AssertionError: Could not compute output Tensor

Question

I am trying to build a model that takes multiple inputs and multiple outputs using a functional API. I followed this to create the code.

def create_model_mul         


        

Answer 1

you have to provide validation_data in the correct format (like your train). you have to pass 2 input data and 2 targets... you are passing only one

this is a dummy example

def create_model_multiple():

    input1 = tf.keras.Input(shape=(13,), name = 'I1')
    input2 = tf.keras.Input(shape=(6,), name = 'I2')
    hidden1 = tf.keras.layers.Dense(units = 4, activation='relu')(input1)
    hidden2 = tf.keras.layers.Dense(units = 4, activation='relu')(input2)
    merge = tf.keras.layers.concatenate([hidden1, hidden2])
    hidden3 = tf.keras.layers.Dense(units = 3, activation='relu')(merge)
    output1 = tf.keras.layers.Dense(units = 2, activation='softmax', name ='O1')(hidden3)
    output2 = tf.keras.layers.Dense(units = 2, activation='softmax', name = 'O2')(hidden3)
    model = tf.keras.models.Model(inputs = [input1,input2], outputs = [output1,output2])
    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])
    return model


x1 = np.random.uniform(0,1, (190,13))
x2 = np.random.uniform(0,1, (190,6))
val_x1 = np.random.uniform(0,1, (50,13))
val_x2 = np.random.uniform(0,1, (50,6))

y1 = np.random.randint(0,2, 190)
y2 = np.random.randint(0,2, 190)
val_y1 = np.random.randint(0,2, 50)
val_y2 = np.random.randint(0,2, 50)


model = create_model_multiple()

history = model.fit({'I1':x1, 'I2':x2},
                    {'O1':y1, 'O2': y2},
                    validation_data=([val_x1,val_x2], [val_y1,val_y2]), # <=========
                    epochs=100,
                    verbose = 1)