ValueError: `labels.shape` must equal `logits.shape` except for the last dimension. Received: labels.shape=(880,) and logits.shape=(16, 3)

Question

This is my multiclass neural network model.

# We declared a function for creating a model.
def build_model1_two_hidden_layers():
    model = Sequential()
    
    # Input layer => input_shape must be explicitly designated
    model.add(Dense(16, input_shape = (normed_train_data.shape[1],)))
    
    # Output layer => output dimension = 1 since it is a regression problem
    model.add(Dense(3, activation='softmax'))

    # Activation: sigmoid, softmax, tanh, relu, LeakyReLU. 
    # Optimizer: SGD, Adam, RMSProp, etc.
    # https://www.tensorflow.org/api_docs/python/tf/keras/optimizers
    learning_rate = 0.0001
    optimizer1 = optimizers.Adam(learning_rate)
    model.compile(
        optimizer=optimizer1,
        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
        metrics=['accuracy']
    )
    return model

Currently, I am not adding any hidden layer , just want to test the model withou hidden layers first.However , I have an assumption that the model recognize my output layer as hidden layer.

This is my sample data input.

SEX AGE        MENTAL DIS   HISTORY     SUIC        HISTORY     LIVING      ECONOMIC    SUIC RISK   ANXIETY STATE   ANXIETY TRAIT
0.510342       2.133002                 -0.605015   -0.645996   0.417973    0.429160    -1.533806   -1.690477      -1.867256

This is my error

ValueError: labels.shape must equal logits.shape except for the last dimension. Received: labels.shape=(880,) and logits.shape=(16, 3)

I think 880 is my size of dataset and 16 is the perceptrons for the input layer and 3 is the perceptron for output layer. How to rectify this error?