电影评论二分类.py

# 加载数据集 （保留训练数据中前 10 000 个最常出现的单词）
from keras.datasets import imdb
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)

# 将整数序列编码为二进制矩阵
import numpy as np
def vectorize_sequences(sequences, dimension=10000):
 results = np.zeros((len(sequences), dimension))  # 创建一个形状为(len(sequences), dimension) 的零矩阵
 for i, sequence in enumerate(sequences):
   results[i, sequence] = 1. #将 results[i] 的指定索引设为 1
 return results
x_train = vectorize_sequences(train_data)    # 将训练数据向量化
x_test = vectorize_sequences(test_data)     # 将测试数据向量化

# 标签向量化
y_train = np.asarray(train_labels).astype('float32')
y_test = np.asarray(test_labels).astype('float32')

# 构建网络，模型定义
from keras import models
from keras import layers
model = models.Sequential()
model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(16, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

# 编译模型
from keras import optimizers
model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['accuracy'])
### optimizer参数：优化器 loss：损失函数 metrics：模型性能标准

###或者使用自定义损失和指标
## from keras import losses
## from keras import metrics
## model.compile(optimizer=optimizers.RMSprop(lr=0.001),
## loss=losses.binary_crossentropy,
## metrics=[metrics.binary_accuracy])


# 验证方法
x_val = x_train[:10000]
partial_x_train = x_train[10000:]
y_val = y_train[:10000]
partial_y_train = y_train[10000:]

# 训练模型
history = model.fit(partial_x_train,partial_y_train,epochs=20,batch_size=512,validation_data=(x_val, y_val))
## epochs为整体循环次数 batch_size是每组 512个小样本 验证数据集 validation_data

# 绘制训练损失和测试损失
import matplotlib.pyplot as plt
history_dict = history.history
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']
epochs = range(1, len(loss_values) + 1)
plt.plot(epochs, loss_values, 'bo', label='Training loss')
plt.plot(epochs, val_loss_values, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()

#绘制训练精度与测试精度
plt.clf()
acc = history_dict['acc']
val_acc = history_dict['val_acc']
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()