使用tensoflow2搭建神经网络进行歌曲分类，使用librosa进行歌曲信号的特征提取

from sklearn.preprocessing import StandardScaler import numpy as np from sklearn.model_selection import train_test_split from model import creat_model import tensorflow as tf import matplotlib.pyplot as plt #导入数据 data=np.load('data.npz')['arr_0'] label=np.load('data.npz')['arr_1'] #归一化 scaler = StandardScaler() X = scaler.fit_transform(np.array(data, dtype=float)) print('数据集的维度',X.shape) #独热编码 label=tf.one_hot(label,10) print('label shape',label.shape) #划分训练集，测试集 X_train, X_test, label_train, label_test = train_test_split(X, np.array(label), test_size=0.2) print('训练集',X_train.shape) print('训练集标签',label_train.shape) #模型的构建 model=creat_model() print('打印模型') model.summary() #编译模型 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) #模型的训练 history=model.fit(X_train,label_train,epochs=500,batch_size=64,validation_data=(X_test,label_test)) acc = history.history['accuracy'] val_acc = history.history['val_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] plt.subplot(1, 2, 1) plt.plot(acc, label='Training Accuracy') plt.plot(val_acc, label='Validation Accuracy') plt.title('Training and Validation Accuracy') plt.legend() plt.subplot(1, 2, 2) plt.plot(loss, label='Training Loss') plt.plot(val_loss, label='Validation Loss') plt.title('Training and Validation Loss') plt.legend() plt.savefig('loss.png') plt.show() #模型的保存 model.save('model.h5') #模型的评估 test_loss,test_acc=model.evaluate(X_test,label_test) print('测试误差',test_loss) print('测试准确率',test_acc)