This is the error I get on the first epoch when calling model.fit:

Exception has occurred: ValueError
Layer "functional" expects 2 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor 'data:0' shape=(None, 128) dtype=float32>]
  File "D:workspaceMachine Learning 545PSU_classescs445_group_projectcodeKeras Music Genres Classificationencoder_decoder_feature_extractor.py", line 177, in train_encoder_decoder_model
    model.fit(x = X_train,
  File "D:workspaceMachine Learning 545PSU_classescs445_group_projectcodeKeras Music Genres Classificationencoder_decoder_feature_extractor.py", line 217, in <module>
    trained_model = train_encoder_decoder_model(encoder_decoder_model, X_train, y_train, X_test, y_test)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Layer "functional" expects 2 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor 'data:0' shape=(None, 128) dtype=float32>]"

this is my model:

def define_encoder_decoder_model(num_features):
    # Define the encoder
    encoder_inputs = Input(shape=(None, num_features))
    encoder_hidden1 = Dense(100, activation='relu')(encoder_inputs)
    encoder_hidden2 = Dense(50, activation='relu')(encoder_hidden1)
    encoder_lstm = LSTM(25, return_state=True)
    encoder_outputs, state_h, state_c = encoder_lstm(encoder_hidden2)
    encoder_states = [state_h, state_c]

    # Define the decoder
    decoder_inputs = Input(shape=(None, 25))
    decoder_hidden1 = Dense(50, activation='relu')(decoder_inputs)
    decoder_hidden2 = Dense(100, activation='relu')(decoder_hidden1)
    decoder_lstm = LSTM(num_features, return_sequences=True, return_state=True)
    decoder_outputs, _, _ = decoder_lstm(decoder_hidden2, initial_state=encoder_states)
    decoder_dense = Dense(num_features, activation='softmax')
    decoder_outputs = decoder_dense(decoder_outputs)

    # Define the model that will turn encoder_inputs and decoder_inputs into decoder_outputs
    model = Model([encoder_inputs, decoder_inputs], decoder_outputs)

    # Compile the model
    model.compile(optimizer='adam', loss='categorical_crossentropy', 
                  metrics=['accuracy', 'precision', 'recall', 'f1_score'])

    # Summary of the model
    model.summary()

    return modeltype here

this is how I’m calling model.fit:

def train_encoder_decoder_model(model, X_train, y_train, X_test, y_test):
    """
    Trains an encoder-decoder model using the provided data.

    Args:
        model: The encoder-decoder model to train.
        X_train: The input training data.
        y_train: The target training data.
        X_test: The input test data.
        y_test: The target test data.

    Returns:
        The trained encoder-decoder model.
    """
    print("shapes: X_train:", np.shape(X_train)," y_train: ", np.shape(y_train)," X_test: ", np.shape(X_test)," y_test: ", np.shape(y_test))
    # Train the model
    # append to a file the training log for each epoch
    file_logger = FileLogger('training.log')
    y_train_T = tf.convert_to_tensor(np.array([y_train]).T)
    y_test_T = tf.convert_to_tensor(np.array([y_test]).T)
    #x_train = tf.convert_to_tensor(X_train)
    y_train = X_train
    y_test = X_test
    model.fit(x = X_train,
              y= y_train,
              batch_size=100,
              epochs=100,
              verbose=2,
              validation_data=(X_test, y_test),
              callbacks=[file_logger])
    return model

This is an encoder-decoder model so the X_train dataset is equal to the y_train, same for X_test, y_test. In this first case the shape of the training set is (799,128) and the test data set is (299,128). The features are represented as “float64” values.

I’m running the code under visual studio code. I pre process the data into a normalized and scaled dataset and then divide it into training dataset and test datasets, build my encoder-decoder model (see method above), and then try to train the model. What I get is this output from the model.fit “Epoch 1/100” and the error message shown above.

What is this error and how to resolve it?