Introduction

This error means your model expects a 3D tensor shaped like (batch, 14999, 7), but you are feeding a 2D tensor shaped like (batch, 7). In practice, that usually means a sequence or timestep dimension is missing from the data or the model was built for sequence input even though the real data is just tabular features.

Read the expected shape as rank plus meaning

The model expects:

• batch dimension: None
• timestep or sequence length: 14999
• features per timestep: 7

But the actual input is:

• batch dimension: None
• feature dimension only: 7

So the problem is not just size. It is rank. The model expects one extra axis.

Check whether your data is actually sequential

Before reshaping anything, answer this question:

• does each sample truly contain 14999 timesteps with 7 features each

If yes, the data pipeline probably flattened or dropped the sequence dimension. If no, the model was built for the wrong problem type.

That distinction matters because random reshaping can silence the error while still producing meaningless training input.

If sequence structure is real, restore the missing axis

When the raw data really is sequential, reshape it to the expected form:

1import numpy as np
2
3X = np.random.random((32, 14999, 7)).astype("float32")
4print(X.shape)  # (32, 14999, 7)

If the data arrived flattened, then a reshape may be appropriate:

X_flat = np.random.random((32, 14999 * 7)).astype("float32")
X_seq = X_flat.reshape((-1, 14999, 7))
print(X_seq.shape)  # (32, 14999, 7)

Only do this if the original flattened representation really encoded that sequence layout.

If the data is just 7 features, rebuild the model

If each sample only has 7 ordinary features, the model should accept shape (7,) instead:

1import tensorflow as tf
2
3inputs = tf.keras.Input(shape=(7,))
4x = tf.keras.layers.Dense(64, activation="relu")(inputs)
5outputs = tf.keras.layers.Dense(1)(x)
6model = tf.keras.Model(inputs, outputs)

That is the right fix when the model was accidentally designed as if the data were sequential.

Inspect shapes directly from the pipeline

Shape bugs often enter through tf.data transformations, batching, or preprocessing functions. Inspect one batch explicitly:

for xb, yb in dataset.take(1):
    print(xb.shape, yb.shape)

That tells you whether the shape mismatch exists in the raw arrays, the dataset pipeline, or only at model-fit time.

Keep preprocessing and model definition coupled

The safest workflow is to make input-shape assumptions visible in one place and verify them before training:

print("model expects:", model.input_shape)
print("data has:", X_train.shape)

This tiny check catches a surprising number of bugs before you waste time on full training runs.

Common Pitfalls

• Reshaping blindly without confirming the real semantic structure of the data.
• Building a sequence model for tabular input with only 7 features.
• Flattening sequential data during preprocessing and forgetting to restore the sequence axis.
• Assuming Keras will infer a missing timestep dimension automatically.
• Debugging only the model code without printing shapes from the data pipeline.

Summary

• The error is a rank mismatch: the model expects (batch, 14999, 7) but receives (batch, 7).
• First decide whether the data is truly sequential or just tabular.
• If the sequence axis is real, restore it in preprocessing.
• If the data is only 7 features per sample, rebuild the model to accept (7,).
• Print both model and data shapes early to catch this class of bug quickly.