what causes the model can not predict using other data

Question

I am here using ResNet50 to create a regression model. I ran into a problem when I wanted to test a model using other data. The length of the dataset is 2050. Then I separate it into training and testing data. I divide it by 1500 as training data and 500 as test data. At the time of the training process, I had good results and was able to predict quite accurately. but when I want to test it using testing data, the prediction results are bad.

below is the model loss result

the code :

Insole = pd.read_csv('1119_Rwalk40s1_list.txt', header=None, low_memory=False)
SIData =  np.asarray(Insole)

df = pd.read_csv('1119_Rwalk40s1.csv', low_memory=False)
columns = ['Fx','Fy','Fz','Mx','My','Mz']
selected_df = df[columns]
FCDatas = selected_df[:2050]

SmartInsole = np.array(SIData)
FCData = np.array(FCDatas)

xX = SmartInsole
yY = FCData

scaler_x = MinMaxScaler(feature_range=(0, 1))
scaler_x.fit(xX)
xscale = scaler_x.transform(xX)

scaler_y = MinMaxScaler(feature_range=(0, 1))
scaler_y.fit(yY)
yscale = scaler_y.transform(yY)

SIDataPCA = xscale
pca = PCA(n_components=12)
pca.fit(SIDataPCA)
SIdata_pca = pca.transform(SIDataPCA)

#For Training
trainX = SIdata_pca[:1500]
trainY = yscale[:1500]

#For Testing
testX = SIdata_pca[1500]
testY = yscale[1500:]

X_train, X_test, y_train, y_test = train_test_split(trainX, trainY, test_size=0.20, random_state=2)

Below is the my resnet model structure:

Below is identity blok:

def identity_block(input_tensor,units):

x = layers.Dense(units)(input_tensor)
x = layers.Activation('relu')(x)

x = layers.Dense(units)(x)
x = layers.Activation('relu')(x)

x = layers.Dense(units)(x)

x = layers.add([x, input_tensor])
x = layers.Activation('relu')(x)

return x

Below is dens_block:

def dens_block(input_tensor,units):

x = layers.Dense(units)(input_tensor)
x = layers.Activation('relu')(x)

x = layers.Dense(units)(x)
x = layers.Activation('relu')(x)

x = layers.Dense(units)(x)

shortcut = layers.Dense(units)(input_tensor)

x = layers.add([x, shortcut])
x = layers.Activation('relu')(x)
return x

Resnet50 model:

def ResNet50Regression():
Res_input = layers.Input(shape=(12,))
width = 32

x = dens_block(Res_input,width)
x = identity_block(x,width)
x = identity_block(x,width)

x = dens_block(x,width)
x = identity_block(x,width)
x = identity_block(x,width)

x = dens_block(x,width)
x = identity_block(x,width)
x = identity_block(x,width)

x = layers.Dense(6,activation="sigmoid")(x)
model = models.Model(inputs=Res_input, outputs=x)

return model

model = ResNet50Regression()

model.compile(loss='mse', 
              optimizer=Adam(), 
                            metrics=['mse'])

history = model.fit(X_train, y_train, 
                    batch_size=32, 
                                        epochs=50, 
                                        validation_data=(X_test, y_test), 
                                        verbose=2)

model.save('Resnet50-1203.h5')
ypred = model.predict(trainX)


x=[]
colors=['red','green','brown','teal','gray','black','maroon','orange','purple']
colors2=['green','red','orange','black','maroon','teal','blue','gray','brown']
x = np.arange(0,1500)*40/1500 
for i in range(0,6):
    plt.figure(figsize=(15,6))
    plt.plot(x,trainY[0:1500,i],color=colors[i])
    plt.plot(x,ypred[0:1500,i], markerfacecolor='none',color=colors2[i])
    plt.title('Result for ResNet Regression (Training Data)')
    plt.ylabel(columns[i])
    plt.xlabel('Time(s)')
    plt.legend(['FP Data', 'SI Prediction'], loc='best')
    # plt.savefig('Regression Result.png'[i])
    plt.show()

Testing Model using other data code:

new_model = load_model('Resnet50-1203.h5')
model.evaluate(testX, testY)
Test_xX_model = new_model.predict(testX)
x=[]
colors=['red','green','brown','teal','gray','black','maroon','orange','purple']
colors2=['green','red','orange','black','maroon','teal','blue','gray','brown']
x = np.arange(0,550)*40/550 
for i in range(0,6):
    plt.figure(figsize=(15,6))
    plt.plot(x,testY[0:550,i],color=colors[i])
    plt.plot(x,Test_xX_model[0:550,i], markerfacecolor='none',color=colors2[i])
    plt.title('Result for ResNet Regression (Testing Data)')
    plt.ylabel(columns[i])
    plt.xlabel('Time(s)')
    plt.legend(['FP Data', 'SI Prediction'], loc='best')
    # plt.savefig('Regression Result.png'[i])
    plt.show()

1 of traning data predictions results:

1 of testing data predictions results:

what should i do for this case?