TheAlgorithms/Python · error · ValueError

Input arrays must have the same length.

Error message

Input arrays must have the same length.

What it means

Raised by binary_cross_entropy when y_true and y_pred have different lengths. The loss is averaged elementwise over paired entries, so mismatched arrays cannot be combined and the function validates len() equality before computing.

Source

Thrown at machine_learning/loss_functions.py:35

    Parameters:
    - y_true: True binary labels (0 or 1)
    - y_pred: Predicted probabilities for class 1
    - epsilon: Small constant to avoid numerical instability

    >>> true_labels = np.array([0, 1, 1, 0, 1])
    >>> predicted_probs = np.array([0.2, 0.7, 0.9, 0.3, 0.8])
    >>> float(binary_cross_entropy(true_labels, predicted_probs))
    0.2529995012327421
    >>> true_labels = np.array([0, 1, 1, 0, 1])
    >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
    >>> binary_cross_entropy(true_labels, predicted_probs)
    Traceback (most recent call last):
        ...
    ValueError: Input arrays must have the same length.
    """
    if len(y_true) != len(y_pred):
        raise ValueError("Input arrays must have the same length.")

    y_pred = np.clip(y_pred, epsilon, 1 - epsilon)  # Clip predictions to avoid log(0)
    bce_loss = -(y_true * np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred))
    return np.mean(bce_loss)


def binary_focal_cross_entropy(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    gamma: float = 2.0,
    alpha: float = 0.25,
    epsilon: float = 1e-15,
) -> float:
    """
    Calculate the mean binary focal cross-entropy (BFCE) loss between true labels
    and predicted probabilities.

    BFCE loss quantifies dissimilarity between true labels (0 or 1) and predicted

View on GitHub (pinned to f5988cc097)

Solutions

1. Verify shapes before the call: assert y_true.shape == y_pred.shape.
2. Recompute predictions on the exact rows the labels correspond to.
3. Filter both arrays with the same mask when cleaning data.

Example fix

# before
y_pred = model.predict(X_all)
binary_cross_entropy(y_test, y_pred)  # lengths differ

# after
y_pred = model.predict(X_test)
binary_cross_entropy(y_test, y_pred)

y_true = np.asarray(y_true)
y_pred = np.asarray(y_pred)
assert y_true.shape == y_pred.shape, f"{y_true.shape} vs {y_pred.shape}"
loss = binary_cross_entropy(y_true, y_pred)

def same_length(a: np.ndarray, b: np.ndarray) -> bool:
    return len(a) == len(b)

try:
    binary_cross_entropy(y_true, y_pred)
except ValueError as e:
    if "same length" in str(e):
        raise ValueError(f"labels/preds misaligned: {len(y_true)} vs {len(y_pred)}") from e
    raise

When it happens

Trigger: Calling binary_cross_entropy(np.array([0,1,1,0,1]), np.array([0.3,0.8,0.9,0.2])) — 5 labels vs 4 predictions, as in the doctest.

Common situations: Train/test split applied to labels but not predictions, dropping NaN rows from one array only, or evaluating a model that outputs a different batch size than the labels.

Related errors

• x and y have different lengths
• Input arrays must have the same shape.
• y_true must be one-hot encoded.
• Predicted probabilities must sum to approximately 1.
• Shape of y_true and y_pred must be the same.

AI-assisted analysis of TheAlgorithms/Python@f5988cc097 (2026-08-14). Data as JSON: /api/errors/8f44735bec3d2f4f. Report an issue: GitHub.