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Reputation: 51

python ValueError: Error when checking target: expected dense_2 to have shape (12,) but got array with shape (1,)

writing a program, use keras to build BP neural networ to predict data(regression),the program is as follows:

bp_dataset = pd.read_csv('Dataset/allGlassStraightThroughTube.csv')
bp_tube_par = bp_dataset.iloc[:, 3:8]
bp_tube_eff = bp_dataset.iloc[:, -1:]


bp_tube_par_X_train,bp_tube_par_X_test,bp_tube_eff_Y_train,bp_tube_eff_Y_test = train_test_split(bp_tube_par,
                                                                                                 bp_tube_eff,
                                                                                                 random_state=33,
                                                                                                 test_size=0.3)

# normalize the train and test Dataset
sc_X = StandardScaler()
sc_Y = StandardScaler()
sc_bp_tube_par_X_train = sc_X.fit_transform(bp_tube_par_X_train)
sc_bp_tube_par_X_test = sc_X.transform(bp_tube_par_X_test)
sc_bp_tube_eff_Y_train = sc_Y.fit_transform(bp_tube_eff_Y_train)
sc_bp_tube_eff_Y_test = sc_Y.transform(bp_tube_eff_Y_test)

# build BP neural network
model = Sequential()
model.add(Dense(12, input_dim=5, activation='relu'))
model.add(Dense(12, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy', 'mae'])
model.fit(sc_bp_tube_par_X_train, sc_bp_tube_eff_Y_train, epochs=100)
pre_sc_bp_tube_eff_Y_test = model.predict(sc_bp_tube_par_X_test)

but it errors:

Traceback (most recent call last):
  File "C:/Users/win/PycharmProjects/allGlassStraightThroughTube/bpTest.py", line 44, in <module>
model.fit(sc_bp_tube_par_X_train, sc_bp_tube_eff_Y_train, epochs=100)
  ...
  ValueError: Error when checking target: expected dense_2 to have shape (12,) but got array with shape (1,)

could you please tell me the reason and how to correct it

Upvotes: 0

Views: 78

Answers (1)

Suhas Shastry
Suhas Shastry

Reputation: 76

model.add(Dense(12, activation='linear'))

12 here represents output dimension. In your case 12 is input dimension to second layer. Keras handle input dimensions for middle layers and you dont have to mention it explicitly.

Your code should be

model.add(Dense(1, activation='linear'))

Upvotes: 1

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