How do I resolve "IndexError: tuple index out of range"?

TensorFlow General Discussion
,
1

I am trying to do a time series plot forecast in transformer.
The input size is (None, 30).
However, an error occurs here.

x = layers.MultiHeadAttention(
      5 key_dim=1, num_heads=1, dropout=dropout
----> 6 )(inputs, inputs)
      7 x = layers.Dropout(dropout)(x)
      8 x = layers.LayerNormalization(epsilon=1e-6)(x)

An error occurs here.
IndexError: tuple index out of range

def transformer_encoder(inputs, head_size, num_heads, ff_dim, dropout=0):
    # Attention and Normalization
    print(inputs.shape)
    x = layers.MultiHeadAttention(
        key_dim=head_size, num_heads=num_heads, dropout=dropout
    )(inputs, inputs)
    x = layers.Dropout(dropout)(x)
    x = layers.LayerNormalization(epsilon=1e-6)(x)
    res = x + inputs
​
    # Feed Forward Part
    x = layers.Conv1D(filters=ff_dim, kernel_size=1, activation="relu")(res)
    x = layers.Dropout(dropout)(x)
    x = layers.Conv1D(filters=inputs.shape[-1], kernel_size=1)(x)
    x = layers.LayerNormalization(epsilon=1e-6)(x)
    return x + res

def build_model(
    input_shape,
    head_size,
    num_heads,
    ff_dim,
    num_transformer_blocks,
    mlp_units,
    dropout=0,
    mlp_dropout=0,
):
    inputs = keras.Input(shape=input_shape)
    x = inputs
    for _ in range(num_transformer_blocks):
        x = transformer_encoder(x, head_size, num_heads, ff_dim, dropout)

    x = layers.GlobalAveragePooling1D(data_format="channels_first")(x)
    for dim in mlp_units:
        x = layers.Dense(dim, activation="relu")(x)
        x = layers.Dropout(mlp_dropout)(x)
    outputs = layers.Dense(n_classes)(x)
    return keras.Model(inputs, outputs)

from tensorflow import keras
from tensorflow.keras import layers
input_shape = X_train.shape[1:]
model_mlp = build_model(
    input_shape,
    head_size=256,
    num_heads=1,
    ff_dim=1,
    num_transformer_blocks=4,
    mlp_units=[128],
    mlp_dropout=0.4,
    dropout=0.25,
)

model_mlp.compile(optimizer = adam, loss = root_mean_squared_error)
model_mlp.summary()

I am trying to do a time series plot forecast in transformer.
The input size is (None, 30).
However, an error occurs here.

3

The input size is (None, 30).

The input shape should be (None, 30, 1). You can add new dimension using tf.expand_dims to your input as shown below

X_train = tf.expand_dims(X_train, -1)

Thank you!