文本分类:TextCNN(tensorflow2.0实现)
个人其他链接
github
blog
TextRNN
完整代码在github
TextCNN原始论文： Convolutional Neural Networks for Sentence Classification
TextCNN 的网络结构：
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基于tensorflow2.0的keras实现
自定义model
这是tensorflow2.0推荐的写法，继承Model，使模型子类化
需要注意的几点：
- 如果需要使用到其他Layer结构或者Sequential结构，需要在__init__()函数里赋值
- 在model没有fit前，想调用summary函数时显示模型各层shape时，则需要自定义一个函数去build下模型，类似下面代码中的build_graph函数
- summary()显示shape顺序，是按照__init__()里layer赋值的顺序
# -*- coding: utf-8 -*-
# @Time : 2020/4/20 14:44
# @Author : zdqzyx
# @File : textcnn.py
# @Software: PyCharm
import tensorflow as tf
from tensorflow.keras.layers import Embedding, Conv1D, GlobalAveragePooling1D, Dense, Concatenate, GlobalMaxPooling1D
from tensorflow.keras import Model
class TextCNN(Model):
def __init__(self,
maxlen,
max_features,
embedding_dims,
class_num,
kernel_sizes=[1,2,3],
kernel_regularizer=None,
last_activation='softmax'
):
'''
:param maxlen: 文本最大长度
:param max_features: 词典大小
:param embedding_dims: embedding维度大小
:param kernel_sizes: 滑动卷积窗口大小的list, eg: [1,2,3]
:param kernel_regularizer: eg: tf.keras.regularizers.l2(0.001)
:param class_num:
:param last_activation:
'''
super(TextCNN, self).__init__()
self.maxlen = maxlen
self.kernel_sizes = kernel_sizes
self.class_num = class_num
self.embedding = Embedding(input_dim=max_features, output_dim=embedding_dims, input_length=maxlen)
self.conv1s = []
self.avgpools = []
for kernel_size in kernel_sizes:
self.conv1s.append(Conv1D(filters=128, kernel_size=kernel_size, activation='relu', kernel_regularizer=kernel_regularizer))
self.avgpools.append(GlobalMaxPooling1D())
self.classifier = Dense(class_num, activation=last_activation, )
def call(self, inputs, training=None, mask=None):
if len(inputs.get_shape()) != 2:
raise ValueError('The rank of inputs of TextCNN must be 2, but now is %d' % len(inputs.get_shape()))
if inputs.get_shape()[1] != self.maxlen:
raise ValueError('The maxlen of inputs of TextCNN must be %d, but now is %d' % (self.maxlen, inputs.get_shape()[1]))
emb = self.embedding(inputs)
conv1s = []
for i in range(len(self.kernel_sizes)):
c = self.conv1s[i](emb) # (batch_size, maxlen-kernel_size+1, filters)
c = self.avgpools[i](c) # # (batch_size, filters)
conv1s.append(c)
x = Concatenate()(conv1s) # (batch_size, len(self.kernel_sizes)*filters)
output = self.classifier(x)
return output
def build_graph(self, input_shape):
'''自定义函数，在调用model.summary()之前调用
'''
input_shape_nobatch = input_shape[1:]
self.build(input_shape)
inputs = tf.keras.Input(shape=input_shape_nobatch)
if not hasattr(self, 'call'):
raise AttributeError("User should define 'call' method in sub-class model!")
_ = self.call(inputs)
main
构建模型helper，帮助构建模型，以及定义管理各种回调函数
- 其中主要回调函数有三个：EarlyStopping, TensorBoard, ModelCheckpoint
# -*- coding: utf-8 -*-
# @Time : 2020/4/20 14:43
# @Author : zdqzyx
# @File : main.py
# @Software: PyCharm
# ===================== set random ===========================
import numpy as np
import tensorflow as tf
import random as rn
np.random.seed(0)
rn.seed(0)
tf.random.set_seed(0)
# =============================================================
import os
from tensorflow.keras.callbacks import EarlyStopping, TensorBoard, ModelCheckpoint
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing.sequence import pad_sequences
from textcnn import TextCNN
def checkout_dir(dir_path, do_delete=False):
import shutil
if do_delete and os.path.exists(dir_path):
shutil.rmtree(dir_path)
if not os.path.exists(dir_path):
print(dir_path, 'make dir ok')
os.makedirs(dir_path)
class ModelHepler:
def __init__(self, class_num, maxlen, max_features, embedding_dims, epochs, batch_size):
self.class_num = class_num
self.maxlen = maxlen
self.max_features = max_features
self.embedding_dims = embedding_dims
self.epochs = epochs
self.batch_size = batch_size
self.callback_list = []
print('Bulid Model...')
self.create_model()
def create_model(self):
model = TextCNN(maxlen=self.maxlen,
max_features=self.max_features,
embedding_dims=self.embedding_dims,
class_num=self.class_num,
kernel_sizes=[2,3,5],
kernel_regularizer=None,
last_activation='softmax')
model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'],
)
model.build_graph(input_shape=(None, maxlen))
model.summary()
self.model = model
def get_callback(self, use_early_stop=True, tensorboard_log_dir='logs\\TextCNN-epoch-5', checkpoint_path="save_model_dir\\cp-moel.ckpt"):
callback_list = []
if use_early_stop:
# EarlyStopping
early_stopping = EarlyStopping(monitor='val_accuracy', patience=7, mode='max')
callback_list.append(early_stopping)
if checkpoint_path is not None:
# save model
checkpoint_dir = os.path.dirname(checkpoint_path)
checkout_dir(checkpoint_dir, do_delete=True)
# 创建一个保存模型权重的回调
cp_callback = ModelCheckpoint(filepath=checkpoint_path,
monitor='val_accuracy',
mode='max',
save_best_only=True,
save_weights_only=True,
verbose=1,
period=2,
)
callback_list.append(cp_callback)
if tensorboard_log_dir is not None:
# tensorboard --logdir logs/TextCNN-epoch-5
checkout_dir(tensorboard_log_dir, do_delete=True)
tensorboard_callback = TensorBoard(log_dir=tensorboard_log_dir, histogram_freq=1)
callback_list.append(tensorboard_callback)
self.callback_list = callback_list
def fit(self, x_train, y_train, x_val, y_val):
print('Train...')
self.model.fit(x_train, y_train,
batch_size=self.batch_size,
epochs=self.epochs,
verbose=2,
callbacks=self.callback_list,
validation_data=(x_val, y_val))
def load_model(self, checkpoint_path):
checkpoint_dir = os.path.dirname((checkpoint_path))
latest = tf.train.latest_checkpoint(checkpoint_dir)
print('restore model name is : ', latest)
# 创建一个新的模型实例
# model = self.create_model()
# 加载以前保存的权重
self.model.load_weights(latest)
# ================ params =========================
class_num = 2
maxlen = 400
embedding_dims = 200
epochs = 10
batch_size = 128
max_features = 5000
MODEL_NAME = 'TextCNN-epoch-10-emb-200'
use_early_stop=True
tensorboard_log_dir = 'logs\\{}'.format(MODEL_NAME)
# checkpoint_path = "save_model_dir\\{}\\cp-{epoch:04d}.ckpt".format(MODEL_NAME, '')
checkpoint_path = 'save_model_dir\\'+MODEL_NAME+'\\cp-{epoch:04d}.ckpt'
# ====================================================================
print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
print('Pad sequences (samples x time)...')
x_train = pad_sequences(x_train, maxlen=maxlen, padding='post')
x_test = pad_sequences(x_test, maxlen=maxlen, padding='post')
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
model_hepler = ModelHepler(class_num=class_num,
maxlen=maxlen,
max_features=max_features,
embedding_dims=embedding_dims,
epochs=epochs,
batch_size=batch_size
)
model_hepler.get_callback(use_early_stop=use_early_stop, tensorboard_log_dir=tensorboard_log_dir, checkpoint_path=checkpoint_path)
model_hepler.fit(x_train=x_train, y_train=y_train, x_val=x_test, y_val=y_test)
print('Test...')
result = model_hepler.model.predict(x_test)
test_score = model_hepler.model.evaluate(x_test, y_test,
batch_size=batch_size)
print("test loss:", test_score[0], "test accuracy", test_score[1])
model_hepler = ModelHepler(class_num=class_num,
maxlen=maxlen,
max_features=max_features,
embedding_dims=embedding_dims,
epochs=epochs,
batch_size=batch_size
)
model_hepler.load_model(checkpoint_path=checkpoint_path)
# 重新评估模型
loss, acc = model_hepler.model.evaluate(x_test, y_test, verbose=2)
print("Restored model, accuracy: {:5.2f}%".format(100 * acc))