When my friend was working on an Age and Gender Detection model using Convolutional Neural Network, he faced this ValueError. He has given the ‘metrics’ argument as a list during model defining, but it said it should have as many entries as the model have. My model has two outputs ‘age’ and ‘gender’.

He was defining his CNN model and when he tried to fit the model on training data it showed an error saying

ValueError: For a model with multiple outputs, when providing the metrics argument as a list, it should have as many entries as the model has outputs. Received:
metrics=[‘accuracy’]
of length 1 whereas the model has 2 outputs.

This is my full error:

ValueError                                Traceback (most recent call last)
Cell In[47], line 1
----> 1 History=Model.fit(X_train,Y_train_2,batch_size=64,validation_data=(X_test,Y_test_2),epochs=10,callbacks=callback_list)

File c:Usersmebub_9a7jdi8DesktopAge_Gender_Detection_Model.envLibsite-packageskerassrcutilstraceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs)
    119     filtered_tb = _process_traceback_frames(e.__traceback__)
    120     # To get the full stack trace, call:
    121     # `keras.config.disable_traceback_filtering()`
--> 122     raise e.with_traceback(filtered_tb) from None
    123 finally:
    124     del filtered_tb

File c:Usersmebub_9a7jdi8DesktopAge_Gender_Detection_Model.envLibsite-packageskerassrctrainerscompile_utils.py:250, in CompileMetrics._build_metrics_set(self, metrics, num_outputs, output_names, y_true, y_pred, argument_name)
    248 if isinstance(metrics, (list, tuple)):
    249     if len(metrics) != len(y_pred):
--> 250         raise ValueError(
    251             "For a model with multiple outputs, "
    252             f"when providing the `{argument_name}` argument as a "
    253             "list, it should have as many entries as the model has "
    254             f"outputs. Received:n{argument_name}={metrics}nof "
    255             f"length {len(metrics)} whereas the model has "
    256             f"{len(y_pred)} outputs."
    257         )
    258     for idx, (mls, yt, yp) in enumerate(
...
    261         if not isinstance(mls, list):

ValueError: For a model with multiple outputs, when providing the `metrics` argument as a list, it should have as many entries as the model has outputs. Received:
metrics=['accuracy']
of length 1 whereas the model has 2 outputs. 

This is my code for defining model:

def model(input_shape):
    inputs=Input((input_shape))
    conv_1=Convolution(inputs,32)
    maxp_1=MaxPooling2D(pool_size=(2,2))(conv_1)
    conv_2=Convolution(maxp_1,64)
    maxp_2=MaxPooling2D(pool_size=(2,2))(conv_2)
    conv_3=Convolution(maxp_2,128)
    maxp_3=MaxPooling2D(pool_size=(2,2))(conv_3)
    conv_4=Convolution(maxp_3,256)
    maxp_4=MaxPooling2D(pool_size=(2,2))(conv_4)
    flatten= Flatten()(maxp_4)
    dense_1=Dense(64,activation='relu')(flatten)
    dense_2=Dense(64,activation='relu')(flatten)
    drop_1=Dropout(0.2)(dense_1)
    drop_2=Dropout(0.2)(dense_2)
    output_1=Dense(1,activation='sigmoid',name='sex_out')(drop_1)
    output_2=Dense(1,activation='relu',name='age_out')(drop_2)
    model=Model(inputs=[inputs],outputs=[output_1,output_2])
    model.compile(loss=["binary_crossentropy","mae"],optimizer="Adam",metrics=["accuracy"])
    return model