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Fix Keras Multi-Output Forecasting Shape Error

Resolve 'required broadcastable shapes' error in Keras multi-output forecasting. Learn proper data structure alignment and model configuration for timeseries_dataset_from_array.

#data-preparation#keras-timeseries-forecasting#lstm-shape-mismatch#machine-learning-optimization#multi-output-models#tensorflow-error-fix

How to resolve the ‘required broadcastable shapes’ error when implementing multi-output forecasting with Keras timeseries_dataset_from_array?

I’m trying to create a multi-output forecasting model using Keras with the following setup:

Data Preparation:

python
train_data = features.loc[0 : train_split - 1]
x_train = train_data.iloc[:, :6].values
y_train1 = train_data.iloc[:,6].values
y_train2 = train_data.iloc[:,7].values
y_train3 = train_data.iloc[:,8].values
y_train4 = train_data.iloc[:,9].nvalues
combined_targets_train = np.stack([y_train1, y_train2, y_train3, y_train4], axis=-1)

dataset_train = keras.preprocessing.timeseries_dataset_from_array(
    x_train,
    combined_targets_train,
    sequence_length=120,
    sampling_rate=step,
    batch_size=16,
    shuffle=True,
)

Data Shapes:

  • Input shape: (16, 120, 6)
  • Target shape: (16, 4)

Model Architecture:

python
inputs_seq = keras.layers.Input(shape=(120, 6), name='meteo')
lstm_out = LSTM(10, return_sequences=True)(inputs_seq)

output_surfrun = Dense(1, activation='linear', name='pred_surf_runoff')(lstm_out)
output_subsurf = Dense(1, activation='linear', name='pred_sub_surf_runoff')(lstm_out)
output_evap = Dense(1, activation='linear', name='pred_evapo')(lstm_out)
output_een = Dense(1, activation='linear', name='pred_een')(lstm_out)

model = keras.Model(
    inputs=inputs_seq, 
    outputs=[output_surfrun, output_subsurf, output_evap, output_een], 
    name='model_multi_heads'
)

model.compile(optimizer=keras.optimizers.Adam(learning_rate=learning_rate), loss="mse")

Error Encountered:

Epoch 1/10
2025-11-04 16:20:09.341769: W tensorflow/core/framework/op_kernel.cc:1844] INVALID_ARGUMENT: required broadcastable shapes
InvalidArgumentError: Graph execution error:

Detected at node compile_loss/mse/sub defined at (most recent call last):
  File "/databricks/python_shell/scripts/db_ipykernel_launcher.py", line 52, in <module>

What is causing this shape mismatch error, and how can I properly structure my multi-output forecasting model to work with timeseries_dataset_from_array?

NeuroAgent

The error occurs because there’s a shape mismatch between your model outputs and target data. Your LSTM layer uses return_sequences=True, producing outputs of shape (batch_size, sequence_length, units), but your target data has shape (batch_size, 4) instead of the expected (batch_size, sequence_length, 4).

Here’s how to resolve this issue:

Contents

Understanding the Shape Mismatch

The root cause is that your LSTM layer outputs sequences (due to return_sequences=True), creating outputs of shape (batch_size, sequence_length, 10). When you apply Dense layers to this output, they maintain the sequence dimension, resulting in final outputs of shape (batch_size, sequence_length, 1) for each of your 4 output heads.

However, your target data has shape (batch_size, 4), which doesn’t match the model’s expected output shape of (batch_size, sequence_length, 4).

As Keras documentation explains: “If multi_horizon=True then the model will make a forecast for time steps t+T+1, t+T+2, t+T+3. So the target will have shape (T,3). But if multi_horizon=False, the model will make a forecast only for time step t+T+3 and so the target will have shape (T, 1).”

Solution 1: Modify Target Data Structure

The most straightforward solution is to reshape your target data to match the model’s output shape:

python
# Instead of:
# combined_targets_train = np.stack([y_train1, y_train2, y_train3, y_train4], axis=-1)
# which gives shape (samples, 4)

# Reshape targets to match model output:
combined_targets_train = np.stack([y_train1, y_train2, y_train3, y_train4], axis=-1)
# Expand dimensions to add sequence length
combined_targets_train = np.expand_dims(combined_targets_train, axis=1)  # (samples, 1, 4)
# Repeat for sequence length
combined_targets_train = np.repeat(combined_targets_train, 120, axis=1)  # (samples, 120, 4)

This creates targets that match your model’s output shape of (batch_size, sequence_length, 4).

Solution 2: Change Model Architecture

If you only want to predict a single output per sequence (not per time step), modify your model to use return_sequences=False:

python
inputs_seq = keras.layers.Input(shape=(120, 6), name='meteo')
lstm_out = LSTM(10, return_sequences=False)(inputs_seq)  # Changed to False

output_surfrun = Dense(1, activation='linear', name='pred_surf_runoff')(lstm_out)
output_subsurf = Dense(1, activation='linear', name='pred_sub_surf_runoff')(lstm_out)
output_evap = Dense(1, activation='linear', name='pred_evapo')(lstm_out)
output_een = Dense(1, activation='linear', name='pred_een')(lstm_out)

model = keras.Model(
    inputs=inputs_seq, 
    outputs=[output_surfrun, output_subsurf, output_evap, output_een], 
    name='model_multi_heads'
)

model.compile(optimizer=keras.optimizers.Adam(learning_rate=learning_rate), loss="mse")

With this change, each output will have shape (batch_size, 1), which can work with your target shape (batch_size, 4) if you properly stack them.

Solution 3: Use Multi-Step Forecasting Approach

For true multi-step forecasting, structure your data to predict future time steps:

python
def create_multi_step_dataset(data, targets, sequence_length, forecast_horizon):
    """
    Create dataset for multi-step forecasting
    Args:
        data: Input data of shape (samples, features)
        targets: Target data of shape (samples, num_outputs)
        sequence_length: Length of input sequence
        forecast_horizon: Number of steps to predict
    """
    X, y = [], []
    for i in range(len(data) - sequence_length - forecast_horizon + 1):
        X.append(data[i:i + sequence_length])
        # Get forecast_horizon future steps for each target
        y.append(targets[i + sequence_length:i + sequence_length + forecast_horizon])
    return np.array(X), np.array(y)

# Usage
forecast_horizon = 120  # Predict next 120 time steps
x_train_reshaped, y_train_reshaped = create_multi_step_dataset(
    x_train.reshape(-1, 6),  # Reshape to 2D
    combined_targets_train,
    sequence_length=120,
    forecast_horizon=forecast_horizon
)

# Reshape back to 3D for LSTM
x_train_reshaped = x_train_reshaped.reshape(-1, 120, 6)
y_train_reshaped = y_train_reshaped.reshape(-1, forecast_horizon, 4)

Complete Working Implementation

Here’s a complete working example using Solution 1:

python
import numpy as np
import keras
from keras.layers import LSTM, Dense
from keras.models import Model

# Sample data generation (replace with your actual data)
samples = 1000
sequence_length = 120
features = 6
num_outputs = 4

# Generate synthetic data
x_train = np.random.randn(samples, sequence_length, features)
y_train1 = np.random.randn(samples)
y_train2 = np.random.randn(samples) 
y_train3 = np.random.randn(samples)
y_train4 = np.random.randn(samples)

# Combine targets and reshape to match model output
combined_targets_train = np.stack([y_train1, y_train2, y_train3, y_train4], axis=-1)
combined_targets_train = np.expand_dims(combined_targets_train, axis=1)
combined_targets_train = np.repeat(combined_targets_train, sequence_length, axis=1)

# Create dataset
dataset_train = keras.preprocessing.timeseries_dataset_from_array(
    x_train,
    combined_targets_train,
    sequence_length=sequence_length,
    batch_size=16,
    shuffle=True,
)

# Model architecture
inputs_seq = keras.layers.Input(shape=(sequence_length, features), name='meteo')
lstm_out = LSTM(10, return_sequences=True)(inputs_seq)

output_surfrun = Dense(1, activation='linear', name='pred_surf_runoff')(lstm_out)
output_subsurf = Dense(1, activation='linear', name='pred_sub_surf_runoff')(lstm_out)
output_evap = Dense(1, activation='linear', name='pred_evapo')(lstm_out)
output_een = Dense(1, activation='linear', name='pred_een')(lstm_out)

model = Model(
    inputs=inputs_seq, 
    outputs=[output_surfrun, output_subsurf, output_evap, output_een], 
    name='model_multi_heads'
)

model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss="mse")

# Train the model
model.fit(dataset_train, epochs=10)

Best Practices for Multi-Output Forecasting

  1. Consistent Shape Alignment: Ensure your target data shape matches your model output shape. As shown in the Keras traffic forecasting example, the multi_horizon parameter determines whether you predict multiple time steps or just one.

  2. Data Pipeline Consistency: When using timeseries_dataset_from_array, the targets should have the same number of samples as the input data. The Keras API documentation shows that input arrays should have the same number of samples as target arrays.

  3. Output Layer Configuration: For multi-output forecasting, consider using a single output layer with multiple units instead of separate Dense layers:

    python
    output = Dense(4, activation='linear', name='multi_output')(lstm_out)

  4. Sequence Handling: Be clear about whether you want to predict at each time step (sequence-to-sequence) or just at the end (sequence-to-vector). The Keras weather forecasting example demonstrates proper sequence handling.

  5. Batch Processing: When debugging shape issues, always check the shapes of your batches:

    python
    for batch in dataset_train.take(1):
    inputs, targets = batch
    print("Input shape:", inputs.shape)
    print("Target shape:", targets.shape)

By implementing these solutions, you’ll resolve the “required broadcastable shapes” error and create a properly functioning multi-output forecasting model with Keras.

Sources

  1. Keras documentation: Traffic forecasting using graph neural networks and LSTM
  2. Keras API documentation: Timeseries data loading
  3. Keras documentation: Timeseries forecasting for weather prediction
  4. GitHub: Keras timeseries traffic forecasting example
  5. Stack Overflow: Input and target format for multidimensional time-series regression

Conclusion

The “required broadcastable shapes” error in your multi-output forecasting model stems from a mismatch between your model’s output shape and target data shape. By following these key solutions:

  • Solution 1: Reshape your target data to match the model’s output dimensions
  • Solution 2: Modify your model architecture to use return_sequences=False for sequence-to-vector prediction
  • Solution 3: Implement proper multi-step forecasting with aligned input-output sequences

Remember to always verify your data shapes during debugging and choose the approach that best matches your forecasting requirements. The multi-output forecasting capabilities in Keras are powerful when implemented with proper shape alignment between targets and model outputs.

How to properly structure target data for multi-output LSTM forecasting in Keras?What's the difference between sequence-to-sequence and sequence-to-vector forecasting in Keras?How to implement multi-step forecasting with different prediction horizons in Keras?What are the best practices for handling multiple output targets in time series forecasting?How to debug shape mismatches in Keras LSTM models for time series data?Can I use different loss functions for each output head in a multi-output Keras model?
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