 0.1948 - val_loss: 3.0711 - val_accuracy: 0.1953 Epoch 8/14 29/29 [==============================] - 0s 4ms/step - loss: 3.0347 - accuracy: 0.1987 - val_loss: 3.0581 - val_accuracy: 0.1901 Epoch 9/14 29/29 [==============================] - 0s 4ms/step - loss: 3.0155 - accuracy: 0.1981 - val_loss: 3.0576 - val_accuracy: 0.2005 Epoch 10/14 29/29 [==============================] - 0s 4ms/step - loss: 3.0415 - accuracy: 0.2036 - val_loss: 3.0651 - val_accuracy: 0.1953 Epoch 11/14 29/29 [==============================] - 0s 4ms/step - loss: 3.0296 - accuracy: 0.1992 - val_loss: 3.0850 - val_accuracy: 0.1849 Epoch 12/14 29/29 [==============================] - 0s 4ms/step - loss: 3.0132 - accuracy: 0.2053 - val_loss: 3.0643 - val_accuracy: 0.1953 Epoch 13/14 29/29 [==============================] - 0s 4ms/step - loss: 3.0523 - accuracy: 0.1899 - val_loss: 3.0639 - val_accuracy: 0.2005 Epoch 14/14 29/29 [==============================] - 0s 4ms/step - loss: 3.7734 - accuracy: 0.2075 - val_loss: 3.0653 - val_accuracy: 0.2031