检查输入时出错:期望embedding_1_input具有形状(4，)，但数组的形状为(1，)

问题描述

我将预训练的嵌入向量用于我的keras模型.在我做完这一切之前，现在我得到了这个错误:

I use pretrained embedding vectors for my keras model. Before I did it everything worked and now I get this error:

也许有人可以帮助我，我在这里做错了什么.我不确定我是否正确执行了model.fit和model.evaluate.也许有问题吗?

Maybe somebody can help me, what I do wrong here. I am not sure if I did correct model.fit and model.evaluate. Maybe there is a problem?

import csv
import numpy as np
np.random.seed(42)
from keras.models import Sequential, Model
from keras.layers import *
from random import shuffle
from sklearn.model_selection import train_test_split
from keras import optimizers
from keras.callbacks import EarlyStopping
from itertools import groupby
from numpy import asarray
from numpy import zeros
from numpy import array
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences

#function makes a list of antonyms and synonyms from the files
def preprocessing(filename):
    list_words = []
    with open(filename) as tsv:
       for line in csv.reader(tsv, dialect="excel-tab"):
           list_words.append([line[0], line[1]])
    return list_words

#function make a list of not relevant pairs by mixing synonyms and
antonyms
def notrelevant(filename, filename2):
    list_words = []
    with open(filename) as tsv:
        with open(filename2) as tsv2:
           for lines in zip(csv.reader(tsv, dialect="excel-tab"),csv.reader(tsv2, dialect="excel-tab")):
                list_words.append([lines[0][0], lines[1][1]])
    return list_words

antonyms_list = preprocessing("antonyms.tsv")
synonyms_list = preprocessing("synonyms.tsv")
notrelevant_list = notrelevant("antonyms.tsv", "synonyms.tsv")

# function combines all antonyms, synonyms in one list with labels,
shuffle them
def data_prepare(ant,syn,nrel):
        data = []
    for  elem1,elem2 in ant:
        data.append([[elem1,elem2], "Antonyms"])
    for elem1, elem2 in syn:
        data.append([[elem1, elem2], "Synonyms"])
    for elem1, elem2 in nrel:
        data.append([[elem1, elem2], "Not relevant"])
    shuffle(data)
    return data


data_with_labels_shuffled =
data_prepare(antonyms_list,synonyms_list,notrelevant_list)

def label_to_onehot(labels):
    mapping = {label: i for i, label in enumerate(set(labels))}

    one_hot = np.empty((len(labels), 3))
    for i, label in enumerate(labels):
        entry = [0] * len(mapping)
        entry[mapping[label]] = 1
        one_hot[i] = entry
    return (one_hot)

def words_to_ids(labels):
    vocabulary = []
    word_to_id = {}
    ids = []
    for word1,word2 in labels:
        vocabulary.append(word1)
        vocabulary.append(word2)
    counter = 0
    for word in vocabulary:
        if word not in word_to_id:
            word_to_id[word] = counter
            counter += 1
    for word1,word2 in labels:
        ids.append([word_to_id [word1], word_to_id [word2]])
    return (ids)

def split_data(datas):
    data = np.array(datas)
    X, y = data[:, 0], data[:, 1]
    # split the data to get 60% train and 40% test
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)
    y_train = label_to_onehot(y_train)
    X_dev, X_test, y_dev, y_test = train_test_split(X_test, y_test, test_size=0.5, random_state=42)
    y_dev = label_to_onehot(y_dev)
    y_test = label_to_onehot(y_test)
    return X_train, y_train, X_dev, y_dev, X_test, y_test

X_train, y_train, X_dev, y_dev, X_test, y_test = split_data(data_with_labels_shuffled)

# prepare tokenizer
t = Tokenizer()
t.fit_on_texts(X_train)
vocab_size = len(t.word_index) + 1
# integer encode the documents
encoded_docs = t.texts_to_sequences(X_train)


# load the whole embedding into memory
embeddings_index = dict()
f = open('glove.6B.50d.txt')
for line in f:
    values = line.split()
    word = values[0]
    coefs = asarray(values[1:], dtype='float32')
    embeddings_index[word] = coefs
f.close()
# create a weight matrix for words in training docs
embedding_matrix = zeros((vocab_size, 50))
for word, i in t.word_index.items():
    embedding_vector = embeddings_index.get(word)
    if embedding_vector is not None:
        embedding_matrix[i] = embedding_vector



VOCABSIZE = len(data_with_labels_shuffled)
EMBSIZE = 50
HIDDENSIZE = 50
KERNELSIZE = 5
MAXEPOCHS = 5

model = Sequential()
model.add(Embedding(vocab_size, 50, weights=[embedding_matrix],
input_length=4, trainable=False))
model.add(Dropout(0.25))
model.add(Bidirectional(GRU(units = HIDDENSIZE // 2)))
#model.add(Flatten())
model.add(Dense(units = 3, activation = "softmax"))
model.compile(loss='categorical_crossentropy', optimizer="adam",
metrics=['accuracy'])


earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=2, verbose=0, mode='min')
model.fit (X_train, y_train,
       batch_size=64,
       callbacks = [earlystop],
       epochs=100,
       validation_data=(X_dev, y_dev),
       verbose=1)
scores = model.evaluate(X_test, y_testbatch_size=64)

print("Accuracy is: %.2f%%" %(scores[1] * 100))

推荐答案

我认为问题在于，您应该将encoding_docs传递给model.fit()函数而不是X_train，因为encode_docs包含训练数据的标记化，而X_train仍然仅包含单词列表.此外，您必须确保Embedding层的input_length参数与您在encode_docs中创建的这些标记化训练示例的长度相匹配.

I think the problem is that you should pass encoded_docs to your model.fit() function instead of X_train since encoded_docs contains the tokenization of your training data and X_train still only contains a list of words. Moreover, you have to make sure that the input_length parameter of your Embedding layer matches the length of these tokenized training examples that you have created in encoded_docs.

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