给我卷积神经网络文本情感三分类的代码,除了分训练集和测试集输出报告,还要输出他们的准确率
时间: 2024-02-28 14:52:39 浏览: 18
好的,以下是一个基于Keras的卷积神经网络文本情感三分类的代码示例:
```python
import numpy as np
from keras.layers import Dense, Input, Conv1D, MaxPooling1D, Flatten, Dropout
from keras.models import Model
from keras.utils import to_categorical
from sklearn.metrics import classification_report
# 加载数据集
x_train = np.load('x_train.npy')
y_train = np.load('y_train.npy')
x_test = np.load('x_test.npy')
y_test = np.load('y_test.npy')
# 将标签转换为独热编码
y_train = to_categorical(y_train, num_classes=3)
y_test = to_categorical(y_test, num_classes=3)
# 定义模型结构
inputs = Input(shape=(x_train.shape[1],), dtype='float32')
embedding = inputs
conv1 = Conv1D(filters=128, kernel_size=3, activation='relu')(embedding)
pool1 = MaxPooling1D(pool_size=2)(conv1)
conv2 = Conv1D(filters=64, kernel_size=3, activation='relu')(pool1)
pool2 = MaxPooling1D(pool_size=2)(conv2)
flatten = Flatten()(pool2)
dense1 = Dense(64, activation='relu')(flatten)
dropout1 = Dropout(0.5)(dense1)
dense2 = Dense(32, activation='relu')(dropout1)
dropout2 = Dropout(0.5)(dense2)
outputs = Dense(3, activation='softmax')(dropout2)
model = Model(inputs=inputs, outputs=outputs)
# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# 训练模型
model.fit(x_train, y_train, batch_size=64, epochs=10, validation_data=(x_test, y_test))
# 在测试集上评估模型
y_pred = np.argmax(model.predict(x_test), axis=1)
y_true = np.argmax(y_test, axis=1)
print(classification_report(y_true, y_pred))
# 输出准确率
accuracy = np.sum(y_pred == y_true) / len(y_true)
print("Accuracy:", accuracy)
```
需要注意的是,这里的数据集需要自己准备并加载,且需要将标签转换为独热编码。在训练模型时,我们使用了Adam优化器和交叉熵损失函数,评估指标为准确率。在测试集上评估模型时,我们使用sklearn库中的classification_report函数输出分类报告,同时计算并输出准确率。
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