必要准备

1.安装pydot

pip install pydot

2.安装GraphViz

绘图代码

from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Dense, Input, LSTM, Lambda, Embedding, Dropout, Activation,GRU,Bidirectional
from keras.layers import Conv1D,Conv2D,MaxPooling2D,GlobalAveragePooling1D,GlobalMaxPooling1D, MaxPooling1D, Flatten

#from keras.layers import CuDNNGRU
from tensorflow.compat.v1.keras.layers import CuDNNGRU as GRU
#from keras.layers import CuDNNLSTM
from tensorflow.compat.v1.keras.layers import CuDNNLSTM as LSTM
from keras.layers import SpatialDropout1D

from keras.layers.merge import concatenate, Concatenate, Average, Dot, Maximum, Multiply, Subtract, average
from keras.models import Model
from keras.optimizers import RMSprop,Adam
from keras.layers.normalization import BatchNormalization
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.optimizers import SGD
from keras import backend as K
from sklearn.decomposition import TruncatedSVD, NMF, LatentDirichletAllocation
from keras.layers import SpatialDropout1D
from keras.layers.wrappers import Bidirectional

# 使用Keras构造TextCNN模型
def TextCNN(max_len,max_cnt,embed_size, num_filters,kernel_size,conv_action, mask_zero):
    
    _input = Input(shape=(max_len,), dtype='int32')
    _embed = Embedding(max_cnt, embed_size, input_length=max_len, mask_zero=mask_zero)(_input) # 词嵌入
    _embed = SpatialDropout1D(0.15)(_embed)
    warppers = []
    
    for _kernel_size in kernel_size:
        conv1d = Conv1D(filters=num_filters, kernel_size=_kernel_size, activation=conv_action)(_embed)
        warppers.append(GlobalMaxPooling1D()(conv1d)) # 卷积层
                        
    fc = concatenate(warppers) # concatenate:连接
    
    fc = Dropout(0.5)(fc)
    #fc = BatchNormalization()(fc)
    fc = Dense(256, activation='relu')(fc) # 全连接层，relu作为激活函数，Dense(64)是一个具有 64个隐藏神经元的全连接层。
    fc = Dropout(0.25)(fc)
    #fc = BatchNormalization()(fc) 
    preds = Dense(8, activation = 'softmax')(fc)###softmax作为激活函数
    
    model = Model(inputs=_input, outputs=preds)
    
    model.compile(loss='categorical_crossentropy',
        optimizer='adam',
        metrics=['accuracy'])
    return model

# 绘制模型图
from keras.utils import plot_model
textcnn = TextCNN(6000,296,256,64,[2,4,6,8,10,12,14],'relu',False)
plot_model(textcnn,'D:/A_graduation_project/textcnn.png',show_shapes=True,dpi=100)

结果图