keras-team/keras · error · ValueError

input_t is expected to be tensor, but got {input_t}

Error message

input_t is expected to be tensor, but got {input_t}

What it means

Error "input_t is expected to be tensor, but got {input_t}" thrown in keras-team/keras.

Source

Thrown at keras/src/legacy/backend.py:1477

        if not time_major:
            mask = swap_batch_timestep(mask)

    if constants is None:
        constants = []

    # tf.where needs its condition tensor to be the same shape as its two
    # result tensors, but in our case the condition (mask) tensor is
    # (nsamples, 1), and inputs are (nsamples, ndimensions) or even more.
    # So we need to broadcast the mask to match the shape of inputs.
    # That's what the tile call does, it just repeats the mask along its
    # second dimension n times.
    def _expand_mask(mask_t, input_t, fixed_dim=1):
        if tf.nest.is_nested(mask_t):
            raise ValueError(
                f"mask_t is expected to be tensor, but got {mask_t}"
            )
        if tf.nest.is_nested(input_t):
            raise ValueError(
                f"input_t is expected to be tensor, but got {input_t}"
            )
        rank_diff = len(input_t.shape) - len(mask_t.shape)
        for _ in range(rank_diff):
            mask_t = tf.expand_dims(mask_t, -1)
        multiples = [1] * fixed_dim + input_t.shape.as_list()[fixed_dim:]
        return tf.tile(mask_t, multiples)

    if unroll:
        if not time_steps:
            raise ValueError("Unrolling requires a fixed number of timesteps.")
        states = tuple(initial_states)
        successive_states = []
        successive_outputs = []

        # Process the input tensors. The input tensor need to be split on the
        # time_step dim, and reverse if go_backwards is True. In the case of
        # nested input, the input is flattened and then transformed

View on GitHub (pinned to 7a34a03db6)

When it happens

Trigger: Thrown at keras/src/legacy/backend.py:1477 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/2bd8b8a8831bd664. Report an issue: GitHub.