from keras.preprocessing import sequence # 句子序列
from keras.models import sequential # ai模型
from keras.layers import dense, embedding # 控制層
from keras.layers import lstm # rnn神經網路的一種
from keras.datasets import imdb # 資料集
max_feature =
20000
# 特徵數量. 最大的單詞數量
max_length =
80# 最大長度
batch_size =
32# 批量的大小
(x_train, y_train)
,(x_test, y_test)
= imdb.load_data(num_words=max_feature)
# 載入資料
print
(len
(x_train)
,len
(x_test)
)print
(x_train.shape, x_test.shape)
# 形狀
print
("資料填充以後"
)x_train = sequence.pad_sequences(x_train, maxlen=max_length)
# 資料填充
x_test = sequence.pad_sequences(x_test, maxlen=max_length)
# 資料填充
print
(x_train.shape, x_test.shape)
# 形狀
print
(y_train.shape, y_test.shape)
model = sequential(
)model.add(embedding(max_feature,
128)
)# 嵌入層
model.add(lstm(
128, dropout=
0.2, recurrent_dropout=
0.2)
)model.add(dense(
1, activation=
"sigmoid"))
# 輸出結果， 0 1
model.
compile
(loss=
"binary_crossentropy"
,# 損失最小
optimizer=
"adam"
,# 優化
metrics=
["accuracy"])
# 精確
print
("train---------------"
)model.fit(x_train, y_train,
batch_size=batch_size,
epochs=5,
validation_data=
(x_test, y_test)
)score = model.evaluate(x_test, y_test, batch_size=batch_size)
print
("loss函式"
, score[0]
,"識別率"
, score[1]
)print
(model.predict(x_test[1]
))# **的範例
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