def simple_embedding():
"""
一个简单词嵌入的例子，使用的imdb数据集
:return:
"""
max_features = 10000
maxlen = 20 # 在20额单词后截断文本
(x_train, y_train), (x_test, y_test) = load_local(num_words=max_features)
x_train = preprocessing.sequence.pad_sequences(
x_train, maxlen=maxlen) # 整数列表转化为二维数组
x_test = preprocessing.sequence.pad_sequences(x_test, maxlen=maxlen)
model = Sequential()
model.add(Embedding(
10000, 8, input_length=maxlen)) # 参数1是标记的个数，参数2是嵌入的维度，参数3是输入的最大长度
model.add(Flatten()) # 进入分类器之前要展平
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['acc'])
model.summary()
history = model.fit(x_train,
y_train,
epochs=10,
batch_size=32,
validation_split=0.2)
show_results(history)
import keras
from keras import layers
from keras.preprocessing import sequence
from imdb import load_local
from keras.utils import plot_model
import os
os.environ["PATH"] += os.pathsep + 'D:\\Program Files (x86)\\Graphviz2.38\\bin'
max_features = 500
max_len = 500
(x_train, y_train), (x_test, y_test) = load_local(num_words=max_features)
x_train = sequence.pad_sequences(x_train, maxlen=max_len)
x_test = sequence.pad_sequences(x_test, maxlen=max_len)
model = keras.models.Sequential()
model.add(layers.Embedding(max_features, 128, input_length=max_len, name='embed'))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.MaxPooling1D(5))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Dense(1))
model.summary()
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
plot_model(model, show_shapes='True', to_file='model.png')
callbacks = [keras.callbacks.TensorBoard(log_dir='my_log_dir', # 日志写入的位置
histogram_freq=1, # 每一轮之后记录激活
embeddings_freq=1, # 每一轮之后记录嵌入数据