如何从新数据集进行预测？

Question

我创建了一个模型来根据语音样本预测情绪，该模型由以下代码组成：

共有8种情绪： 中立、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帧中，然后加载 它们一一对应于 X 和（标签对应于 Y）然后拆分数据，如下所示：

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])

总准确率为 89%

现在我想用一个新的数据集进行预测。我需要做什么？

Answer 1

如果new_data_x_test和new_data_y_true是你的新数据集，那么训练模型后你需要做的就是：

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 )

问题是，您应该根据模型要求对其进行改造。接下来，使用适当的评估指标对 new_data_y_true 和 new_data_y_pred 进行评估。