神经网络不想使用相同的数据集随机学习

如何解决神经网络不想使用相同的数据集随机学习

我已经成功创建了一个监督顺序模型。输入是4维的，具有1维的输出。使用minmaxscaler按比例缩小数据集。我有5个隐藏层，每层有12个神经元。输入，隐藏和输出层的所有内核初始化程序均为he_normal。对于包括输入和输出在内的所有层，激活都是Relu。

我有500个样本，批量大小为128个数据。

有时损失函数（MSE）并没有改善，请参见下文：

Epoch 1/2000
14/14 [==============================] - 0s 10ms/step - loss: 0.6348 - mape: 77.9929 - mse: 0.6348 - mae: 0.7799 - val_mse: 0.5862 - val_mae: 0.7431 - val_mape: 74.3140 - val_loss: 0.5862
Epoch 2/2000
14/14 [==============================] - 0s 2ms/step - loss: 0.6348 - mape: 77.9929 - mse: 0.6348 - mae: 0.7799 - val_mse: 0.5862 - val_mae: 0.7431 - val_mape: 74.3140 - val_loss: 0.5862
Epoch 3/2000
14/14 [==============================] - 0s 3ms/step - loss: 0.6348 - mape: 77.9929 - mse: 0.6348 - mae: 0.7799 - val_mse: 0.5862 - val_mae: 0.7431 - val_mape: 74.3140 - val_loss: 0.5862
Epoch 4/2000
14/14 [==============================] - 0s 2ms/step - loss: 0.6348 - mape: 77.9929 - mse: 0.6348 - mae: 0.7799 - val_mse: 0.5862 - val_mae: 0.7431 - val_mape: 74.3140 - val_loss: 0.5862
Epoch 5/2000
14/14 [==============================] - 0s 2ms/step - loss: 0.6348 - mape: 77.9929 - mse: 0.6348 - mae: 0.7799 - val_mse: 0.5862 - val_mae: 0.7431 - val_mape: 74.3140 - val_loss: 0.5862
Epoch 6/2000
14/14 [==============================] - 0s 3ms/step - loss: 0.6348 - mape: 77.9929 - mse: 0.6348 - mae: 0.7799 - val_mse: 0.5862 - val_mae: 0.7431 - val_mape: 74.3140 - val_loss: 0.5862
Epoch 7/2000
14/14 [==============================] - 0s 3ms/step - loss: 0.6348 - mape: 77.9929 - mse: 0.6348 - mae: 0.7799 - val_mse: 0.5862 - val_mae: 0.7431 - val_mape: 74.3140 - val_loss: 0.5862
Epoch 8/2000
14/14 [==============================] - 0s 2ms/step - loss: 0.6348 - mape: 77.9929 - mse: 0.6348 - mae: 0.7799 - val_mse: 0.5862 - val_mae: 0.7431 - val_mape: 74.3140 - val_loss: 0.5862

然后停止运行（ctrl+z）并再次重新执行该命令，它变为：

Epoch 1/2000
14/14 [==============================] - 0s 10ms/step - loss: 0.2896 - mae: 0.4896 - mse: 0.2896 - mape: 48.9583 - val_mape: 49.4280 - val_loss: 0.2901 - val_mse: 0.2901 - val_mae: 0.4943
Epoch 2/2000
14/14 [==============================] - 0s 2ms/step - loss: 0.2425 - mae: 0.4380 - mse: 0.2425 - mape: 43.8044 - val_mape: 43.7562 - val_loss: 0.2393 - val_mse: 0.2393 - val_mae: 0.4376
Epoch 3/2000
14/14 [==============================] - 0s 2ms/step - loss: 0.2016 - mae: 0.3890 - mse: 0.2016 - mape: 38.9016 - val_mape: 38.2766 - val_loss: 0.1962 - val_mse: 0.1962 - val_mae: 0.3828
Epoch 4/2000
14/14 [==============================] - 0s 2ms/step - loss: 0.1680 - mae: 0.3478 - mse: 0.1680 - mape: 34.7794 - val_mape: 33.7354 - val_loss: 0.1616 - val_mse: 0.1616 - val_mae: 0.3374
Epoch 5/2000
14/14 [==============================] - 0s 2ms/step - loss: 0.1413 - mae: 0.3156 - mse: 0.1413 - mape: 31.5616 - val_mape: 30.1136 - val_loss: 0.1346 - val_mse: 0.1346 - val_mae: 0.3011
Epoch 6/2000
14/14 [==============================] - 0s 3ms/step - loss: 0.1207 - mae: 0.2890 - mse: 0.1207 - mape: 28.9029 - val_mape: 27.5114 - val_loss: 0.1143 - val_mse: 0.1143 - val_mae: 0.2751
Epoch 7/2000
14/14 [==============================] - 0s 2ms/step - loss: 0.1061 - mae: 0.2693 - mse: 0.1061 - mape: 26.9261 - val_mape: 25.3930 - val_loss: 0.0994 - val_mse: 0.0994 - val_mae: 0.2539
Epoch 8/2000
14/14 [==============================] - 0s 2ms/step - loss: 0.0957 - mae: 0.2548 - mse: 0.0957 - mape: 25.4783 - val_mape: 23.8949 - val_loss: 0.0892 - val_mse: 0.0892 - val_mae: 0.2389

我想知道，第一次跑步发生了什么？为什么使用相同的数据集，该模型在第二轮运行时起作用而在第一轮运行时却不起作用？有什么方法可以通过编程来确保在每次执行脚本时模型都可以学习？我想将模型包含在自适应采样算法中，该算法一次执行一次以上训练阶段。

任何帮助将不胜感激！

干杯， PG

PS：以下是代码段，数据示例，网络体系结构

数据样本（4个输入[Q_in，Tamb，T_in，H_drop] 1个输出[eff_rcv]）

Q_in,Tamb,T_in,H_drop,eff_rcv
609496059.800000,271.792985,807.218964,35.445493,0.870245
783459291.300000,314.275101,828.283384,31.161965,0.923094
391056216.100000,307.686423,816.411201,39.310067,0.748878
289120690.100000,292.437028,828.729067,29.114747,0.812813
508971245.000000,284.898844,812.819974,38.611096,0.817931
.
.

代码段

#split the raw into input (X) and ouput(y)
X_raw = df[df.columns[0:inputdim]].to_numpy()

#Convert to 2D array
y_raw = df[df.columns[-1]].to_numpy()
y_raw = y_raw.reshape(-1,1)

#Import the scaler
mm = MinMaxScaler()

#Scaling the data
X_scaled = mm.fit_transform(X_raw)
y_scaled = mm.fit_transform(y_raw)

#Split into train test - 85% training 15% testing
Xtrain,Xtest,ytrain,ytest = train_test_split(X_scaled,y_scaled,test_size=0.15)

######################  BUILD MODEL ############################ 
inputdim = 4
outputdim = 1

#Number of neurons in each hidden layer < 2*input_dim
num_neurons = 3*inputdim

#Neural network architecture
network_layout = []
for i in range(5):
    network_layout.append(num_neurons)
#Building the neural network#
model = Sequential()

#Adding input layer and first hidden layer
model.add(Dense(network_layout[0],name = "Input",input_dim=inputdim,kernel_initializer=initializers.RandomNormal(),bias_initializer=initializers.Zeros(),use_bias=True,activation=activation))

#Adding the rest of hidden layer
for numneurons in network_layout[1:]:
        model.add(Dense(numneurons,activation=activation))

#Adding the output layer
model.add(Dense(outputdim,name="Output",activation="relu"))

backend.set_epsilon(1)

#Compiling the model
model.compile(optimizer=opt,loss='mse',metrics=['mse','mae','mape'])
model.summary()

#Training the model

history = model.fit(x=Xtrain,y=ytrain,validation_data=(Xtest,ytest),batch_size=batch_size,epochs=epochs)

解决方法

您可以在训练模型之前设置随机种子。这样，您可以确保每次脚本运行的结果都是相同的。

tf.keras.backend.clear_session()
tf.random.set_seed(1)

关于为什么它在最初的摘要中没有得到改善：您有可能遇到无法摆脱的局部最小值。