Question

在使用Keras How to predict sentiment analysis using Keras imdb dataset?进行示例情感分析之后，我想看看它如何处理新数据。

通过“我爱这部电影”，它给出了：

[[0.72863275]]

[[1]]

通过“我讨厌这部电影”，它给出了：

[[0.727815]]
[[1]]

我对“我爱这部电影”和“我恨这部电影”是同一类型吗？（正面和负面）（如果不是），如何使用模型判断评论是正面还是负面？

谢谢。

完整代码：

from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM, Convolution1D, Flatten, Dropout
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from keras.callbacks import TensorBoard

# Using keras to load the dataset with the top_words
top_words = 10000
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)

# Pad the sequence to the same length
max_review_length = 1600
X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)

# Using embedding from Keras
embedding_vecor_length = 300
model = Sequential()
model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length))

# Convolutional model (3x conv, flatten, 2x dense)
model.add(Convolution1D(64, 3, padding='same'))
model.add(Convolution1D(32, 3, padding='same'))
model.add(Convolution1D(16, 3, padding='same'))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(180,activation='sigmoid'))
model.add(Dropout(0.2))
model.add(Dense(1,activation='sigmoid'))

# Log to tensorboard
tensorBoardCallback = TensorBoard(log_dir='./logs', write_graph=True)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

model.fit(X_train, y_train, epochs=1, callbacks=[tensorBoardCallback], batch_size=64)

# Evaluation on the test set
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))

model.save("trained_demo.h5")

import nltk
nltk.download('punkt')

from nltk import word_tokenize
from keras.preprocessing import sequence
word2index = imdb.get_word_index()
test=[]
for word in word_tokenize( "i hate this movie"):
     test.append(word2index[word])

test=sequence.pad_sequences([test],maxlen=max_review_length)
model.predict(test)

print (model.predict(test))
print (model.predict_classes(test))

使用Keras训练模型以获取新数据

0 个答案: