检查输入时出错：预期embedding_1_input具有形状（50，）但形状为（1，）的数组

Question

我有一个错误：

Error when checking input: 
expected embedding_1_input to have shape (50,) but got array with shape (1,)

当我将输入参数input_length更改为1时，错误变为：

Error when checking input: 
expected embedding_1_input to have shape (1,) but got array with shape (50,)

我的代码如下：

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer, TfidfVectorizer
import numpy as np
import os
from keras import metrics
from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional, BatchNormalization, Activation, Conv1D, MaxPooling1D, Flatten, GlobalMaxPooling1D
from keras.models import load_model
from keras.callbacks import ModelCheckpoint
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.preprocessing import sequence, text
import pandas as pd
from gensim import corpora
from gensim import models

maxlen = 50
batch_size = 128
np.random.seed(7)

df = pd.read_csv('C:/Users/DMY/Peer-logic-master/newdata/topnine.csv',encoding='utf-8')

x = df["REVIEW"].fillna("na").values  
y = df["TAG"]
encoder = LabelEncoder()
encoder.fit(y)
y = encoder.transform(y)

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.1)

word_list = []

for i in range(len(x_train)):
    word_list.append(x_train[i].split(' '))


dictionary = corpora.Dictionary(word_list)
corpus = [dictionary.doc2bow(text) for text in word_list]


tfidf = models.TfidfModel(corpus)

X_train_id = [] 
word_id_dict = dictionary.token2id
for i in range(len(word_list)):
    sen_id = []
    word_sen = word_list[i]
    for j in range(len(word_sen)):       
        id = word_id_dict.get(word_sen[j])
        if id is None:
            id = 0 
        sen_id.append(id)
    X_train_id.append(sen_id)

X_train_tfidf_vec = []  
for i in range(len(x_train)):
    temp = {}
    string = x_train[i]
    string_bow = dictionary.doc2bow(string.lower().split()) 
    string_tfidf = tfidf[string_bow]

    for j in range(len(string_tfidf)):
#         print(string_tfidf[j][0])
        temp[string_tfidf[j][0]] = string_tfidf[j][1]
#         print(temp)
    X_train_tfidf_vec.append(temp)

X_train_tfidf = []  
for i in range(len(X_train_id)):
    sen_id = X_train_id[i]
    sen_id_tfidf = X_train_tfidf_vec[i]
    sen = []
    for j in range(len(sen_id)):
        word_id = sen_id[j]
        word_tfidf = sen_id_tfidf.get(word_id)
        if word_tfidf is None:
            word_tfidf = 0
        sen.append(word_tfidf)
    X_train_tfidf.append(sen)

x_train_tfidf = sequence.pad_sequences(X_train_tfidf, maxlen=maxlen,dtype='float64')
#print(len(x_train_tfidf))
#print(x_train_tfidf)

model4 = Sequential()
model4.add(Embedding(len(x_train_tfidf)+1, 100, input_length = ))#input_dim,output_dim,input_length
model4.add(Dropout(0.6))
model4.add(LSTM(100, recurrent_dropout=0.6))
model4.add(Dropout(0.6))
model4.add(Dense(1, activation='sigmoid'))
model4.compile('adam', 'binary_crossentropy', metrics=['accuracy'])
model4_history = model4.fit(x_train_tfidf, y_train, batch_size=batch_size, epochs=7,
                            validation_split=0.1)
score4, acc4 = model4.evaluate(x_test, y_test,
                               batch_size=batch_size)
print('Test accuracy for LSTM Model is:', acc4)
y_pred4 = model4.predict(x_test)
y_pred4 = (y_pred4 > 0.5)
print(classification_report(y_test, y_pred4))

Answer 1

根据official documentation，嵌入层采用许多不同的初始化参数。