我已经创建了一个模型来从语音样本中预测情感，该模型是根据以下代码创建的：

总共有8种情感：中性、平静、快乐、悲伤、愤怒、厌恶、惊讶

我首先提取了每个语音样本的特征，并将它们放入数据框中，然后逐个加载到X中（标签加载到Y中），然后按如下方式分割数据：

x_train, x_test, y_train, y_test = train_test_split(X, Y, random_state=0, shuffle=True)scaler = StandardScaler()x_train = scaler.fit_transform(x_train)x_test = scaler.transform(x_test)x_train = np.expand_dims(x_train, axis=2)x_test = np.expand_dims(x_test, axis=2)model=Sequential()model.add(Conv1D(256, kernel_size=5, strides=1, padding='same', activation='relu', input_shape=(x_train.shape[1], 1)))model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))model.add(Conv1D(256, kernel_size=5, strides=1, padding='same', activation='relu'))model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))model.add(Conv1D(128, kernel_size=5, strides=1, padding='same', activation='relu'))model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))model.add(Dropout(0.2))model.add(Conv1D(64, kernel_size=5, strides=1, padding='same', activation='relu'))model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))model.add(Flatten())model.add(Dense(units=32, activation='relu'))model.add(Dropout(0.3))model.add(Dense(units=8, activation='softmax'))model.compile(optimizer = 'adam' , loss = 'categorical_crossentropy' , metrics = ['accuracy'])model.summary()rlrp = ReduceLROnPlateau(monitor='loss', factor=0.4, verbose=0, patience=2, min_lr=0.0000001)history=model.fit(x_train, y_train, batch_size=64, epochs=75, validation_data=(x_test, y_test), callbacks=[rlrp])

总体准确率达到了89%

现在我想使用一个新的数据集进行预测。我需要做些什么？

回答：

如果new_data_x_test和new_data_y_true是你的新数据集，那么在训练模型后，你只需按照如下步骤操作：

scaler = StandardScaler()new_data_x_test = scaler.transform(new_data_x_test )new_data_x_test= np.expand_dims(new_data_x_test, axis=2)model.load_weight(h5)new_data_y_pred = model.predict(new_data_x_test )

重要的是，你应该根据模型的要求对数据进行转换。接下来，使用适当的评估指标在new_data_y_true和new_data_y_pred上进行评估。