Models passed to `fit` can only have `training` and the first argument in `call` as positional arguments, found

Question

I am trying to follow this code but on another data set:https://www.tensorflow.org/tutorials/text/transformer#encoder_layer I needed to compile and fit the model. however, I get this error while running; I don't know what it means:

 Models passed to `fit` can only have `training` and the first argument in `call` as positional arguments, found: ['tar', 'enc_padding_mask', 'look_ahead_mask', 'dec_padding_mask'].

Here it is the model:

class Transformer(tf.keras.Model):
  def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, 
               target_vocab_size, pe_input, pe_target, rate=0.1,**kwargs,):
    super(Transformer, self).__init__(**kwargs)

    self.encoder = Encoder(num_layers, d_model, num_heads, dff, 
                           input_vocab_size, pe_input, rate)

    self.decoder = Decoder(num_layers, d_model, num_heads, dff, 
                           target_vocab_size, pe_target, rate)

    self.final_layer = tf.keras.layers.Dense(target_vocab_size)
  def get_config(self):

        config = super().get_config().copy()
        config.update({
            'dff':self.dff,
            'input_vocab_size':self.input_vocab_size,
            'target_vocab_size':self.target_vocab_size,
            'pe_input':self.pe_input,
            'pe_target':self.pe_target,
            #'vocab_size': self.vocab_size,
            'num_layers': self.num_layers,
            #'units': self.units,
            'd_model': self.d_model,
            'num_heads': self.num_heads,
            'rate': self.rate,
        })
        return config

  def call(self, inp, tar, training, enc_padding_mask, 
           look_ahead_mask, dec_padding_mask):

    enc_output = self.encoder(inp, training, enc_padding_mask)  # (batch_size, inp_seq_len, d_model)

    # dec_output.shape == (batch_size, tar_seq_len, d_model)
    dec_output, attention_weights = self.decoder(
        tar, enc_output, training, look_ahead_mask, dec_padding_mask)

    final_output = self.final_layer(dec_output)  # (batch_size, tar_seq_len, target_vocab_size)
    #    return final_output, attention_weights


    return tf.keras.Model(inputs=[inputs, dec_inputs], outputs=outputs, name=name)

and creating the model, compiling it, and fitting it as follows:

transformer = Transformer(num_layers, d_model, num_heads, dff,
                          input_vocab_size, target_vocab_size, 
                          pe_input=input_vocab_size, 
                          pe_target=target_vocab_size,
                          rate=dropout_rate)

transformer.compile(optimizer=optimizer, loss=loss_function, metrics=[accuracy])

transformer.fit(dataset, epochs=EPOCHS)

EDIT: Bases on @Geeocode updated the def function in transformer class to be:

def call(self, inp, tar, enc_padding_mask,look_ahead_mask, dec_padding_mask, training=False,):

    enc_output = self.encoder(inp, training, enc_padding_mask)  # (batch_size, inp_seq_len, d_model)

    # dec_output.shape == (batch_size, tar_seq_len, d_model)
    dec_output, attention_weights = self.decoder(
        tar, enc_output, training, look_ahead_mask, dec_padding_mask)

    final_output = self.final_layer(dec_output)  # (batch_size, tar_seq_len, target_vocab_size)
    return final_output, attention_weights

However, I still get the same error

Answer 1

The reason why you got the error is because self.call only takes two variables an input and a training flag. If you have multiple input variables, they are passed as a tuple. Therefore, you can have a function definition similar to the following:

def call(self, input, training):
  inp, tar, enc_padding_mask,look_ahead_mask, dec_padding_mask = input
  ...