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【深度学习框架Keras】一个单标签多分类的例子
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一、Reuters数据集是一个关于新闻短讯的数据集，其包含了46个互斥的主题，而且每个主题指定包含10个样例。
from keras.datasets import reuters
(train_data,train_labels),(test_data,test_labels) = reuters.load_data(num_words=10000)
二、数据集的相关信息
print('the shape of train data is ',train_data.shape)
print('the shape of test data is ',test_data.shape)
print('an example of train data is ',train_data[10])
the shape of train data is (8982,)
the shape of test data is (2246,)
an example of train data is [1, 245, 273, 207, 156, 53, 74, 160, 26, 14, 46, 296, 26, 39, 74, 2979, 3554, 14, 46, 4689, 4329, 86, 61, 3499, 4795, 14, 61, 451, 4329, 17, 12]
三、处理数据集
import numpy as np
# 神经网络的输入必须是tensor而不是list，所以需要将数据集处理为25000*8000
def vectorize_sequences(sequences,dimension=10000):
results = np.zeros((len(sequences),dimension))
# one-hot编码
for i,sequence in enumerate(sequences):
results[i,sequence] = 1.
return results
x_train = vectorize_sequences(train_data)
x_test = vectorize_sequences(test_data)
将labels通过to_categorical处理为one-hot编码(如果不使用one-hot编码，而是直接使用原始的label，那么loss function应选择sparse_categorical_crossentropy) from keras.utils.np_utils import to_categorical one_hot_train_labels = to_categorical(train_labels) #此时每个标签为一个46维的向量 one_hot_test_labels = to_categorical(test_labels) 四、设计网络结构 from keras import models from keras import layers def build_model(): model = models.Sequential() # 对于46分类的问题，隐层的单元不能小于46，因此选择了64 model.add(layers.Dense(64,activation='relu',input_shape=(10000,))) model.add(layers.Dense(64,activation='relu')) model.add(layers.Dense(46,activation='softmax')) # 使用softmax输出46个主题的概率 model.compile(optimizer='rmsprop', loss='categorical_crossentropy', # 多分类问题选择categorical_crossentropy作为loss function，用来衡量预测分布与真实分布的距离 metrics=['accuracy']) return model model = build_model() 五、划分验证集用于选择超参数epochs x_val = x_train[:1000] partial_x_train = x_train[1000:] y_val = one_hot_train_labels[:1000] partial_y_train = one_hot_train_labels[1000:] 六、训练模型 history = model.fit(partial_x_train, partial_y_train, epochs=20,# 在全数据集上迭代20次 batch_size=512,# 每个batch的大小为512 validation_data=(x_val,y_val)) Train on 7982 samples, validate on 1000 samples Epoch 1/20 7982/7982 [==============================] - 1s 182us/step - loss: 2.5287 - acc: 0.4945 - val_loss: 1.7224 - val_acc: 0.6110 Epoch 2/20 7982/7982 [==============================] - 1s 143us/step - loss: 1.4464 - acc: 0.6885 - val_loss: 1.3466 - val_acc: 0.7090 Epoch 3/20 7982/7982 [==============================] - 1s 142us/step - loss: 1.0951 - acc: 0.7656 - val_loss: 1.1712 - val_acc: 0.7440 Epoch 4/20 7982/7982 [==============================] - 1s 143us/step - loss: 0.8690 - acc: 0.8171 - val_loss: 1.0787 - val_acc: 0.7590 Epoch 5/20 7982/7982 [==============================] - 1s 144us/step - loss: 0.7024 - acc: 0.8480 - val_loss: 0.9848 - val_acc: 0.7840 Epoch 6/20 7982/7982 [==============================] - 1s 146us/step - loss: 0.5654 - acc: 0.8795 - val_loss: 0.9435 - val_acc: 0.8030 Epoch 7/20 7982/7982 [==============================] - 1s 144us/step - loss: 0.4570 - acc: 0.9039 - val_loss: 0.9107 - val_acc: 0.8020 Epoch 8/20 7982/7982 [==============================] - 1s 146us/step - loss: 0.3683 - acc: 0.9237 - val_loss: 0.9341 - val_acc: 0.7920 Epoch 9/20 7982/7982 [==============================] - 1s 144us/step - loss: 0.3019 - acc: 0.9312 - val_loss: 0.8936 - val_acc: 0.8090 Epoch 10/20 7982/7982 [==============================] - 1s 143us/step - loss: 0.2528 - acc: 0.9424 - val_loss: 0.9115 - val_acc: 0.8140 Epoch 11/20 7982/7982 [==============================] - 1s 141us/step - loss: 0.2176 - acc: 0.9476 - val_loss: 0.9180 - val_acc: 0.8110 Epoch 12/20 7982/7982 [==============================] - 1s 143us/step - loss: 0.1867 - acc: 0.9515 - val_loss: 0.9083 - val_acc: 0.8140 Epoch 13/20 7982/7982 [==============================] - 1s 143us/step - loss: 0.1696 - acc: 0.9521 - val_loss: 0.9354 - val_acc: 0.8060 Epoch 14/20 7982/7982 [==============================] - 1s 148us/step - loss: 0.1532 - acc: 0.9557 - val_loss: 0.9683 - val_acc: 0.8040 Epoch 15/20 7982/7982 [==============================] - 1s 158us/step - loss: 0.1387 - acc: 0.9557 - val_loss: 0.9707 - val_acc: 0.8130 Epoch 16/20 7982/7982 [==============================] - 1s 156us/step - loss: 0.1316 - acc: 0.9559 - val_loss: 1.0274 - val_acc: 0.8020 Epoch 17/20 7982/7982 [==============================] - 1s 157us/step - loss: 0.1217 - acc: 0.9577 - val_loss: 1.0270 - val_acc: 0.7970 Epoch 18/20 7982/7982 [==============================] - 1s 155us/step - loss: 0.1201 - acc: 0.9574 - val_loss: 1.0417 - val_acc: 0.8050 Epoch 19/20 7982/7982 [==============================] - 1s 156us/step - loss: 0.1141 - acc: 0.9588 - val_loss: 1.0975 - val_acc: 0.7990 Epoch 20/20 7982/7982 [==============================] - 1s 156us/step - loss: 0.1114 - acc: 0.9594 - val_loss: 1.0732 - val_acc: 0.8010 七、绘制loss和accuracyx轴为epochs，y轴为loss import matplotlib.pyplot as plt %matplotlib inline loss = history.history['loss'] val_loss = history.history['val_loss'] epochs = range(1,len(loss)+1) plt.plot(epochs,loss,'bo',label='Training loss') plt.plot(epochs,val_loss,'b',label='Validation loss') plt.title('Training and validation loss') plt.xlabel('Epochs') plt.ylabel('Loss') plt.legend()x轴为epochs,y轴为accuracy plt.clf() acc = history.history['acc'] val_acc = history.history['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()评估模型在测试集上的loss和accuracy results = model.evaluate(x_test,one_hot_test_labels) results [1.3601689705971831, 0.7800534283700843] 九、生成预测值 predictions = model.predict(x_test) print(predictions[0].shape)# 第0个样本预测值 print(np.sum(predictions[0]))# 所有预测值的和为1 print(np.argmax(predictions[0])) # 46个维度中最大的是第3个维度，即其属于第3个主题 (46,) 0.9999999 3 |
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