23.12 1D CNN 文本分类
优质
小牛编辑
130浏览
2023-12-01
2个轮次后达到 0.89 的测试精度。 </br> 在 Intel i5 2.4Ghz CPU 上每轮次 90秒。 </br> 在 Tesla K40 GPU 上每轮次 10秒。
from __future__ import print_function
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import Conv1D, GlobalMaxPooling1D
from keras.datasets import imdb
# 设置参数:
max_features = 5000
maxlen = 400
batch_size = 32
embedding_dims = 50
filters = 250
kernel_size = 3
hidden_dims = 250
epochs = 2
print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')
print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
print('Build model...')
model = Sequential()
# 我们从有效的嵌入层开始,该层将 vocab 索引映射到 embedding_dims 维度
model.add(Embedding(max_features,
embedding_dims,
input_length=maxlen))
model.add(Dropout(0.2))
# 我们添加了一个 Convolution1D,它将学习大小为 filter_length 的过滤器词组过滤器:
model.add(Conv1D(filters,
kernel_size,
padding='valid',
activation='relu',
strides=1))
# 我们使用最大池化:
model.add(GlobalMaxPooling1D())
# We add a vanilla hidden layer:
model.add(Dense(hidden_dims))
model.add(Dropout(0.2))
model.add(Activation('relu'))
# 我们投影到单个单位输出层上,并用 sigmoid 压扁它:
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test))