what causes the model can not predict using other data - regression

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?