我的模型精度只有百分之一

我想为聊天机器人创建一个模型。有一个代码。但是，200个时期的训练导致模型的准确率只有百分之一点五：（。我至少有百分之七十。我给出了下面的代码：`import nltk

nltk.download('punkt')

nltk.download('wordnet')

nltk.download('omw-1.4')

from nltk.stem import WordNetLemmatizer

lemmatizer = WordNetLemmatizer()

import json

import pickle

import numpy as np

from keras.models import Sequential

from keras.layers import Dense, Activation, Dropout

from keras.optimizers import SGD

import random

words=[]

classes = []

documents = []

ignore_words = ['?', '!']

data_file = open('intents.json').read()

intents = json.loads(data_file)

for intent in intents['intents']:

    for pattern in intent['patterns']:

        # take each word and tokenize it

        w = nltk.word_tokenize(pattern)

        words.extend(w)

        # adding documents

        documents.append((w, intent['tag']))

        # adding classes to our class list

        if intent['tag'] not in classes:

            classes.append(intent['tag'])

words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]

words = sorted(list(set(words)))

classes = sorted(list(set(classes)))

print (len(documents), "documents")

print (len(classes), "classes", classes)

print (len(words), "unique lemmatized words", words)

pickle.dump(words,open('words.pkl','wb'))

pickle.dump(classes,open('classes.pkl','wb'))

# initializing training data

training = []

output_empty = [0] * len(classes)

for doc in documents:

    # initializing bag of words

    bag = []

    # list of tokenized words for the pattern

    pattern_words = doc[0]

    # lemmatize each word - create base word, in attempt to represent related words

    pattern_words = [lemmatizer.lemmatize(word.lower()) for word in pattern_words]

    # create our bag of words array with 1, if word match found in current pattern

    for w in words:

        bag.append(1) if w in pattern_words else bag.append(0)

    # output is a '0' for each tag and '1' for current tag (for each pattern)

    output_row = list(output_empty)

    output_row[classes.index(doc[1])] = 1

    training.append([bag, output_row])

# shuffle our features and turn into np.array

random.shuffle(training)

training = np.array(training)

# create train and test lists. X - patterns, Y - intents

train_x = list(training[:,0])

train_y = list(training[:,1])

打印（“创建的培训数据”）

# Create model - 3 layers. First layer 128 neurons, second layer 64 neurons and 3rd output layer contains number of neurons

# equal to number of intents to predict output intent with softmax

model = Sequential()

model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(64, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(len(train_y[0]), activation='softmax'))

# Compile model. Stochastic gradient descent with Nesterov accelerated gradient gives good results for this model

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

#fitting and saving the model

hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)

model.save('chatbot_model.h5', hist)

print("model created")`

我认为这是因为庞大的培训数据库。当我在数据库中有大约30个训练短语时，预测值为90-100%。但当我将数据库增加到400-450个短语时，结果是百分之一点五的预测。但也许教学方法不好？我不知道该怎么想了。很可能是一个数据库。