| Unknown activation function '{activation}' cannot be seriali | exception | error | keras, argument-validation, typeerror, image-ops |
| Could not interpret activation function identifier: {identif | exception | error | keras, argument-validation, valueerror, image-ops |
| ConvNeXt does not support the `channels_first` image data fo | exception | error | keras, image-ops, argument-validation, internal-api |
| If using `weights="imagenet"` with `include_top=True`, `clas | exception | error | keras, image-ops, strides, argument-validation |
| Architecture configuration does not match {weights_name} var | exception | error | keras, image-ops, rank-error, shape-mismatch |
| Model name "{name}" does not match weights variant "{weights | exception | error | keras, image-ops, shape-mismatch, patch-embedding |
| DenseNet does not support the `channels_first` image data fo | exception | error | keras, image-ops, padding, shape-mismatch |
| The `weights` argument should be either `None` (random initi | exception | error | keras, image-ops, padding, argument-validation |
| If using `weights` as `"imagenet"` with `include_top` as tru | exception | error | keras, argument-validation, typeerror, image-ops |
| weights_path undefined | exception | error | keras, rank-validation, cropping |
| The `weights` argument should be either `None` (random initi | exception | error | keras, dynamic-shape, cropping |
| If using `weights="imagenet"` as true, `classes` should be 1 | exception | error | keras, cropping, validation |
| The number of repeats in `EfficientNet` must be > 0. Receive | exception | error | keras, cropping, validation |
| The `weights` argument should be either `None` (random initi | exception | error | keras, cropping, shape-validation |
| If using `weights="imagenet"` as true, `classes` should be 1 | exception | error | keras, cropping, validation |
| The number of repeats in `EfficientNetV2` must be > 0. Recei | exception | error | keras, cropping, validation |
| Expected mode to be one of `caffe`, `tf` or `torch`. Receive | exception | error | keras, cropping, shape-validation |
| Expected data_format to be one of `channels_first` or `chann | exception | error | keras, rank-validation, cropping |
| `decode_predictions` expects a batch of predictions (i.e. a | exception | error | |
| When setting `include_top=True` and loading `imagenet` weigh | exception | error | |
| `input_shape` must be a tuple of three integers. | exception | error | |
| The input must have 3 channels; Received `input_shape={input | exception | error | |
| Input size must be at least {min_size}x{min_size}; Received: | exception | error | |
| If `include_top` is True, you should specify a static `input | exception | error | |
| Only `None` and `softmax` activations are allowed for the `c | exception | error | |
| The `weights` argument should be either `None` (random initi | exception | error | |
| If using `weights="imagenet"` with `include_top=True`, `clas | exception | error | |
| Unknown Inception-ResNet block type. Expects "block35", "blo | exception | error | |
| The `weights` argument should be either `None` (random initi | exception | error | |
| If using `weights="imagenet"` with `include_top=True`, `clas | exception | error | |
| The `weights` argument should be either `None` (random initi | exception | error | |
| If using `weights='imagenet'` with `include_top=True`, `clas | exception | error | |
| If imagenet weights are being loaded, depth multiplier must | exception | error | |
| If imagenet weights are being loaded, alpha can be one of`0. | exception | error | |
| The `weights` argument should be either `None` (random initi | exception | error | |
| If using `weights="imagenet"` with `include_top` as true, `c | exception | error | |
| input_tensor: {input_tensor}is not type input_tensor. Receiv | exception | error | |
| input_shape[1] must equal shape(input_tensor)[1] when `image | exception | error | |
| input_tensor.shape[2] must equal input_shape[1]; Received `i | exception | error | |
| input_tensor is not a Keras tensor; Received `input_tensor={ | exception | error | |
| input_tensor must be a valid Keras tensor type; Received {in | exception | error | |
| If imagenet weights are being loaded, alpha must be one of ` | exception | error | |
| The `weights` argument should be either `None` (random initi | exception | error | |
| If using `weights="imagenet"` with `include_top` as true, `c | exception | error | |
| input_tensor: {input_tensor}is not type input_tensor. Recei | exception | error | |
| When backend.image_data_format()=channels_first, input_shape | exception | error | |
| input_shape[1] must equal input_tensor.shape[2]. Received i | exception | error | |
| input_tensor specified: {input_tensor}is not a keras tensor | exception | error | |
| input_tensor: {input_tensor}is type: {type(input_tensor)}whi | exception | error | |
| Input size must be at least 32x32; Received `input_shape={in | exception | error | |
| If imagenet weights are being loaded, alpha can be one of `0 | exception | error | |
| NASNet does not support the `channels_first` image data form | exception | error | |
| The `weights` argument should be either `None` (random initi | exception | error | |
| If using `weights` as `"imagenet"` with `include_top` as tru | exception | error | |
| When specifying the input shape of a NASNet and loading `Ima | exception | error | |
| For NASNet-A models, the `penultimate_filters` must be a mul | exception | error | |
| ImageNet weights can only be loaded with NASNetLarge or NASN | exception | error | |
| The `weights` argument should be either `None` (random initi | exception | error | |
| If using `weights='imagenet'` with `include_top=True`, `clas | exception | error | |
| The `weights` argument should be either `None` (random initi | exception | error | |
| If using `weights='imagenet'` with `include_top=True`, `clas | exception | error | |
| The `weights` argument should be either `None` (random initi | exception | error | |
| If using `weights='imagenet'` with `include_top=True`, `clas | exception | error | |
| The `weights` argument should be either `None` (random initi | exception | error | |
| If using `weights='imagenet'` with `include_top=True`, `clas | exception | error | |
| Unable to import backend : {backend()} | exception | error | |
| The `padding` argument must be one of 'valid', 'same'. Recei | exception | error | |
| axis {axis} is out of bounds for an array with dimension {nu | exception | error | |
| The number of input channels must match the kernel's input c | exception | error | |
| `value` must be an integer, tuple or list. Received: value={ | exception | error | |
| `shift` and `axis` must be broadcastable to the same length. | exception | error | |
| not a valid gufunc signature: {signature} | exception | error | |
| input with shape {shape} does not have enough dimensions for | exception | error | |
| output shape {shape} does not match core dimensions {core_di | exception | error | |
| inconsistent size for core dimension {dim}: {size} vs {dim_s | exception | error | |
| wrong number of positional arguments: expected {len(input_co | exception | error | |
| output must be a tuple when multiple outputs are expected, g | exception | error | |
| wrong number of output arguments: expected {len(output_core_ | exception | error | |
| Cannot pass None at locations {none_args} with signature={si | exception | error | |
| cycle detected in type promotion lattice for node {n} | exception | error | |
| {dtype=} is not a valid dtype for Keras type promotion. | exception | error | |
| Input dtypes {tuple(str(n) for n in nodes)} have no availabl | exception | error | |
| Internal Type Promotion error: {nodes} do not have a unique | exception | error | |
| Invalid value for argument `dtype`. Expected one of {ALLOWED | exception | error | |
| Invalid value for argument `dtype`. Expected one of {extende | exception | error | |
| Invalid value for argument `precision`. Expected one of ('16 | exception | error | |
| There is no implicit conversions from float8 dtypes to other | exception | error | |
| Unexpected keyword arguments: {', '.join(kwargs.keys())} | exception | error | |
| KerasTensor cannot have `sparse=True` and `ragged=True` at t | exception | error | |
| The `shape` attribute of KerasTensor is immutable. One shoul | exception | error | |
| The `dtype` attribute of KerasTensor is immutable. One shoul | exception | error | |
| The `sparse` attribute of KerasTensor is immutable. One shou | exception | error | |
| The `ragged_rank` attribute of KerasTensor is immutable. One | exception | error | |
| The `row_splits_dtype` attribute of KerasTensor is immutable | exception | error | |
| The `ragged` attribute of KerasTensor is immutable. One shou | exception | error | |
| A KerasTensor is symbolic: it's a placeholder for a shape an | exception | error | |
| A KerasTensor is symbolic: it's a placeholder for a shape an | exception | error | |
| A KerasTensor is symbolic: it's a placeholder for a shape an | exception | error | |
| A KerasTensor cannot be used as input to a JAX function. A K | exception | error | |
| A KerasTensor cannot be used as input to a TensorFlow functi | exception | error | |
| Iterating over a symbolic KerasTensor is not supported. | exception | error | |
| A symbolic KerasTensor cannot be used as a boolean. | exception | error | |
| Argument `name` must be a string and cannot contain characte | exception | error | |
| Invalid mode '{mode}'. Supported modes are: 'full', 'activat | exception | error | |
| Invalid reference variable in StatelessScope: all keys in ar | exception | error | |
| Invalid variable value in StatelessScope: all values in argu | exception | error | |
| Argument `name` must be a string and cannot contain characte | exception | error | |
| Invalid value for argument `aggregation`. Expected one of `N | exception | error | |
| Invalid value for argument `synchronization`. Expected one o | exception | error | |
| When creating a Variable from an initializer, the `shape` ar | exception | error | |
| You are attempting to create a variable while in a stateless | exception | error | |
| Variable {self.path} is already initialized. | exception | error | |
| You are attempting to initialize a variable while in a state | validation | error | |
| Shapes used to initialize variables must be fully-defined (n | validation | error | |
| The shape of the target variable and the shape of the target | validation | error | |
| `overwrite_with_gradient` must be a boolean. Received: {valu | validation | error | |
| Invalid value for attribute `regularizer`. Expected a callab | validation | error | |
| Invalid value for attribute `constraint`. Expected a callabl | validation | error | |
| Only scalar arrays can be converted to Python scalars. Got: | validation | error | |
| A Keras Variable cannot be used as a boolean. | validation | error | |
| Invalid dtype: {dtype} | validation | error | |
| Undefined shapes are not supported. | validation | error | |
| Cannot convert '{shape}' to a shape. | validation | error | |
| Cannot convert '{shape}' to a shape. Found invalid dimension | validation | error | |
| Cannot convert '{shape}' to a shape. Negative dimensions are | validation | error | |
| `AutocastScope` can only be used with a floating-point targe | validation | error | |
| Unknown `floatx` value: {value}. Expected one of {accepted_d | validation | error | |
| The `data_format` argument must be one of {'channels_first', | validation | error | |
| To use NNX with the JAX backend, you must install `flax`. | exception | error | |
| Invalid `floatx` configuration. Expected one of {'float16', | validation | error | |
| Invalid `epsilon` configuration. Expected a float. Received: | validation | error | |
| Invalid `image_data_format` configuration. Expected one of { | validation | error | |
| Variable is not properly initialized (raw_value missing) and | exception | error | |
| `ragged=True` is not supported with jax backend | validation | error | |
| `unroll` must be an positive integer or boolean. Received: u | validation | error | |
| Unsupported reduction: {reduction} | validation | error | |
| Invalid value for argument `device_name`. Expected a string | validation | error | |
| The provided job_addresses {job_addresses} has {len(job_addr | validation | error | |
| Device not found: {device_name} | validation | error | |
| Cannot create sharding when device mesh is not set for Tenso | validation | error | |
| Illegal None dimension in {x} with shape {x.shape} | validation | error | |
| Invalid images rank: expected rank 3 (single image) or rank | validation | error | |
| Invalid channel size: expected 3 (RGB) or 1 (Grayscale). Rec | validation | error | |
| Invalid images dtype: expected float dtype. Received: images | validation | error | |
| Invalid value for argument `interpolation`. Expected of one | validation | error | |
| Invalid value for argument `fill_mode`. Only `'constant'` is | validation | error | |
| Only one of `pad_to_aspect_ratio` & `crop_to_aspect_ratio` c | validation | error | |
| Argument `size` must be a tuple of two elements (height, wid | validation | error | |
| Invalid value for argument `interpolation`. Expected of one | validation | error | |
| Invalid value for argument `fill_mode`. Expected of one {AFF | validation | error | |
| Invalid transform rank: expected rank 1 (single transform) o | validation | error | |
| Invalid start_points shape: expected (4,2) for a single imag | validation | error | |
| Invalid end_points shape: expected (4,2) for a single image | validation | error | |
| start_points and end_points must have the same shape. Receiv | validation | error | |
| First dim of `coordinates` must be the same as the rank of ` | validation | error | |
| Invalid coordinates rank: expected at least rank 2. Received | validation | error | |
| Invalid value for argument `fill_mode`. Expected one of {set | validation | error | |
| Invalid value for argument `order`. Expected one of {[0, 1]} | validation | error | |
| Invalid value for argument `method`. Expected of one {SCALE_ | validation | error | |
| Cholesky decomposition failed. The input might not be a vali | validation | error | |
| `mode` argument value not supported. Expected one of {'reduc | validation | error | |
| Expected input to have rank >= 2. Received input with shape | validation | error | |
| Argument `num_segments` must be set when using the JAX backe | validation | error | |
| `cdist` inputs must have rank >= 2 | validation | error | |
| Last dimension of inputs to `cdist` must match | validation | error | |
| Input `x` should be a tuple of two tensors - real and imagin | validation | error | |
| Input `x` should be a tuple of two tensors - real and imagin | validation | error | |
| At least one tensor in input `x` is not of type float.Receiv | validation | error | |
| Invalid input type. Expected `float32` or `float64`. Receive | validation | error | |
| `fft_length` must equal or larger than `sequence_length`. Re | validation | error | |
| If a string is passed to `window`, it must be one of `"hann" | validation | error | |
| The shape of `window` must be equal to [sequence_length].Rec | validation | error | |
| Input `x` must have at least 2 dimensions. Received shape: { | validation | error | |
| Invalid padding '{padding}', must be 'same' or 'valid'. | validation | error | |
| adaptive_average_pool supports only 1D/2D/3D inputs | validation | error | |
| adaptive_max_pool supports only 1D/2D/3D inputs | validation | error | |
| The number of input channels must be evenly divisible by ker | validation | error | |
| The convolution operation resulted in an empty output. This | validation | error | |
| Arguments `target` and `output` must have the same shape. Re | validation | error | |
| Arguments `target` and `output` must be at least rank 1. Rec | validation | error | |
| Argument `output` must be at least rank 1. Received: output. | validation | error | |
| Arguments `target` and `output` must have the same shape up | validation | error | |
| Argument synchronized=True is not supported with JAX. | exception | error | |
| Invalid strategy {strategy}. Supported values are 'greedy' a | validation | error | |
| Input shapes {x1.shape} and {x2.shape} must match for PSNR c | validation | error | |
| Flash attention is not supported in your current JAX version | exception | error | |
| Require at least Ampere arch to run | exception | error | |
| Sharding along sequence dimension not allowed in TPU kernel | validation | error | |
| `dot_product_attention` only supports 4D inputs. Received: q | validation | error | |
| Expected `{t_name}` to have shape (B, 1, T, S) or (B, N, T, | validation | error | |
| Input array must have at least 2 dimensions. Received: array | validation | error | |
| Invalid axes: {axes}. Axes must be a tuple of two different | validation | error | |
| `x` and `weights` must both be BCOOs | validation | error | |
| `x` and `weights` BCOOs must have the same indices | validation | error | |
| ldexp exponent must be an integer type. Received: x2 dtype={ | validation | error | |
| Argument `constant_values` can only be provided when `mode = | validation | error | |
| `searchsorted` only supports 1-D sorted sequences. You can u | validation | error | |
| Variable is not properly initialized (raw_value missing) and | exception | error | |
| `ragged=True` is not supported with jax backend | validation | error | |
| `unroll` must be an positive integer or boolean. Received: u | validation | error | |
| Unsupported reduction: {reduction} | validation | error | |
| Invalid value for argument `device_name`. Expected a string | validation | error | |
| Invalid images rank: expected rank 3 (single image) or rank | validation | error | |
| Invalid channel size: expected 3 (RGB) or 1 (Grayscale). Rec | validation | error | |
| Invalid images dtype: expected float dtype. Received: images | validation | error | |
| Invalid value for argument `interpolation`. Expected of one | validation | error | |
| Invalid value for argument `fill_mode`. Only `'constant'` is | validation | error | |
| Only one of `pad_to_aspect_ratio` & `crop_to_aspect_ratio` c | exception | error | |
| Invalid value for argument `interpolation`. Expected of one | exception | error | |
| Invalid value for argument `fill_mode`. Expected of one {AFF | exception | error | |
| Invalid transform rank: expected rank 1 (single transform) o | exception | error | |
| Invalid start_points shape: expected (4,2) for a single imag | exception | error | |
| Invalid end_points shape: expected (4,2) for a single image | exception | error | |
| start_points and end_points must have the same shape. Receiv | exception | error | |
| First dim of `coordinates` must be the same as the rank of ` | exception | error | |
| Invalid coordinates rank: expected at least rank 2. Received | exception | error | |
| Invalid value for argument `fill_mode`. Expected one of {set | exception | error | |
| Invalid value for argument `order`. Expected one of {[0, 1]} | exception | error | |
| Invalid value for argument `method`. Expected of one {SCALE_ | exception | error | |
| Cholesky decomposition failed. The input might not be a vali | exception | error | |
| `mode` argument value not supported. Expected one of {'reduc | exception | error | |
| Expected input to have rank >= 2. Received input with shape | exception | error | |
| Argument `num_segments` must be set when using the JAX backe | exception | error | |
| `cdist` inputs must have rank >= 2 | exception | error | |
| Last dimension of inputs to `cdist` must match | exception | error | |
| Input `x` should be a tuple of two tensors - real and imagin | exception | error | |
| Input `x` should be a tuple of two tensors - real and imagin | exception | error | |
| At least one tensor in input `x` is not of type float.Receiv | exception | error | |
| Invalid input type. Expected `float32` or `float64`. Receive | exception | error | |
| `fft_length` must equal or larger than `sequence_length`. Re | exception | error | |
| If a string is passed to `window`, it must be one of `"hann" | exception | error | |
| The shape of `window` must be equal to [sequence_length].Rec | exception | error | |
| Input `x` must have at least 2 dimensions. Received shape: { | exception | error | |
| Invalid padding '{padding}', must be 'same' or 'valid'. | validation | error | |
| adaptive_average_pool supports only 1D/2D/3D inputs | validation | error | |
| adaptive_max_pool supports only 1D/2D/3D inputs | validation | error | |
| The number of input channels must be evenly divisible by ker | validation | error | |
| The convolution operation resulted in an empty output. This | validation | error | |
| Arguments `target` and `output` must have the same shape. Re | validation | error | |
| Arguments `target` and `output` must be at least rank 1. Rec | validation | error | |
| Argument `output` must be at least rank 1. Received: output. | validation | error | |
| Arguments `target` and `output` must have the same shape up | validation | error | |
| Argument synchronized=True is not supported with JAX. | exception | error | |
| Invalid strategy {strategy}. Supported values are 'greedy' a | validation | error | |
| Input shapes {x1.shape} and {x2.shape} must match for PSNR c | validation | error | |
| Flash attention is not supported in your current JAX version | exception | error | |
| Require at least Ampere arch to run | error_code | error | |
| Sharding along sequence dimension not allowed in TPU kernel | validation | error | |
| `dot_product_attention` only supports 4D inputs. Received: q | validation | error | |
| Expected `{t_name}` to have shape (B, 1, T, S) or (B, N, T, | validation | error | |
| Received `None` value for `axis` | validation | error | |
| Repeated axis in `axis`: {axis} | validation | error | |
| In `axis`, axis {a} is out of bounds for array of dimension | validation | error | |
| Repeated axis in `axis`: {canonical_axis} | validation | error | |
| Unsupported sparse format: {x1.__class__} | validation | error | |
| Arguments not recognized: {kwargs} | validation | error | |
| Arguments `sample_weight` and `class_weight` cannot be speci | validation | error | |
| `sparse=True` is not supported with numpy backend | validation | error | |
| `ragged=True` is not supported with numpy backend | validation | error | |
| `f` should be a callable. Received: f={f} | validation | error | |
| `unroll` must be an positive integer or boolean. Received: u | validation | error | |
| Got no `xs` to scan over and `length` not provided. | validation | error | |
| Array inputs to associative_scan must have the same first di | validation | error | |
| Shapes are incompatible for associative_scan interleaving. a | validation | error | |
| Unsupported reduction: {reduction} | validation | error | |
| Length of `start_indices` must match length of `shape`. Rece | validation | error | |
| `track` is not implemented in the numpy backend. | exception | error | |
| `add_endpoint` is not implemented in the numpy backend. | exception | error | |
| Invalid images rank: expected rank 3 (single image) or rank | validation | error | |
| Invalid channel size: expected 3 (RGB) or 1 (Grayscale). Rec | validation | error | |
| Invalid images dtype: expected float dtype. Received: images | validation | error | |
| Invalid images dtype: expected float dtype. Received: images | validation | error | |
| Invalid value for argument `interpolation`. Expected of one | validation | error | |
| Invalid value for argument `fill_mode`. Only `'constant'` is | validation | error | |
| Only one of `pad_to_aspect_ratio` & `crop_to_aspect_ratio` c | validation | error | |
| Argument `size` must be a tuple of two elements (height, wid | validation | error | |
| Unknown resize method | validation | error | |
| Invalid value for argument `interpolation`. Expected of one | validation | error | |
| Invalid value for argument `fill_mode`. Expected of one {AFF | validation | error | |
| Invalid transform rank: expected rank 1 (single transform) o | validation | error | |
| Invalid value for argument `interpolation`. Expected of one | validation | error | |
| Invalid start_points shape: expected (4,2) for a single imag | validation | error | |
| Invalid end_points shape: expected (4,2) for a single image | validation | error | |
| start_points and end_points must have the same shape. Receiv | validation | error | |
| First dim of `coordinates` must be the same as the rank of ` | exception | error | |
| Invalid coordinates rank: expected at least rank 2. Received | exception | error | |
| Invalid value for argument `fill_mode`. Expected one of {set | exception | error | |
| Invalid value for argument `order`. Expected one of [0, 1]. | exception | error | |
| Invalid value for argument `method`. Expected of one {SCALE_ | exception | error | |
| `mode` argument value not supported. Expected one of {'reduc | exception | error | |
| Expected input to have rank >= 2. Received input with shape | exception | error | |
| JVP is not supported by the Numpy backend. | exception | error | |
| `cdist` inputs must have rank >= 2 | exception | error | |
| Last dimension of inputs to `cdist` must match | exception | error | |
| Input `x` should be a tuple of two tensors - real and imagin | exception | error | |
| Input `x` should be a tuple of two tensors - real and imagin | exception | error | |
| At least one tensor in input `x` is not of type float.Receiv | exception | error | |
| Invalid input type. Expected `float32` or `float64`. Receive | exception | error | |
| `fft_length` must equal or larger than `sequence_length`. Re | exception | error | |
| If a string is passed to `window`, it must be one of `"hann" | exception | error | |
| The shape of `window` must be equal to [sequence_length].Rec | exception | error | |
| axis size must be divisible by 2. Received: x.shape={x.shape | exception | error | |
| Invalid padding '{padding}', must be 'same' or 'valid'. | exception | error | |
| adaptive_average_pool supports only 1D/2D/3D | exception | error | |
| adaptive_max_pool supports only 1D/2D/3D | exception | error | |
| The number of input channels must be evenly divisible by ker | exception | error | |
| The convolution operation resulted in an empty output. This | exception | error | |
| Unsupported value `sparse=True` with numpy backend | exception | error | |
| Arguments `target` and `output` must have the same shape. Re | exception | error | |
| Arguments `target` and `output` must be at least rank 1. Rec | exception | error | |
| Argument `output` must be at least rank 1. Received: output. | exception | error | |
| Arguments `target` and `output` must have the same shape up | exception | error | |
| Argument synchronized=True is not supported with NumPy. | exception | error | |
| Invalid strategy {strategy}. Supported values are 'greedy' a | exception | error | |
| Input shapes {x1.shape} and {x2.shape} must match for PSNR c | exception | error | |
| Flash attention is not supported in numpy backend. | exception | error | |
| `dot_product_attention` only supports 4D inputs. Received: q | exception | error | |
| Input array must have at least 2 dimensions. Received: array | exception | error | |
| Invalid axes: {axes}. Axes must be a tuple of two different | exception | error | |
| Unsupported value `sparse=True` with numpy backend | exception | error | |
| Both input arrays must be (arrays of) 2 or 3-dimensional vec | exception | error | |
| ldexp exponent must be an integer type. Received: x2 dtype={ | exception | error | |
| Argument `constant_values` can only be provided when `mode = | exception | error | |
| `searchsorted` only supports 1-D sorted sequences. You can u | exception | error | |
| `sparse=True` is not supported with numpy backend | exception | error | |
| `ragged=True` is not supported with numpy backend | exception | error | |
| `f` should be a callable. Received: f={f} | exception | error | |
| `unroll` must be an positive integer or boolean. Received: u | exception | error | |
| Got no `xs` to scan over and `length` not provided. | exception | error | |
| Array inputs to associative_scan must have the same first di | exception | error | |
| Shapes are incompatible for associative_scan interleaving. a | exception | error | |
| Unsupported reduction: {reduction} | exception | error | |
| Length of `start_indices` must match length of `shape`. Rece | exception | error | |
| Invalid images rank: expected rank 3 (single image) or rank | exception | error | |
| Invalid channel size: expected 3 (RGB) or 1 (Grayscale). Rec | exception | error | |
| Invalid images dtype: expected float dtype. Received: images | exception | error | |
| Invalid images dtype: expected float dtype. Received: images | exception | error | |
| Invalid value for argument `interpolation`. Expected of one | exception | error | |
| Invalid value for argument `fill_mode`. Only `'constant'` is | exception | error | |
| Only one of `pad_to_aspect_ratio` & `crop_to_aspect_ratio` c | exception | error | |
| Argument `size` must be a tuple of two elements (height, wid | exception | error | |
| Unknown resize method | exception | error | |
| Invalid value for argument `interpolation`. Expected of one | exception | error | |
| Invalid value for argument `fill_mode`. Expected of one {AFF | exception | error | |
| Invalid transform rank: expected rank 1 (single transform) o | exception | error | |
| Invalid value for argument `interpolation`. Expected of one | exception | error | |
| Invalid start_points shape: expected (4,2) for a single imag | exception | error | |
| Invalid end_points shape: expected (4,2) for a single image | exception | error | |
| start_points and end_points must have the same shape. Receiv | exception | error | |
| First dim of `coordinates` must be the same as the rank of ` | exception | error | |
| Invalid coordinates rank: expected at least rank 2. Received | exception | error | |
| Invalid value for argument `fill_mode`. Expected one of {set | exception | error | |
| Invalid value for argument `method`. Expected of one {SCALE_ | exception | error | |
| `mode` argument value not supported. Expected one of {'reduc | exception | error | |
| Expected input to have rank >= 2. Received input with shape | exception | error | |
| JVP is not supported by the Numpy backend. | exception | error | |
| `cdist` inputs must have rank >= 2 | exception | error | |
| Last dimension of inputs to `cdist` must match | exception | error | |
| Input `x` should be a tuple of two tensors - real and imagin | exception | error | |
| Input `x` should be a tuple of two tensors - real and imagin | exception | error | |
| At least one tensor in input `x` is not of type float.Receiv | exception | error | |
| Invalid input type. Expected `float32` or `float64`. Receive | exception | error | |
| `fft_length` must equal or larger than `sequence_length`. Re | exception | error | |
| If a string is passed to `window`, it must be one of `"hann" | exception | error | |
| The shape of `window` must be equal to [sequence_length].Rec | exception | error | |
| axis size must be divisible by 2. Received: x.shape={x.shape | exception | error | |
| Invalid padding '{padding}', must be 'same' or 'valid'. | exception | error | |
| adaptive_average_pool supports only 1D/2D/3D | exception | error | |
| adaptive_max_pool supports only 1D/2D/3D | exception | error | |
| The number of input channels must be evenly divisible by ker | exception | error | |
| The convolution operation resulted in an empty output. This | exception | error | |
| Unsupported value `sparse=True` with numpy backend | exception | error | |
| Arguments `target` and `output` must have the same shape. Re | exception | error | |
| Arguments `target` and `output` must be at least rank 1. Rec | exception | error | |
| Argument `output` must be at least rank 1. Received: output. | exception | error | |
| Arguments `target` and `output` must have the same shape up | exception | error | |
| Argument synchronized=True is not supported with NumPy. | exception | error | |
| Invalid strategy {strategy}. Supported values are 'greedy' a | exception | error | |
| Input shapes {x1.shape} and {x2.shape} must match for PSNR c | exception | error | |
| Flash attention is not supported in numpy backend. | exception | error | |
| `dot_product_attention` only supports 4D inputs. Received: q | exception | error | |
| Input array must have at least 2 dimensions. Received: array | exception | error | |
| Invalid axes: {axes}. Axes must be a tuple of two different | exception | error | |
| `convert_to_numpy` failed to convert the tensor. | exception | error | |
| `f` should be a callable. Received: f={f} | exception | error | |
| `unroll` must be an positive integer or boolean. Received: u | exception | error | |
| Got no `xs` to scan over and `length` not provided. | exception | error | |
| Array inputs to associative_scan must have the same first di | exception | error | |
| Unsupported reduction: {reduction} | exception | error | |
| `slice` operation requires tuple for `start_indices with the | exception | error | |
| `slice` operation requires tuple for `shape` with the openvi | exception | error | |
| `slice` is not supported by OpenVINO backend for `start_indi | exception | error | |
| `slice_update` is not supported by openvino backend for `sta | exception | error | |
| `slice_update` requires integral start_indices | exception | error | |
| Expected tuple or dict for `loop_vars`, Received: {type(loop | exception | error | |
| `cond` function must return a scalar boolean value, but got | exception | error | |
| `track` is not implemented in the openvino backend. | exception | error | |
| `add_endpoint` is not implemented in the openvino backend. | exception | error | |
| Invalid images rank: expected rank 3 (single image) or rank | exception | error | |
| Invalid channel size: expected 3 (RGB) or 1 (Grayscale). Rec | exception | error | |
| Invalid images dtype: expected float dtype. Received: images | exception | error | |
| Invalid value for argument `interpolation`. Expected of one | exception | error | |
| Invalid value for argument `fill_mode`. Only `'constant'` is | exception | error | |
| Only one of `pad_to_aspect_ratio` & `crop_to_aspect_ratio` c | exception | error | |
| Argument `size` must be a tuple of two elements (height, wid | exception | error | |
| Invalid images rank: expected rank 3 (single image) or rank | exception | error | |
| Invalid value for argument `interpolation`. Expected of one | exception | error | |
| Invalid value for argument `fill_mode`. Expected of one {AFF | exception | error | |
| Invalid transform rank: expected rank 1 (single transform) o | exception | error | |
| Invalid start_points shape: expected (4,2) for a single imag | exception | error | |
| Invalid end_points shape: expected (4,2) for a single image | exception | error | |
| Invalid value for argument `fill_mode`. Expected one of {fil | exception | error | |
| Invalid value for argument `order`. Expected one of [0, 1]. | exception | error | |
| Invalid coordinates rank: expected at least rank 2. Received | exception | error | |
| First dim of `coordinates` must be the same as the rank of ` | exception | error | |
| Invalid images rank: expected rank 3 (single image) or rank | exception | error | |
| Operation must be 'min' or 'max', received {operation} | exception | error | |
| `view` from {old_dtype} to {new_dtype} is not supported for | exception | error | |
| Axis must be specified when shapes of a and weights differ. | exception | error | |
| Shape of weights must be consistent with shape of a along sp | exception | error | |
| input x is None | exception | error | |
| Unsupported value `sparse=True` | exception | error | |
| `broadcast_to` is supported only for tuple and list `shape`. | exception | error | |
| Dimension of vectors for cross product must be 2 or 3. Got d | exception | error | |
| diag supports only 1D or 2D tensors | exception | error | |
| `diagonal` requires input tensor with static rank. | exception | error | |
| diagonal requires input tensor with rank >= 2.Given rank: {r | exception | error | |
| `axis1` and `axis2` cannot be the same. | exception | error | |
| order must be non-negative but got {repr(n)} | exception | error | |
| `bins` must be 1-D array-like | exception | error | |
| The `flip` operation does not support tensors with dynamic r | exception | error | |
| axes must be a tuple of length 2 | exception | error | |
| `rot90` does not support tensors with dynamic rank for the O | exception | error | |
| axes must be different | exception | error | |
| `kron` does not support tensors with dynamic rank for the Op | exception | error | |
| `lcm` is only supported for integer types. | exception | error | |
| Could not extract num value from tensor | exception | error | |
| Number of samples, `num`, must be non-negative. | exception | error | |
| median only supports single axis reduction | exception | error | |
| meshgrid requires at least 2 input arrays. Received: {len(x) | exception | error | |
| indexing must be either 'xy' or 'ij' | exception | error | |
| Cannot determine `ndim`: tensor has a dynamically-ranked Par | exception | error | |
| Argument `constant_values` can only be provided when `mode = | exception | error | |
| `pad` operation supports only scalar pad value in constant m | exception | error | |
| `side` must be either 'left' or 'right'. Received: side={sid | exception | error | |
| `searchsorted` only supports 1-D sorted sequences. You can u | exception | error | |
| unsupported type of indices_or_sections: {type(indices_or_se | exception | error | |
| Cannot use array_split with static Python logic on dynamic a | exception | error | |
| `stack` supports only `x` as list or tuple. Received: {type( | exception | error | |
| `swapaxes` does not support tensors with dynamic rank for th | exception | error | |
| x2 must be provided if x1 is specified. | exception | error | |
| `shape` argument cannot contain `None`. Received: shape={sha | exception | error | |
| mode: {mode} not available chose from valid, same, full. | exception | error | |
| select(): condlist and choicelist must have the same length | validation | error | |
| Unknown type of input data {type(data)} | validation | error | |
| `fit` is not supported with openvino backend | exception | error | |
| Arguments not recognized: {kwargs} | validation | error | |
| `train_on_batch` is not supported with openvino backend | exception | error | |
| Variable {self.path} is already initialized. | validation | error | |
| You are attempting to initialize a variable while in a state | validation | error | |
| All tensors passed to `ops.shape` must have a statically kno | validation | error | |
| `f` should be a callable. Received: f={f} | validation | error | |
| `unroll` must be an positive integer or boolean. Received: u | validation | error | |
| Got no `xs` to scan over and `length` not provided. | validation | error | |
| Array inputs to associative_scan must have the same first di | validation | error | |
| Unsupported reduction: {reduction} | validation | error | |
| Cannot create sharding when device mesh is not set for Tenso | validation | error | |
| Invalid images rank: expected rank 3 (single image) or rank | validation | error | |
| Invalid channel size: expected 3 (RGB) or 1 (Grayscale). Rec | validation | error | |
| Invalid images dtype: expected float dtype. Received: images | validation | error | |
| Invalid value for argument `interpolation`. Expected of one | validation | error | |
| Invalid value for argument `fill_mode`. Only `'constant'` is | validation | error | |
| Only one of `pad_to_aspect_ratio` & `crop_to_aspect_ratio` c | validation | error | |
| Argument `size` must be a tuple of two elements (height, wid | validation | error | |
| Invalid value for argument `interpolation`. Expected of one | validation | error | |
| Invalid value for argument `fill_mode`. Expected of one {AFF | validation | error | |
| Invalid transform rank: expected rank 1 (single transform) o | validation | error | |
| Invalid start_points shape: expected (4,2) for a single imag | validation | error | |
| Invalid end_points shape: expected (4,2) for a single image | validation | error | |
| start_points and end_points must have the same shape. Receiv | validation | error | |
| First dim of `coordinates` must be the same as the rank of ` | validation | error | |
| Invalid coordinates rank: expected at least rank 2. Received | exception | error | |
| map_coordinates currently requires order<=1 | exception | error | |
| Unknown fill_mode: {fill_mode} | exception | error | |
| Invalid value for argument `method`. Expected of one {SCALE_ | exception | error | |
| All `axis` values must be in the range [-ndim, ndim). Receiv | exception | error | |
| Invalid `ord` argument for vector norm. Received: ord={ord} | exception | error | |
| Invalid `ord` argument for matrix norm. Received: ord={ord} | exception | error | |
| Invalid axis values. Received: axis={axis} | exception | error | |
| `mode` argument value not supported. Expected one of {'reduc | exception | error | |
| Leading dimensions of input arrays must match | exception | error | |
| {a.ndim}-dimensional array given. Array must be two-dimensio | exception | error | |
| {b.ndim}-dimensional array given. Array must be one or two-d | exception | error | |
| Argument `num_segments` cannot be set when sorted is True wh | exception | error | |
| `cdist` inputs must have rank >= 2 | exception | error | |
| Last dimension of inputs to `cdist` must match | exception | error | |
| Input `x` should be a tuple of two tensors - real and imagin | exception | error | |
| Input `x` should be a tuple of two tensors - real and imagin | exception | error | |
| At least one tensor in input `x` is not of type float.Receiv | exception | error | |
| Invalid input type. Expected `float32` or `float64`. Receive | exception | error | |
| `fft_length` must equal or larger than `sequence_length`. Re | exception | error | |
| If a string is passed to `window`, it must be one of `"hann" | exception | error | |
| The shape of `window` must be equal to [sequence_length].Rec | exception | error | |
| axis size must be divisible by 2. Received: x.shape={x.shape | exception | error | |
| Pooling inputs's shape must be 3, 4 or 5, corresponding to 1 | exception | error | |
| Input length must be statically known for adaptive pooling | exception | error | |
| Input spatial dimensions must be statically known for adapti | exception | error | |
| adaptive_average_pool supports 1D, 2D, or 3D inputs only. | exception | error | |
| `label_mode` must be one of `"fine"`, `"coarse"`. Received: | validation | error | |
| After filtering for sequences shorter than maxlen={maxlen}, | validation | error | |
| Unsupported `.npy` file version: {version}. | validation | error | |
| Unknown loss function: '{loss_item}'. | validation | error | |
| Subclasses must implement compute_loss | exception | error | |
| Expect `mesh` to be an instance of `DeviceMesh`. Received: m | exception | error | |
| You must specify a layout_map argument. | exception | error | |
| Argument `layout_map` must be a `LayoutMap` instance. Receiv | exception | error | |
| If `num_processes` is greater than `num_model_replicas`, `nu | exception | error | |
| Path '{key}' matches multiple layout specification keys: {ma | exception | error | |
| {key} already exist in the LayoutMap with value {self._layou | exception | error | |
| {layout} should be a TensorLayout type, got {type(layout)} | exception | error | |
| Cannot interpret `dtype` argument. Expected a string or an i | exception | error | |
| Unknown target_spec attribute '{spec_key}' | validation | error | |
| Unknown converter attribute '{attr}' | validation | error | |
| To export to LiteRT with the PyTorch backend, you must insta | exception | error | |
| Failed to convert PyTorch model to LiteRT. Common causes: un | exception | error | |
| Unsupported arguments for LiteRT export with the PyTorch bac | validation | error | |
| _backend_add_endpoint() must be implemented in backend subcl | exception | error | |
| Expected `variables` to be a list/tuple/set. Received instea | exception | error | |
| Expected all elements in `variables` to be `tf.Variable` ins | exception | error | |
| No endpoints have been set yet. Call add_endpoint(). | exception | error | |
| `backend_variable` must be a `backend.Variable`. Recevied: b | exception | error | keras, export, savedmodel, type-error, backend |
| The TFSMLayer is only currently supported with the TensorFlo | exception | error | keras, tfsmlayer, backend, tensorflow, not-implemented |
| The endpoint '{call_endpoint}' is neither an attribute of th | exception | error | keras, tfsmlayer, savedmodel, endpoint, validation |
| The endpoint '{call_training_endpoint}' is neither an attrib | exception | error | keras, tfsmlayer, training-endpoint, savedmodel, validation |
| Requested the deserialization of a `TFSMLayer`, which loads | exception | error | keras, tfsmlayer, deserialization, security, unsafe-deserialization |
| The PyTorch export requires the filepath to end with '.pt2'. | exception | error | keras, pytorch, export, file-extension, validation |
| Returning attention scores is not supported when flash atten | exception | error | keras, attention, flash-attention, grouped-query-attention, incompatibility |
| The last dimension of `query_shape` and `value_shape` must b | exception | error | keras, attention, shape-mismatch, validation, grouped-query-attention |
| All dimensions of `value` and `key`, except the last one, mu | exception | error | keras, attention, shape-mismatch, validation, grouped-query-attention |
| Received an invalid value for argument `num_heads`, expected | exception | error | keras, multi-head-attention, argument-validation, value-error |
| Received an invalid value for argument `key_dim`, expected a | exception | error | |
| Received an invalid value for argument `value_dim`, expected | exception | error | |
| Invalid `output_shape`: {output_shape}. When specified, the | exception | error | |
| `attention_axes` must be an int, list, or tuple.Received: at | exception | error | |
| `sliding_window` must be `None` or a positive integer. Recei | exception | error | |
| Dropout is not supported when flash attention is enabled. Pl | exception | error | |
| You must build the layer before accessing `kernel`. | exception | error | |
| Lora is incompatible with kernel constraints. In order to en | exception | error | |
| Cannot enable lora on a layer that isn't yet built. | exception | error | |
| lora is already enabled. This can only be done once per laye | exception | error | |
| Layer '{self.name}' was never built and thus it doesn't have | exception | critical | keras, model-loading, custom-layers, serialization |
| `strides > 1` not supported in conjunction with `dilation_ra | exception | error | keras, convolution, transposed-conv, invalid-arguments |
| Invalid `output_padding` argument. Each value in `output_pad | exception | error | keras, transposed-conv, output-padding, shape-mismatch |
| Invalid value for argument `depth_multiplier`. Expected a st | exception | error | keras, depthwise-conv, invalid-arguments, validation |
| The argument `kernel_size` cannot contain 0. Received kernel | exception | error | keras, depthwise-conv, kernel-size, invalid-arguments |
| The argument `strides` cannot contains 0. Received strides={ | exception | error | keras, depthwise-conv, strides, invalid-arguments |
| Invalid value for argument `depth_multiplier`. Expected a st | exception | error | keras, separable-conv, invalid-arguments, validation |
| Invalid value for argument `filters`. Expected a strictly po | exception | error | keras, separable-conv, filters, invalid-arguments |
| The argument `kernel_size` cannot contain 0. Received: kerne | exception | error | keras, separable-conv, kernel-size, invalid-arguments |
| The argument `strides` cannot contains 0(s). Received: strid | exception | error | keras, separable-conv, strides, invalid-arguments |
| Received an invalid value for `units`, expected a positive i | exception | error | keras, dense, type-validation, units |
| You must build the layer before accessing `kernel`. | exception | error | keras, dense, lazy-build, attribute-error |
| Lora is incompatible with kernel constraints. In order to en | exception | error | keras, lora, fine-tuning, constraints |
| Cannot enable lora on a layer that isn't yet built. | exception | error | keras, lora, lazy-build, precondition |
| lora is already enabled. This can only be done once per laye | exception | error | keras, lora, idempotency, duplicate-call |
| lora is not currently supported with GPTQ quantization. | exception | error | keras, lora, gptq, quantization, unsupported-operation |
| Cannot save layer '{self.name}' because it is quantized with | exception | critical | keras, quantization, gptq, awq, model-saving, calibration |
| Currently, `_float8_call` doesn't support LoRA | exception | error | keras, lora, float8, quantization, unsupported-operation |
| Unsupported quantization mode: {self.quantization_mode} | exception | error | keras, lora, quantization, model-saving, unsupported-operation |
| You must build the layer before accessing `kernel`. | exception | error | keras, einsum-dense, lazy-build, attribute-error |
| Lora is incompatible with kernel constraints. In order to en | exception | error | keras, lora, einsum-dense, kernel-constraint, peft |
| Cannot enable lora on a layer that isn't yet built. | exception | error | keras, lora, einsum-dense, layer-build, peft |
| lora is already enabled. This can only be done once per laye | exception | error | keras, lora, einsum-dense, idempotency, peft |
| lora is not currently supported with GPTQ quantization. | exception | error | keras, lora, gptq, quantization, einsum-dense |
| Cannot save layer '{self.name}' because it is quantized with | exception | critical | keras, quantization, gptq, awq, model-saving, calibration |
| Could not determine row/column split. | exception | error | keras, gptq, quantization, einsum-dense, kernel-shape |
| AWQ quantization only supports 2D or 3D kernels. | exception | error | keras, awq, quantization, einsum-dense, kernel-rank |
| Currently, `_float8_call` doesn't support LoRA | exception | error | keras, float8, quantization, lora, einsum-dense |
| Unsupported quantization mode: {self.quantization_mode} | exception | error | keras, lora, quantization, model-saving, einsum-dense |
| Invalid tensor type: {tensor_type} | exception | error | keras, einsum-dense, quantization, internal-api |
| Invalid einsum equation '{equation}'. Equations must be in t | exception | error | keras, einsum-dense, einsum-equation, validation |
| Input shape and output shape do not match at shared dimensio | exception | error | keras, einsum-dense, shape-mismatch, split-equation |
| Dimension '{dim}' was specified in the output '{output_spec} | exception | error | keras, einsum-dense, einsum-equation, split-equation |
| Weight dimension '{dim}' did not have a match in either the | exception | error | keras, einsum-dense, einsum-equation, kernel-shape |
| Bias dimension '{char}' was requested, but is not part of th | exception | error | keras, einsum-dense, bias-axes, einsum-equation |
| `input_dim` must be a positive integer. Received: input_dim= | exception | error | keras, embedding, constructor-validation, input-dim |
| `output_dim` must be a positive integer. Received: output_di | exception | error | keras, embedding, constructor-validation, output-dim |
| You must build the layer before accessing `embeddings`. | exception | error | keras, embedding, build-state, attribute-access |
| Lora is incompatible with embedding constraints. In order to | exception | error | |
| Cannot enable lora on a layer that isn't yet built. | exception | error | |
| lora is already enabled. This can only be done once per laye | exception | error | |
| Unsupported quantization mode: {self.quantization_mode} | exception | error | |
| Argument `input_tensor` must be a KerasTensor. Received inva | exception | error | |
| When providing the `input_tensor` argument, you cannot provi | exception | error | |
| When providing the `input_tensor` argument, you cannot provi | exception | error | |
| When providing the `input_tensor` argument, you cannot provi | exception | error | |
| When providing the `input_tensor` argument, you cannot provi | exception | error | |
| When providing the `input_tensor` argument, you cannot provi | exception | error | |
| You cannot pass both `shape` and `batch_shape` at the same t | exception | error | |
| You cannot pass both `batch_size` and `batch_shape` at the s | exception | error | |
| You must pass a `shape` argument. | exception | error | |
| `sparse=True` is not supported with the {backend.backend()} | exception | error | |
| `ragged=True` is not supported with the {backend.backend()} | exception | error | |
| We could not automatically infer the shape of the Lambda's o | exception | error | |
| Invalid input type for serialization. Received: {fn} of type | exception | error | |
| Requested the deserialization of a `Lambda` layer whose `fun | exception | error | |
| Received an invalid value for `units`, expected a positive i | exception | error | |
| Received an invalid value for `threshold`, expected a float | exception | error | |
| Received an invalid value for `threshold`, expected a non-ne | exception | error | |
| Layer {layer} supplied to Wrapper isn't a supported layer ty | validation | error | |
| Argument `axes` must be a dict with integer keys. Received: | validation | error | |
| Axis {} is greater than the maximum allowed value: {} | validation | error | |
| Missing data for input "{name}". You passed a data dictionar | validation | error | |
| Layer "{layer_name}" expects {len(input_spec)} named input(s | validation | error | |
| Layer "{layer_name}" expects {len(input_spec)} input(s), but | validation | error | |
| Inputs to a layer should be tensors. Got '{x}' (of type {typ | validation | error | |
| Input {input_index} with name '{spec.name}' of layer '{layer | validation | error | |
| Input {input_index} with name '{spec.name}' of layer '{layer | validation | error | |
| Input {input_index} with name '{spec.name}' of layer '{layer | validation | error | |
| Input {input_index} with name '{spec.name}' of layer '{layer | validation | error | |
| Input {input_index} with name '{spec.name}' of layer '{layer | validation | error | |
| Input {input_index} with name '{spec.name}' of layer '{layer | validation | error | |
| Backend '{backend.backend()}' must implement a layer mixin c | exception | error | |
| Unrecognized keyword arguments passed to {self.__class__.__n | validation | error | |
| Expected `trainable` to be a boolean. Received: trainable={t | validation | error | |
| add_weight() takes at most 3 positional arguments but {len(a | validation | error | |
| `name` must be passed as a keyword argument. Received: add_w | validation | error | |
| `shape` was passed both positionally and as a keyword argume | validation | error | |
| `initializer` was passed both positionally and as a keyword | validation | error | keras, layers, add-weight, arguments |
| `dtype` was passed both positionally and as a keyword argume | validation | error | keras, layers, add-weight, dtype |
| You called `set_weights(weights)` on layer '{self.name}' wit | validation | error | keras, weights, set-weights, loading |
| Layer {self.name} weight shape {variable.shape} is not compa | validation | error | keras, weights, shape, set-weights |
| Implicitly enabling GPTQ quantization by setting `dtype_poli | validation | error | keras, quantization, gptq, dtype-policy |
| Only input tensors may be passed as positional arguments. Th | validation | error | keras, layers, call-arguments, tensor-validation |
| To call stateless_call, {self.__class__.__name__} must be bu | validation | error | keras, stateless, build, functional-api |
| Argument `trainable_variables` must be a list of tensors cor | validation | error | keras, stateless, variables, validation |
| Argument `non_trainable_variables` must be a list of tensors | validation | error | keras, stateless, variables, validation |
| Method `compute_output_shape()` of layer {self.__class__.__n | validation | error | keras, custom-layers, output-shape |
| `add_loss()` can only be called from inside `build()` or `ca | validation | error | keras, layers, add-loss, tensor-validation |
| Cannot quantize a layer that isn't yet built. Layer '{self.n | validation | error | keras, quantization, build |
| Layer '{self.name}' is already quantized with dtype_policy=' | validation | error | keras, quantization, double-operation |
| Invalid quantization mode. Expected one of {dtype_policies.Q | validation | error | keras, quantization, invalid-argument |
| Quantization mode='{mode}' doesn't work well with compute_dt | validation | error | keras, quantization, dtype, float16 |
| Layer '{self.name}' was never built and thus it doesn't have | validation | error | keras, saving, loading, custom-layers, build |
| Layer '{self.name}' expected {len(all_vars)} variables, but | validation | error | keras, loading, weights, variable-mismatch |
| Layer `add_metric()` method is deprecated. Add your metric i | exception | error | keras, metrics, deprecated-api, migration |
| You tried to call `count_params` on layer '{self.name}', but | validation | error | keras, build, parameter-count |
| In layer '{self.__class__.__name__}', you forgot to call `su | exception | critical | keras, custom-layers, init, subclassing |
| Cannot add call-context args after the layer has been called | exception | error | keras, layer, lifecycle, api-misuse |
| In a nested call() argument, you cannot mix tensors and non- | validation | error | keras, tensor, argument-validation, nested-argument |
| {error_preamble} For layer '{class_name}', Received `{method | validation | error | keras, shape-inference, api-contract, naming-convention |
| {error_preamble} For layer '{class_name}', received `{method | validation | error | keras, shape-inference, signature-mismatch |
| Inputs have incompatible shapes. Received shapes {shape1} an | validation | error | keras, merge, shape-mismatch, broadcasting |
| A merge layer should be called on a list of inputs. Received | validation | error | keras, merge, input-format, api-misuse |
| A merge layer should be called on a list of at least 1 input | validation | error | keras, merge, empty-input |
| Cannot merge tensors with different batch sizes. Received te | validation | error | keras, merge, batch-size, shape-mismatch |
| A merge layer should be called on a list of inputs. Received | validation | error | keras, merge, input-format, runtime |
| `mask` should be a list. Received: mask={mask} | validation | error | keras, merge, masking |
| `inputs` should be a list. Received: inputs={inputs} | validation | error | keras, merge, masking, input-format |
| The lists `inputs` and `mask` should have the same length. R | validation | error | keras, merge, masking, length-mismatch |
| A `Concatenate` layer should be called on a list of at least | validation | error | keras, concatenate, input-format |
| A `Concatenate` layer requires inputs with matching shapes e | validation | error | keras, concatenate, rank-mismatch, shape-mismatch |
| A `Concatenate` layer should be called on a list of inputs. | validation | error | keras, concatenate, shape-inference, input-format |
| `mask` should be a list. Received mask={mask} | validation | error | keras, concatenate, masking |
| `inputs` should be a list. Received: inputs={inputs} | validation | error | keras, concatenate, masking, input-format |
| The lists `inputs` and `mask` should have the same length. R | validation | error | keras, concatenate, masking, length-mismatch |
| Cannot do batch_dot on inputs with rank < 2. Received inputs | validation | error | keras, dot, batch-dot, rank-mismatch |
| Cannot do batch_dot on inputs with different batch sizes. Re | validation | error | |
| Multiple target dimensions are not supported. Expected: None | validation | error | |
| Cannot perform batch_dot over axis 0. If your inputs are not | validation | error | |
| Cannot do batch_dot on inputs with shapes {x_shape} and {y_s | validation | error | |
| Invalid type for argument `axes`: it should be a list or an | validation | error | |
| Invalid format for argument `axes`: it should contain two el | validation | error | |
| Invalid format for argument `axes`: list elements should be | validation | error | |
| A `Dot` layer should be called on a list of 2 inputs. Receiv | validation | error | |
| Incompatible input shapes: axis values {shape1[axes[0]]} (at | validation | error | |
| A `Dot` layer should be called on exactly 2 inputs. Received | validation | error | |
| A `Subtract` layer should be called on exactly 2 inputs. Rec | validation | error | |
| A `Subtract` layer should be called on exactly 2 inputs. Rec | validation | error | |
| Argument synchronized=True is only supported with the Tensor | validation | error | |
| Received invalid keys for `renorm_clipping` argument: {renor | validation | error | |
| rmax should be greater than rmin in the `renorm_clipping` ar | validation | error | |
| dmax should be non-negative in the `renorm_clipping` argumen | validation | error | |
| Axis {axis} is out of bounds for input shape {input_shape}. | validation | error | |
| The mask provided should be one dimension less than the inpu | validation | error | |
| Received an invalid value for argument `groups`, expected a | validation | error | |
| Axis {self.axis} of input tensor should have a defined dimen | exception | error | |
| Number of groups ({self.groups}) cannot be more than the num | exception | error | |
| Number of groups ({self.groups}) must be a multiple of the n | exception | error | |
| Axis {axis} is out of bounds for input shape {input_shape}. | exception | error | |
| Expected an int or a list/tuple of ints for the argument 'ax | exception | error | |
| Duplicate axes are not allowed. Received: axis={self.axis} | exception | error | |
| Axis {axis} is out of bounds for input shape {input_shape}. | exception | error | |
| `power_iterations` should be greater than zero. Received: `p | exception | error | |
| {type(self.layer).__name__} object has no attribute 'kernel' | exception | error | |
| Invalid value for `axis` argument: expected an int or a list | exception | error | |
| Axis {self.axis} is out of bounds for input shape {input_sha | exception | error | |
| For 1D input, `output_size` tuple must have length 1. Receiv | exception | error | |
| `output_size` must be an integer or tuple of 1 integer. Rece | exception | error | |
| `output_size` must be an integer or (height, width) tuple. R | exception | error | |
| `output_size` must be an integer or (depth, height, width) t | exception | error | |
| For 1D input, `output_size` tuple must have length 1. Receiv | exception | error | |
| `output_size` must be an integer or tuple of 1 integer. Rece | exception | error | |
| `output_size` must be an integer or (height, width) tuple. R | exception | error | |
| `output_size` must be an integer or (depth, height, width) t | exception | error | |
| Invalid data_format: {self.data_format}. Expected 'channels_ | exception | error | |
| `pool_mode` must be either 'max' or 'average'. Received: {se | validation | error | |
| Unknown arg for output_mode: {output_mode} | validation | error | |
| num_tokens must be set to use this layer. If the number of t | validation | error | |
| `num_tokens` must be >= 1. Received: num_tokens={num_tokens} | validation | error | |
| `count_weights` is not used when `output_mode` is not `'coun | validation | error | |
| The `seed` and `generator` variable must be set in the `__in | validation | error | |
| `sparse=True` cannot be used with backend {backend.backend() | validation | error | |
| `sparse=True` may only be used if `output_mode` is `'one_hot | validation | error | |
| `num_bins` must be greater than or equal to 0. Received: `nu | validation | error | |
| Both `num_bins` and `bin_boundaries` should not be set. Rece | validation | error | |
| You need to set either `num_bins` or `bin_boundaries`. | validation | error | |
| Cannot adapt a Discretization layer that has been initialize | validation | error | |
| You need to either pass the `bin_boundaries` argument at con | validation | error | |
| Invalid value for argument `output_mode`. Expected one of {' | validation | error | |
| Invalid value for argument `output_mode`. Expected one of {' | validation | error | |
| The `features` argument cannot be None or empty. | validation | error | |
| When specifying `crosses`, the argument `crossing_dim` (dime | validation | error | |
| All features referenced in the `crosses` argument should be | validation | error | |
| Invalid value for argument `output_mode`. Expected one of {' | validation | error | |
| Invalid feature type: {feature} | validation | error | |
| `adapt()` can only be called on a tf.data.Dataset or a dict | validation | error | keras, preprocessing, feature-space, input-type |
| Feature '{name}' has `output_mode='one_hot'`. Thus its prepr | validation | error | keras, feature-space, one-hot, dtype |
| Feature '{name}' has `output_mode='one_hot'`. However it isn | validation | error | keras, feature-space, one-hot, cardinality |
| Cannot concatenate features because feature '{name}' has not | validation | error | keras, feature-space, concat, encoding |
| You need to call `.adapt(dataset)` on the FeatureSpace befor | validation | error | keras, feature-space, adapt, state |
| A FeatureSpace can only be called with a dict. Received: dat | validation | error | keras, feature-space, input-type, dict |
| Expected rebatched data to have batch size 1. Received: shap | validation | error | keras, feature-space, batch-size, shape |
| Layer HashedCrossing requires TensorFlow. Install it via `pi | exception | error | keras, hashed-crossing, tensorflow, import-error |
| `sparse=True` can only be used with the TensorFlow backend. | validation | error | keras, hashed-crossing, sparse, backend |
| Expected as input a list/tuple of 2 tensors. Received input_ | validation | error | keras, hashed-crossing, shape, input-validation |
| Expected the two input tensors to have identical shapes. Rec | validation | error | keras, hashed-crossing, shape |
| `HashedCrossing` should be called on a list or tuple of inpu | validation | error | keras, hashed-crossing, input-type |
| `HashedCrossing` should be called on at least two inputs. Re | validation | error | keras, hashed-crossing, input-count |
| All `HashedCrossing` inputs should have shape `()`, `(batch_ | validation | error | keras, hashed-crossing, shape |
| All `HashedCrossing` inputs should have equal shape. Receive | validation | error | keras, hashed-crossing, shape |
| All `HashedCrossing` inputs should be dense tensors. Receive | validation | error | keras, hashed-crossing, sparse-tensor, ragged |
| All `HashedCrossing` inputs should have an integer or string | validation | error | keras, hashed-crossing, dtype |
| Layer Hashing requires TensorFlow. Install it via `pip insta | exception | error | keras, hashing, tensorflow, import-error |
| The `num_bins` for `Hashing` cannot be `None` or non-positiv | validation | error | keras, hashing, num-bins, argument-validation |
| When `output_mode="int"`, `dtype` should be an integer type, | validation | error | keras, hashing, dtype, output-mode |
| Invalid value for argument `output_mode`. Expected one of {a | validation | error | keras, preprocessing, hashing, invalid-argument |
| `sparse` may only be true if `output_mode` is `"one_hot"`, ` | validation | error | keras, preprocessing, hashing, sparse, invalid-argument |
| The `salt` argument for `Hashing` can only be a tuple of siz | validation | error | keras, preprocessing, hashing, salt, invalid-argument |
| {self._VALUE_RANGE_VALIDATION_ERROR}Received: value_range={v | validation | error | keras, image-preprocessing, auto-contrast, invalid-argument |
| The `value_range` argument should be a list of two numbers. | validation | error | keras, image-preprocessing, auto-contrast, invalid-argument |
| Layer {self.__class__.__name__} does not take a `factor` arg | validation | error | keras, image-preprocessing, invalid-argument, config |
| The `factor` argument should be a number (or a list of two n | validation | error | keras, image-preprocessing, factor, invalid-argument |
| `height` and `width` must be set if `format='xyxy'`. | validation | error | keras, bounding-boxes, clip, missing-argument |
| `variance` must be length 4, got {variance} | validation | error | keras, bounding-boxes, encoding, variance |
| `encoding_format` should be one of 'center_xywh' or 'center_ | validation | error | keras, bounding-boxes, encoding, invalid-enum-argument |
| `encoded_format` should be 'center_xywh' or 'center_yxhw', b | validation | error | keras, bounding-boxes, decoding, invalid-enum-argument |
| compute_iou() expects boxes1 to be batched, or to be unbatch | validation | error | keras, bounding-boxes, iou, rank-error |
| compute_iou() expects boxes2 to be batched, or to be unbatch | validation | error | keras, bounding-boxes, iou, rank-error |
| When using relative bounding box formats (e.g. `rel_yxyx`) t | validation | error | keras, bounding-boxes, iou, relative-coordinates, missing-argument |
| If providing `bounding_boxes['labels']` as a list, it should | validation | error | keras, bounding-boxes, labels, densify, type-error |
| Expected `bounding_boxes` agurment to be a dict with keys 'b | validation | error | keras, bounding-boxes, validation, missing-key |
| If `bounding_boxes['boxes']` is a list, then `bounding_boxes | validation | error | keras, bounding-boxes, input-validation, object-detection |
| If `bounding_boxes['boxes']` and `bounding_boxes['labels']` | validation | error | keras, bounding-boxes, length-mismatch, batch-data |
| If `bounding_boxes['boxes']` is a Ragged tensor, `bounding_ | validation | error | keras, ragged-tensor, bounding-boxes, tensorflow |
| Found bounding_boxes['boxes'].shape={boxes_shape} and expect | validation | error | keras, bounding-boxes, tensor-rank, input-validation |
| Found bounding_boxes['boxes'].shape={boxes_shape} and expect | validation | error | keras, bounding-boxes, tensor-rank, batching |
| Expected `bounding_boxes['boxes']` to have rank 2 or 3, with | validation | error | keras, bounding-boxes, tensor-rank, input-validation |
| `input_shape` must be a non-nested tuple or list of rank-1 w | validation | error | keras, compute-output-shape, center-crop, model-building |
| self._VALUE_RANGE_VALIDATION_ERROR + f"Received: value_range | validation | error | keras, equalization, value-range, constructor-validation |
| There are unsupported keys in `bounding_boxes`: {list(extra_ | validation | error | keras, bounding-boxes, dict-keys, preprocessing |
| self._VALUE_RANGE_VALIDATION_ERROR + f"Received: value_range | validation | error | keras, random-brightness, value-range, constructor-validation |
| Expected the input image to be rank 3 or 4. Received inputs. | validation | error | keras, random-brightness, tensor-rank, image-preprocessing |
| self._VALUE_RANGE_VALIDATION_ERROR + f"Received: value_range | validation | error | keras, color-degeneration, value-range, constructor-validation |
| Expected the input image to be rank 3 or 4. Received inputs. | validation | error | keras, color-degeneration, tensor-rank, image-preprocessing |
| Invalid images rank: expected rank 3 (single image) or rank | validation | error | keras, color-degeneration, compute-output-shape, tensor-rank |
| Input images must have 3 channels, but received images with | validation | error | keras, color-degeneration, channels, compute-output-shape |
| Expected the input image to be rank 3 or 4. Received inputs. | validation | error | keras, random-contrast, tensor-rank, image-preprocessing |
| RandomCrop requires the input to have a fully defined height | validation | error | keras, random-crop, dynamic-shape, image-preprocessing |
| Unknown `interpolation` {interpolation}. Expected of one {se | exception | error | keras, preprocessing, argument-validation, enum |
| Unknown `fill_mode` {fill_mode}. Expected of one {self._SUPP | exception | error | keras, preprocessing, argument-validation, enum |
| The `{name}` argument should be a number (or a list of two n | validation | error | keras, preprocessing, argument-validation, factor-range |
| The `{name}` argument should be a number (or a list of two n | exception | error | keras, preprocessing, argument-validation, factor-range |
| The `fill_value` argument should be a number (or a list of t | exception | error | keras, preprocessing, argument-validation, shape |
| Expected the input image to be rank 3 or 4. Received inputs. | exception | error | keras, preprocessing, tensor-shape, rank-validation |
| The `{name}` argument should be a number (or a list of two n | exception | error | keras, preprocessing, argument-validation, kernel-size |
| {name} must be an odd number. Received: {name}={factor} | exception | error | keras, preprocessing, argument-validation, kernel-size |
| The `{name}` argument should be a number (or a list of two n | exception | error | keras, preprocessing, argument-validation, factor-range |
| Expected the input image to be rank 3 or 4. Received inputs. | exception | error | |
| `factor` should be between 0 and 1. Received: factor={factor | exception | error | |
| Expected the input image to be rank 3 or 4. Received inputs. | exception | error | |
| Expected the input image to be rank 3 or 4. Received inputs. | validation | error | |
| The `scale` argument should be a number in the range [0,1]. | validation | error | |
| Unknown `interpolation` {interpolation}. Expected of one {se | exception | error | |
| The `value_range` argument should be a list of two numbers. | validation | error | |
| Expected the input image to be rank 3 or 4. Received: inputs | validation | error | |
| Unknown `fill_mode` {fill_mode}. Expected of one {self._SUPP | exception | error | |
| Unknown `interpolation` {interpolation}. Expected of one {se | exception | error | |
| The `value_range` argument should be a list of two numbers. | validation | error | |
| Expected the input image to be rank 3 or 4. Received: inputs | validation | error | |
| The `value_range` argument should be a list of two numbers. | validation | error | |
| Expected the input image to be rank 3 or 4. Received: inputs | validation | error | |
| Unknown `fill_mode` {fill_mode}. Expected of one {self._SUPP | exception | error | |
| Unknown `interpolation` {interpolation}. Expected of one {se | exception | error | |
| The `factor` argument should be a number (or a list of two n | validation | error | keras, preprocessing, augmentation, validation |
| The `factor` argument should be a number (or a list of two n | validation | error | keras, preprocessing, range-validation, augmentation |
| Unknown `fill_mode` {fill_mode}. Expected of one {self._SUPP | exception | error | keras, preprocessing, fill-mode, augmentation |
| Unknown `interpolation` {interpolation}. Expected of one {se | exception | error | keras, preprocessing, interpolation, augmentation |
| Received: {factor_name}={factor} | validation | error | keras, preprocessing, validation, augmentation |
| Received: input_number={input_number} | validation | error | keras, preprocessing, range-validation, augmentation |
| Unknown `fill_mode` {fill_mode}. Expected of one {self._SUPP | exception | error | keras, preprocessing, fill-mode, augmentation |
| Unknown `interpolation` {interpolation}. Expected of one {se | exception | error | keras, preprocessing, interpolation, augmentation |
| Received: {factor_name}={factor} | validation | error | keras, preprocessing, validation, augmentation |
| Received: input_number={input_number} | validation | error | keras, preprocessing, range-validation, augmentation |
| Received: value_range={value_range} | validation | error | keras, preprocessing, validation, augmentation |
| Received: {factor_name}={factor} | validation | error | keras, preprocessing, validation, augmentation |
| Received: input_number={input_number} | validation | error | keras, preprocessing, range-validation, augmentation |
| If set, `max_tokens` must be greater than 1. Received: max_t | validation | error | keras, preprocessing, index-lookup, validation |
| If pad_to_max_tokens is True, must set `max_tokens`. Receive | validation | error | keras, preprocessing, index-lookup, validation |
| `num_oov_indices` must be greater than or equal to 0. Receiv | validation | error | keras, preprocessing, index-lookup, argument-validation |
| `salt` can only be used when `oov_method='farmhash'`. Receiv | validation | error | keras, preprocessing, index-lookup, hashing, argument-validation |
| The `salt` argument for `IndexLookup` can only be a tuple of | validation | error | keras, preprocessing, index-lookup, argument-validation, type-error |
| `output_mode` must be `'int'` when `invert` is true. Receive | validation | error | keras, preprocessing, index-lookup, argument-validation |
| `sparse` may only be true if `output_mode` is `'one_hot'`, ` | validation | error | keras, preprocessing, index-lookup, argument-validation, sparse |
| `idf_weights` should only be set if `output_mode` is `'tf_id | validation | error | keras, preprocessing, index-lookup, tf-idf, argument-validation |
| Unrecognized keyword argument(s): {kwargs} | validation | error | keras, preprocessing, index-lookup, api-mismatch, typo |
| When specifying the `vocabulary` argument, in TF-IDF output | validation | error | keras, preprocessing, index-lookup, tf-idf, missing-argument |
| `idf_weights` must be set if output_mode is 'tf_idf'. | validation | error | keras, preprocessing, index-lookup, tf-idf, missing-argument |
| `idf_weights` should only be set if output_mode is `'tf_idf' | validation | error | keras, preprocessing, index-lookup, tf-idf, argument-validation |
| Requested the loading of a vocabulary file outside of the mo | validation | error | keras, preprocessing, index-lookup, deserialization, security |
| Vocabulary file {vocabulary} does not exist. | validation | error | keras, preprocessing, index-lookup, file-path, vocabulary |
| output_mode `'tf_idf'` does not support loading a vocabulary | validation | error | keras, preprocessing, index-lookup, tf-idf, unsupported-operation |
| Cannot set a tensor vocabulary on layer {self.name} when not | exception | error | keras, preprocessing, index-lookup, eager-execution, tf-function |
| Cannot set an empty vocabulary. Received: vocabulary={vocabu | validation | error | keras, preprocessing, index-lookup, vocabulary, empty-input |
| The passed vocabulary has at least one repeated term. Please | validation | error | keras, preprocessing, index-lookup, vocabulary, duplicates |
| Found reserved mask token at unexpected location in `vocabul | validation | error | keras, preprocessing, index-lookup, vocabulary, special-tokens |
| Found reserved OOV token at unexpected location in `vocabula | validation | error | keras, preprocessing, index-lookup, vocabulary, special-tokens |
| Attempted to set a vocabulary larger than the maximum vocab | validation | error | keras, preprocessing, index-lookup, vocabulary, size-limit |
| `idf_weights` must be the same length as vocabulary. len(idf | validation | error | keras, preprocessing, index-lookup, tf-idf, length-mismatch |
| TF-IDF data must be a 1-index array. Received: type(idf_weig | validation | error | keras, preprocessing, tf-idf, shape-validation |
| Cannot adapt layer '{self.name}' after setting a static voca | validation | error | keras, adapt, vocabulary, state-error |
| When `output_mode` is `'tf_idf'`, `idf_weights` must be prov | validation | error | keras, tf-idf, missing-argument, preprocessing |
| When using `output_mode={self.output_mode}` and `pad_to_max_ | exception | error | keras, preprocessing, vocabulary, runtime-error |
| When using `output_mode={self.output_mode}` and `pad_to_max_ | exception | error | keras, vocabulary, frozen-state, shape-validation |
| Layer IntegerLookup requires TensorFlow. Install it via `pip | exception | critical | keras, dependency, tensorflow, import-error |
| If `max_tokens` is set for `IntegerLookup`, it must be great | validation | error | keras, argument-validation, integer-lookup |
| The value of `num_oov_indices` argument for `IntegerLookup` | validation | error | keras, argument-validation, integer-lookup |
| `sparse=True` can only be used with the TensorFlow backend. | validation | error | keras, backend-mismatch, sparse, preprocessing |
| Only `vocabulary_dtype='int64'` is supported at this time. R | validation | error | keras, dtype, integer-lookup, argument-validation |
| `fft_length` must be greater than or equal to `sequence_len | validation | error | keras, audio, argument-validation, mel-spectrogram |
| When setting values directly, both `mean` and `variance` mus | validation | error | keras, normalization, argument-validation |
| When setting values directly, `mean` and `variance` must hav | validation | error | keras, normalization, shape-validation |
| The rank of `mean` must be less than or equal to the number | validation | error | keras, normalization, rank-validation |
| All `axis` values must be in the range [-ndim, ndim). Receiv | validation | error | keras, normalization, axis-validation, build |
| All `axis` values to be kept must have a known shape. Receiv | validation | error | keras, normalization, dynamic-shape, build |
| adapt() received an empty iterable (no batches). Expected at | validation | error | keras, normalization, adapt, empty-data |
| adapt() expects an iterable that yields arrays or tensors wi | validation | error | keras, normalization, adapt, type-validation |
| Unsupported data type: {type(data)}. `adapt` supports `np.nd | validation | error | keras, normalization, adapt, type-validation |
| The layer was built with input_shape={self._build_input_shap | validation | error | keras, normalization, adapt, shape-validation |
| adapt() yielded a batch with incompatible shape. Expected {s | validation | error | |
| Unsupported data type: {type(data)} | exception | error | |
| You must call `.build(input_shape)` on the layer before usin | validation | error | |
| `Pipeline` config must contain a `layers` key mapping to a l | validation | error | |
| `frame_step` should be a positive integer not greater than ` | validation | error | |
| `fft_length` should be not less than `frame_length`. Receive | validation | error | |
| Output mode is invalid, it must be one of {', '.join(all_mod | validation | error | |
| Scaling is invalid, it must be `None`, 'density' or 'spectru | validation | error | |
| Padding is invalid, it should be 'valid', 'same'. Received: | validation | error | |
| Invalid input type. Expected `float16`, `float32` or `float6 | validation | error | |
| Layer StringLookup requires TensorFlow. Install it via `pip | exception | error | |
| `sparse=True` can only be used with the TensorFlow backend. | validation | error | |
| Layer TextVectorization requires TensorFlow. Install it via | exception | error | |
| `sparse=True` can only be used with the TensorFlow backend. | validation | error | |
| `ragged=True` can only be used with the TensorFlow backend. | validation | error | |
| `ngrams` must be None, an integer, or a tuple of integers. R | validation | error | |
| `output_sequence_length` must be either None or an integer w | validation | error | |
| `output_sequence_length` must not be set if `output_mode` is | validation | error | |
| `ragged` must not be true if `output_mode` is `'int'`. Recei | validation | error | |
| When using `TextVectorization` to tokenize strings, the inpu | validation | error | |
| Invalid value received for argument `rate`. Expected a float | validation | error | |
| Invalid value received for argument `rate`. Expected a float | validation | error | |
| Invalid value received for argument `noise_shape`. Expected | validation | error | |
| Invalid value received for argument `noise_shape`. Expected | validation | error | |
| Invalid value received for argument `noise_shape`. Expected | validation | error | |
| Invalid value received for argument `noise_shape`. Expected | validation | error | |
| Invalid value received for argument `rate`. Expected a float | validation | error | |
| Invalid value received for argument `stddev`. Expected a flo | validation | error | |
| `cropping` parameter of `Cropping1D` layer must be smaller t | validation | error | |
| `cropping` parameter of `Cropping1D` layer must be smaller t | validation | error | |
| `cropping` cannot be negative. Received: cropping={cropping} | validation | error | |
| `cropping` should have two elements. Received: cropping={cro | validation | error | |
| `cropping` should be either an int, a tuple of 2 ints (symme | validation | error | |
| Values in `cropping` argument should be smaller than the cor | validation | error | |
| Values in `cropping` argument should be smaller than the cor | validation | error | |
| `cropping` cannot be negative. Received: cropping={cropping} | validation | error | |
| `cropping` should have 3 elements. Received: {cropping}. | validation | error | |
| `cropping` should be either an int, a tuple of 3 ints (symme | validation | error | |
| Values in `cropping` argument should be smaller than the cor | validation | error | keras, cropping3d, shape-validation, reshaping-layer |
| Values in `cropping` argument should be smaller than the cor | validation | error | keras, cropping3d, runtime-shape-check, reshaping-layer |
| Invalid permutation argument `dims` for Permute Layer. The s | validation | error | keras, permute, argument-validation, reshaping-layer |
| Expected an integer value for `n`, got {type(n)}. | validation | error | keras, repeat-vector, type-validation, argument-validation |
| Argument `n` should be a positive integer. Received: n={n} | validation | error | keras, repeat-vector, argument-validation, value-validation |
| Expected an integer value for `size`, got {type(size)}. | validation | error | keras, upsampling1d, type-validation, argument-validation |
| Argument `size` should be a positive integer. Received: size | validation | error | keras, upsampling1d, argument-validation, value-validation |
| Invalid `data_format` argument: {data_format} | validation | error | keras, upsampling2d, data-format, argument-validation |
| Invalid data_format: {data_format} | validation | error | keras, upsampling3d, data-format, argument-validation |
| `padding` should have two elements. Received: padding={paddi | validation | error | keras, zero-padding2d, argument-validation, padding |
| `padding` should be either an int, a tuple of 2 ints (symmet | validation | error | |
| `padding` should have 3 elements. Received: {padding}. | validation | error | |
| `padding` should be either an int, a tuple of 3 ints (symmet | validation | error | |
| Please initialize `Bidirectional` layer with a `keras.layers | validation | error | |
| `backward_layer` need to be a `keras.layers.Layer` instance. | validation | error | |
| Invalid merge mode. Received: {merge_mode}. Merge mode shoul | validation | error | |
| Forward layer and backward layer should have different `go_b | validation | error | |
| Forward layer and backward layer are expected to have the sa | validation | error | |
| Unrecognized value for `merge_mode`. Received: {self.merge_m | validation | error | |
| Unrecognized value for `merge_mode`. Received: {self.merge_m | validation | error | |
| Layer must be stateful. | exception | error | |
| Rank {rank} convolutions are not currently implemented. Rece | validation | error | |
| Specifying `strides > 1` is not compatible with `dilation_ra | validation | error | |
| ConvLSTM layers only support static input shapes for the spa | exception | error | |
| The channel dimension of the inputs (last axis) should be de | exception | error | |
| Received an invalid value for argument `units`, expected a p | exception | error | |
| Invalid valid received for argument `use_cudnn`. Expected on | exception | error | |
| use_cudnn=True was specified, but cuDNN is not supported for | exception | error | |
| Received an invalid value for argument `units`, expected a p | exception | error | |
| Invalid valid received for argument `use_cudnn`. Expected on | exception | error | |
| use_cudnn=True was specified, but cuDNN is not supported for | exception | error | |
| Argument `cell` should have a `call` method. Received: cell= | exception | error | |
| The RNN cell should have a `state_size` attribute (single in | exception | error | |
| state_size must be specified as property on the RNN cell. | exception | error | |
| state_size must be an integer, or a list/tuple of integers ( | exception | error | |
| output_size must be an integer. | exception | error | |
| When using `stateful=True` in a RNN, the batch size must be | exception | error | |
| Cannot unroll a RNN if the time dimension is undefined.
- I | exception | error | |
| If an RNN is stateful, the batch size of the input sequences | exception | error | |
| Received an invalid value for argument `units`, expected a p | exception | error | keras, rnn, constructor-validation, units |
| All cells must have a `call` method. Received cell without a | exception | error | keras, rnn, stacked-cells, duck-typing |
| All cells must have a `state_size` attribute. Received cell | exception | error | keras, rnn, custom-cell, state-size |
| Please initialize `TimeDistributed` layer with a `keras.laye | exception | error | keras, time-distributed, type-validation, constructor |
| `TimeDistributed` Layer should be passed an `input_shape` wi | exception | error | keras, time-distributed, shape-validation, input-dimensions |
| The `mask` passed to the `TimeDistributed` layer must be at | exception | error | keras, time-distributed, masking, shape-validation |
| The `mask` passed to the `TimeDistributed` layer has a shape | exception | error | keras, time-distributed, masking, dimension-mismatch |
| Cannot do batch_dot on inputs with rank < 2. Received inputs | exception | error | keras, batch-dot, tensor-rank, legacy-backend |
| Cannot do batch_dot on inputs with different batch sizes. Re | exception | error | keras, batch-dot, batch-size-mismatch, legacy-backend |
| Multiple target dimensions are not supported. Expected: None | exception | error | keras, batch-dot, axes-validation, legacy-backend |
| Cannot perform batch_dot over axis 0. If your inputs are not | exception | error | keras, tensorflow, batch-dot, shape-mismatch |
| Cannot do batch_dot on inputs with tf.shapes {x_shape} and { | exception | error | keras, tensorflow, batch-dot, dimension-mismatch |
| Unknown data_format: {data_format} | exception | error | keras, tensorflow, data-format, argument-validation |
| Unexpected bias dimensions {len(bias_shape)}. Expected it to | exception | error | keras, tensorflow, bias-add, rank-mismatch |
| Invalid padding: {padding} | exception | error | keras, tensorflow, convolution, padding, argument-validation |
| Expected the 2 dimensions of the `dilation_rate` argument to | exception | error | keras, tensorflow, conv2d-transpose, dilation, argument-validation |
| `pool_size` must be a tuple of 2 integers. | exception | error | |
| `strides` must be a tuple of 2 integers. | exception | error | |
| Invalid pooling mode: {str(pool_mode)} | exception | error | |
| Expected input `x` to have rank 2. Received: rank(x)={ndim(x | exception | error | |
| Invalid `data_format` argument: {data_format} | exception | error | |
| `interpolation` argument should be one of: "{interploations_ | exception | error | |
| Invalid data_format: {data_format} | exception | error | |
| mask_t is expected to be tensor, but got {mask_t} | exception | error | |
| input_t is expected to be tensor, but got {input_t} | exception | error | |
| Unrolling requires a fixed number of timesteps. | exception | error | |
| Cannot apply softmax to a tensor that is 1D. Received input: | exception | error | |
| Cannot compute sparse categorical crossentropy with `axis={} | exception | error | |
| Expected `padding` to be a tuple of 2 tuples of 2 integers. | exception | error | |
| Expected `padding` to be a tuple of 3 tuples of 2 integers. | exception | error | keras, padding, argument-validation, legacy, conv3d |
| Rank of `condition` should be less than or equal to rank of | exception | error | keras, broadcasting, rank-mismatch, legacy, switch |
| Expected `padding` to be a tuple of 2 integers. Received: pa | exception | error | keras, padding, legacy, argument-validation, time-series |
| `factor` argument cannot have an upper bound lesser than the | validation | error | keras, data-augmentation, argument-validation, legacy, random-height |
| `factor` argument must have values larger than -1. Received: | validation | error | keras, data-augmentation, argument-validation, legacy, random-height |
| `factor` argument cannot have an upper bound less than the l | validation | error | keras, data-augmentation, argument-validation, legacy, random-width |
| Theta of a Thresholded ReLU layer cannot be None, expecting | validation | error | keras, activation, argument-validation, legacy, relu |
| The theta value of a Thresholded ReLU layer should be >=0. R | validation | error | keras, activation, argument-validation, legacy, relu |
| Invalid Reduction Key: {key}. Expected keys are "{cls.all()} | validation | error | keras, losses, reduction, invalid-argument |
| Asked to retrieve element {idx}, but the Sequence has length | validation | error | keras, sequence, index-error, out-of-range |
| Invalid color mode: {color_mode}; expected "rgb", "rgba", or | validation | error | keras, image-data-generator, color-mode, invalid-argument |
| Invalid subset name: {subset};expected "training" or "valida | validation | error | keras, image-data-generator, subset, invalid-argument |
| `filepaths` property method has not been implemented in {}. | exception | error | keras, iterator, not-implemented, abstract-method |
| `labels` property method has not been implemented in {}. | exception | error | keras, iterator, not-implemented, abstract-method |
| `sample_weight` property method has not been implemented in | exception | error | keras, iterator, not-implemented, abstract-method, sample-weights |
| Invalid class_mode: {}; expected one of: {} | validation | error | keras, directory-iterator, class-mode, invalid-argument |
| All of the arrays in `x` should have the same length. Found | validation | error | keras, numpy-array-iterator, shape-mismatch, multi-input |
| `x` (images tensor) and `y` (labels) should have the same le | validation | error | keras, numpy-array-iterator, shape-mismatch, labels |
| `x` (images tensor) and `sample_weight` should have the same | validation | error | keras, data-augmentation, shape-mismatch |
| Invalid subset name: {subset}; expected "training" or "valid | validation | error | keras, validation-split, argument-validation |
| Training and validation subsets have different number of cla | validation | error | keras, train-validation-split, class-imbalance |
| Input data in `NumpyArrayIterator` should have rank 4. You p | exception | error | keras, numpy, tensor-rank |
| If class_mode="{}", y_col must be a list. Received {}. | exception | error | keras, dataframe, multi-output |
| All values in column x_col={x_col} must be strings. | exception | error | keras, dataframe, type-validation |
| If class_mode="{}", y_col="{}" column values must be strings | exception | error | keras, dataframe, type-validation |
| If class_mode="binary" there must be 2 classes. {} class/es | exception | error | keras, binary-classification, argument-validation |
| If class_mode="binary" there must be 2 classes. Found {} cla | exception | error | keras, binary-classification, argument-validation |
| If class_mode="{}", y_col="{}" column values must be type st | exception | error | keras, dataframe, type-validation |
| Column weight_col={weight_col} must be numeric. | exception | error | keras, dataframe, dtype-validation |
| Expect string, list or tuple but found {} in {} column | exception | error | keras, dataframe, type-validation |
| `data_format` should be `"channels_last"` (channel after row | exception | error | keras, data-format, argument-validation |
| `validation_split` must be strictly between 0 and 1. Receiv | exception | error | keras, validation-split, range-validation |
| `zoom_range` should be a float or a tuple or list of two flo | exception | error | keras, data-augmentation, argument-validation |
| `brightness_range should be tuple or list of two floats. Rec | exception | error | keras, data-augmentation, argument-validation |
| Input to `.fit()` should have rank 4. Got array with shape: | exception | error | keras, numpy, tensor-rank |
| `zoom_range` should be a tuple or list of two floats. Receiv | exception | warning | keras, deprecated, data-augmentation |
| 'row_axis', 'col_axis', and 'channel_axis' must be distinct | exception | error | keras, preprocessing, image, validation |
| Invalid axis' indices: {actual_indices - valid_indices} | exception | error | keras, preprocessing, image, validation, negative-index |
| Input arrays must be multi-channel 2D images. | exception | error | keras, preprocessing, image, tensor-shape |
| Channels are allowed and the first and last dimensions. | exception | error | keras, preprocessing, image, axis-order |
| Data and targets have to be of same length. Data length is { | exception | error | keras, timeseries, preprocessing, validation |
| `start_index+length={self.start_index} > end_index={self.end | exception | error | keras, timeseries, preprocessing, index-bounds |
| Data not JSON Serializable: {data} | exception | error | keras, serialization, json, timeseries |
| Targets not JSON Serializable: {targets} | exception | error | keras, serialization, json, timeseries |
| Unrecognized keyword arguments: {str(kwargs)} | exception | error | keras, text, tokenizer, validation |
| Specify a dimension (`num_words` argument), or fit on some t | exception | error | keras, text, tokenizer, fit-before-transform |
| Fit the Tokenizer on some data before using tfidf mode. | exception | error | keras, text, tokenizer, tfidf |
| Unknown vectorization mode: | exception | error | keras, text, tokenizer, mode |
| Unable to serialize {obj} to JSON, because the TypeSpec clas | exception | error | keras, serialization, json, tensorflow, typespec |
| Unable to serialize {obj} to JSON. Unrecognized type {type(o | exception | error | keras, serialization, json, custom-layers |
| `save_model()` using h5 format requires h5py. Could not impo | exception | error | keras, saving, hdf5, dependency |
| `load_model()` using h5 format requires h5py. Could not impo | exception | error | keras, loading, hdf5, dependency |
| No model config found in the file at {filepath}. | exception | error | keras, loading, hdf5, model-config |
| The following attributes cannot be saved to HDF5 file becaus | exception | error | keras, saving, hdf5, attribute-limit |
| Layer count mismatch when loading weights from file. Model e | exception | error | keras, loading, hdf5, weights, architecture-mismatch |
| Weight count mismatch for layer #{k} (named {layer.name} in | exception | error | keras, loading, hdf5, weights, layer-mismatch |
| Weight count mismatch for top-level weights when loading wei | exception | error | |
| Shape mismatch in {name}for weight {symbolic_weights[i].path | exception | error | |
| Weight count mismatch for layer #{k} (named {layer.name}). L | exception | error | |
| Weight count mismatch for top-level weights of model. Model | exception | error | |
| `model_from_config` expects a dictionary, not a list. Receiv | exception | error | |
| The provided configuration is not a valid Keras configuratio | exception | error | |
| Saved configuration not understood. Configuration should be | exception | error | |
| Cannot serialize {instance} because it doesn't implement `ge | exception | error | |
| Improper config format for {config}. Expecting python dict c | exception | error | |
| Unknown {printable_module_name}: '{class_name}'. Please ensu | exception | error | |
| Unknown {printable_module_name}: '{object_name}'. Please ens | exception | error | |
| Could not interpret serialized {printable_module_name}: {ide | exception | error | |
| Could not interpret loss identifier: {identifier} | exception | error | |
| Invalid value for argument `reduction`. Expected one of {all | exception | error | |
| q must be in the interval (0, 1) | exception | error | |
| `axis` must be of type `int`. Received: axis={axis} of type | exception | error | |
| Targets `y_true` are expected to be a tensor of shape `(batc | exception | error | |
| Logits `y_pred` are expected to be a tensor of shape `(batch | exception | error | |
| Could not interpret metric identifier: {identifier} | exception | error | |
| Argument `num_thresholds` must be an integer > 0. Received: | exception | error | keras, metrics, num-thresholds |
| Argument `specificity` must be in the range [0, 1]. Received | exception | error | keras, metrics, specificity, range-validation |
| Argument `sensitivity` must be in the range [0, 1]. Received | exception | error | keras, metrics, sensitivity, range-validation |
| Argument `recall` must be in the range [0, 1]. Received: rec | exception | error | keras, metrics, recall, range-validation |
| Argument `precision` must be in the range [0, 1]. Received: | exception | error | keras, metrics, precision, range-validation |
| Invalid `curve` argument value "{curve}". Expected one of: { | exception | error | keras, metrics, auc, enum-validation |
| Invalid `summation_method` argument value "{summation_method | exception | error | keras, metrics, auc, enum-validation |
| Argument `num_thresholds` must be an integer > 1. Received: | exception | error | keras, metrics, auc, num-thresholds |
| `num_labels` is needed only when `multi_label` is True. | exception | error | keras, metrics, auc, multi-label, argument-misuse |
| `y_pred` must have rank 2 when `multi_label=True`. Found ran | exception | error | keras, metrics, auc, shape-validation, multi-label |
| Invalid `average` argument value. Expected one of: {None, 'm | exception | error | keras, metrics, fbeta, argument-validation |
| Invalid `beta` argument value. It should be a Python float. | exception | error | keras, metrics, fbeta, type-validation |
| Invalid `beta` argument value. It should be > 0. Received: b | exception | error | keras, metrics, fbeta, range-validation |
| Invalid `threshold` argument value. It should be a Python fl | exception | error | keras, metrics, fbeta, threshold, type-validation |
| Invalid `threshold` argument value. It should verify 0 < thr | exception | error | keras, metrics, fbeta, threshold, range-validation |
| FBetaScore expects 2D inputs with shape (batch_size, output_ | exception | error | keras, metrics, fbeta, shape-validation |
| FBetaScore expects 2D inputs with shape (batch_size, output_ | exception | error | keras, metrics, fbeta, shape-validation, static-shape |
| Target class id {max(target_class_ids)} is out of range, whi | exception | error | keras, metrics, iou, segmentation, off-by-one |
| Argument `metric_variables` must be a list of tensors corres | exception | error | keras, metrics, stateless-api, variables |
| You forgot to call `super().__init__()` in the `__init__()` | exception | error | keras, metrics, custom-metric, init, subclassing |
| Threshold values must be in [0, 1]. Received: {invalid_thres | validation | error | keras, metrics, thresholds, validation, valueerror |
| Invalid AUC curve value: "{key}". Expected values are ["PR", | validation | error | keras, metrics, auc, enum, invalid-argument |
| Invalid AUC summation method value: "{key}". Expected values | validation | error | keras, metrics, auc, enum, invalid-argument |
| `label_weights` for multilabel data should be handled outsid | validation | error | keras, metrics, confusion-matrix, multilabel, internal-api |
| Please provide at least one valid confusion matrix variable | validation | error | keras, metrics, confusion-matrix, dict-keys, internal-api |
| Invalid keys: "{invalid_keys}". Valid variable key options a | validation | error | keras, metrics, confusion-matrix, dict-keys, internal-api |
| When class_id is provided, y_pred must be a 2D array with sh | validation | error | keras, metrics, shape-mismatch, class-id, rank |
| Invalid value for argument `class_aggregation`. Expected one | validation | error | keras, metrics, r2-score, invalid-argument, regression |
| Invalid value for argument `num_regressors`. Expected a valu | validation | error | keras, metrics, r2-score, argument-out-of-range, regression |
| R2Score expects 2D inputs with shape (batch_size, output_dim | validation | error | keras, metrics, r2-score, shape-mismatch, rank |
| R2Score expects 2D inputs with shape (batch_size, output_dim | validation | error | keras, metrics, r2-score, dynamic-shape, shape-mismatch |
| Unexpected keyword argument(s): {tuple(kwargs.keys())} | validation | error | keras, models, clone-model, kwargs, api-mismatch |
| `call_function` argument is not supported with Sequential mo | validation | error | keras, models, clone-model, sequential, functional |
| Arguments `clone_function` and `input_tensors` are only supp | validation | error | keras, models, clone-model, subclassed-model, unsupported-argument |
| Argument `call_function` is only supported for Functional mo | validation | error | keras, models, clone-model, functional, unsupported-argument |
| Expected `model` argument to be a `Sequential` model instanc | validation | error | keras, models, clone-model, sequential, internal-api, type-check |
| Expected `clone_function` argument to be a callable. Receive | validation | error | keras, models, clone-model, callable-check, type-check |
| Argument `input_tensors` must contain a single tensor. | validation | error | keras, models, clone-model, sequential, input-tensors |
| Argument `input_tensors` must be a KerasTensor. Received inv | validation | error | keras, models, clone-model, keras-tensor, type-check |
| Expected `model` argument to be a Functional Model instance. | validation | error | |
| All entries in `input_tensors` must be KerasTensors. Receive | validation | error | |
| `input_tensors` must have the same structure as model.input\ | validation | error | |
| All `inputs` values must be KerasTensors. Received: inputs={ | validation | error | |
| All `outputs` values must be KerasTensors. Received: outputs | validation | error | |
| `Model.layers` attribute is reserved and should not be used. | exception | error | |
| The input '{input_tensor.name}' is not optional, but None wa | validation | error | |
| The structure of `inputs` doesn't match the expected structu | validation | error | |
| The input '{input_name}' is not optional, but None was passe | validation | error | |
| Invalid input shape for input {x} with name '{self._inputs[i | validation | error | |
| Unexpected object from deserialization, expected a layer or | validation | error | |
| Invalid Functional model configuration. The graph of the Fun | validation | error | |
| Cannot deserialize the model (invalid config data?) | validation | error | |
| Invalid Functional model configuration. Missing node: {inbou | validation | error | |
| Layer node index out of bounds.\ninbound_layer = {inbound_la | exception | error | |
| Unknown layer: {history[0]} | validation | error | |
| Layer node index out of bounds.
inbound_layer = {layer}
inbo | exception | error | |
| Backend '{backend.backend()}' must implement the Trainer cla | exception | error | |
| Model {self.__class__.__name__} does not have a `call()` met | exception | error | |
| `Model.layers` attribute is reserved and should not be used. | exception | error | |
| Provide only a layer name or a layer index. Received: index= | exception | error | |
| Was asked to retrieve layer at index {index} but model only | exception | error | |
| No such layer: {name}. Existing layers are: {list(layer.name | exception | error | |
| Provide either a layer name or layer index at `get_layer`. | exception | error | |
| Unrecognized keyword arguments passed to {self.__class__.__n | exception | error | |
| The `filters` argument must be a regex string, a list of reg | exception | error | |
| For {mode=}, a valid quantization structure must be provided | exception | error | |
| Unrecognized format={format}. Supported formats are: {list(a | exception | error | |
| LiteRT export requires TensorFlow or PyTorch backend. | exception | error | |
| Torch export requires PyTorch backend. | exception | error | |
| Unable to revive model from config. When overriding the `get | exception | error | |
| The following variable path is found twice in the model: '{v | exception | error | |
| Invalid `value_format` argument. Expected one of {'numpy_arr | exception | error | |
| Unknown variable name: {k} | exception | error | |
| Only instances of `keras.Layer` can be added to a Sequential | exception | error | |
| All layers added to a Sequential model should have unique na | exception | error | |
| Sequential model '{self.name}' has already been configured t | exception | error | |
| Sequential model {self.name} cannot be built because it has | exception | error | |
| Sequential model '{self.name}' has already been configured t | exception | error | keras, sequential, input-shape, build |
| Layers added to a Sequential model should have a single posi | exception | error | keras, sequential, layer, introspection |
| Layers added to a Sequential model can only have a single re | exception | error | keras, sequential, layer, required-arguments |
| `Sequential.layers` attribute is reserved and should not be | exception | error | keras, sequential, attribute, layers |
| Sequential model '{self.name}' has no defined input shape ye | exception | error | keras, sequential, input-shape, lazy-build |
| Sequential model '{self.name}' has no defined output shape y | exception | error | keras, sequential, output-shape, lazy-build |
| Sequential model '{self.name}' has no defined inputs yet. | exception | error | keras, sequential, inputs, lazy-build |
| Sequential model '{self.name}' has no defined outputs yet. | exception | error | keras, sequential, outputs, lazy-build |
| A Sequential model configuration must be a dictionary contai | exception | error | keras, sequential, from-config, serialization |
| The model contains two variables with a duplicate path: path | exception | error | keras, variables, checkpointing, naming |
| Array inputs to associative_scan must have the same first di | exception | error | keras, ops, associative-scan, shape |
| Invalid reduction: {reduction}. Supported values are: None, | exception | error | keras, ops, scatter-update, reduction |
| The number of dimensions in `inputs` must match the number o | exception | error | keras, ops, slice, shape |
| The number of dimensions in `start_indices` must match the n | exception | error | keras, ops, slice, indices |
| Cannot infer argument `num` from shape {x.shape}. Either pro | exception | error | keras, ops, symbolic-tensors, dynamic-shape |
| `true_fn` and `false_fn` should return outputs of the same k | exception | error | keras, ops, cond, shape-mismatch |
| `inputs` argument cannot be empty. Received:
inputs={inputs} | exception | error | keras, ops, function, inputs |
| `outputs` argument cannot be empty. Received:
inputs={inputs | exception | error | keras, ops, function, outputs |
| Output with path `{path}` is not connected to `inputs` | exception | error | keras, ops, function, graph |
| Function was called with an invalid input structure. Expecte | exception | error | keras, ops, function, pytree |
| {self.__class__.__name__} was passed incompatible inputs. Fo | exception | error | keras, shape-mismatch, input-validation, functional |
| Graph disconnected: cannot find parent for tensor {x} at ope | exception | error | keras, graph, disconnected-graph, functional-api |
| The name "{name}" is used {all_names.count(name)} times in t | exception | error | keras, naming, serialization, graph |
| Tensor {tensor} from operation '{operation.name}' is part of | exception | error | keras, graph, cycle, recurrent |
| Invalid images rank: expected rank 3 (single image) or rank | exception | error | keras, image, rank-validation, shape-mismatch |
| Invalid channel size: expected 3 (RGB) or 1 (Grayscale). Rec | exception | error | keras, image, channel-validation, shape-mismatch |
| Invalid images dtype: expected float dtype. Received: images | exception | error | keras, image, dtype-validation, rgb-to-hsv |
| Input images must have 3 channels, but received images with | exception | error | keras, image, channel-validation, rgb-to-hsv |
| Invalid images rank: expected rank 3 (single image) or rank | exception | error | keras, image, rank-validation, crop |
| Expected `size` to be a tuple of 2 integers. Received: size= | exception | error | keras, image, resize, argument-validation |
| `size` must have positive height and width. Received: size={ | exception | error | keras, image, resize, argument-validation |
| Only one of `pad_to_aspect_ratio` & `crop_to_aspect_ratio` c | exception | error | keras, image, resize, argument-validation, config |
| Invalid transform rank: expected rank 1 (single transform) o | exception | error | keras, image, affine-transform, rank-validation |
| Invalid `size` argument. Expected an int or a tuple. Receive | exception | error | |
| Invalid `size` argument. Expected a tuple of length 2 or 3. | exception | error | |
| Invalid `size` argument. Expected an int or a tuple of lengt | exception | error | |
| Invalid `size` argument. Expected an int or a tuple of lengt | exception | error | |
| Invalid `strides` argument. Got: {strides} | exception | error | |
| `patches` has unexpected rank for {'3D' if self.is_3d else ' | exception | error | |
| `patches` last dim ({flat}) is not divisible by prod(size) ( | exception | error | |
| `padding='valid'` requires output_size to equal size * grid. | exception | error | |
| For `padding='same'`, `output_size` {dim_name} ({o}) must be | exception | error | |
| Invalid `output_size` argument. Expected a tuple or list. Re | exception | error | |
| `{fn_name}` currently supports only non-overlapping reconstr | exception | error | |
| Invalid `size`. Expected length 2 for 2D reconstruction. Got | exception | error | |
| Invalid `output_size`. Expected length 2 (H, W). Got: output | exception | error | |
| Invalid `padding`. Expected 'same' or 'valid'. Got: {padding | exception | error | |
| `patches` has unexpected rank for 2D channels_first reconstr | exception | error | |
| `patches` has unexpected rank for 2D reconstruction. Expecte | exception | error | |
| `patches` last dim ({static_flat}) is not divisible by prod( | exception | error | |
| For `padding='same'`, `output_size` height ({H}) must be in | exception | error | |
| For `padding='same'`, `output_size` width ({W}) must be in t | exception | error | keras, image, patches, shape-mismatch, input-validation |
| `padding='valid'` requires output_size to equal size * grid. | exception | error | keras, image, patches, shape-mismatch, input-validation |
| Invalid `output_size`. Expected length 3 (D, H, W). Got: out | exception | error | keras, image, patches, argument-shape, input-validation |
| `patches` has unexpected rank for 3D channels_first reconstr | exception | error | keras, image, patches, data-format, rank-error |
| `patches` has unexpected rank for 3D reconstruction. Expecte | exception | error | keras, image, patches, rank-error, input-validation |
| `patches` last dim ({static_flat}) is not divisible by prod( | exception | error | keras, image, patches, divisibility, shape-mismatch |
| For `padding='same'`, `output_size` depth ({D}) must be in t | exception | error | keras, image, patches, shape-mismatch, input-validation |
| `padding='valid'` requires output_size to equal size * grid. | exception | error | keras, image, patches, shape-mismatch, input-validation |
| First dim of `coordinates` must be the same as the rank of ` | exception | error | keras, image, map-coordinates, shape-mismatch, input-validation |
| Invalid coordinates rank: expected at least rank 2. Received | exception | error | keras, image, map-coordinates, rank-error, input-validation |
| {name} must be >= 0. Received: {name}={value} | exception | error | keras, image, padding, argument-validation |
| Must specify exactly two of top_padding, bottom_padding, tar | exception | error | keras, image, padding, argument-validation |
| Must specify exactly two of left_padding, right_padding, tar | exception | error | keras, image, padding, argument-validation |
| Must specify exactly two of top_cropping, bottom_cropping, t | exception | error | keras, image, cropping, argument-validation |
| Must specify exactly two of left_cropping, right_cropping, t | exception | error | keras, image, cropping, argument-validation |
| top_padding must be >= 0. Received: top_padding={top_padding | exception | error | keras, image, padding, negative-dimension, input-validation |
| bottom_padding must be >= 0. Received: bottom_padding={botto | exception | error | |
| target_height must be >= 0. Received: target_height={target_ | exception | error | |
| left_padding must be >= 0. Received: left_padding={left_padd | exception | error | |
| right_padding must be >= 0. Received: right_padding={right_p | exception | error | |
| target_width must be >= 0. Received: target_width={target_wi | exception | error | |
| When the height of the images is unknown, `target_height` mu | exception | error | |
| When the width of the images is unknown, `target_width` must | exception | error | |
| top_cropping must be >= 0. Received: top_cropping={top_cropp | exception | error | |
| bottom_cropping must be >= 0. Received: bottom_cropping={bot | exception | error | |
| left_cropping must be >= 0. Received: left_cropping={left_cr | exception | error | |
| right_cropping must be >= 0. Received: right_cropping={right | exception | error | |
| Invalid start_points shape: expected (4,2) for a single imag | validation | error | keras, image, perspective, shape-validation |
| Invalid end_points shape: expected (4,2) for a single image | validation | error | keras, image, perspective, shape-validation |
| start_points and end_points must have the same shape. Receiv | validation | error | keras, image, perspective, shape-validation |
| Invalid images rank: expected rank 4 (batch of images). Rece | validation | error | keras, image, sobel, shape-validation |
| Invalid image1 rank: expected rank 3 (single image) or rank | validation | error | keras, image, ssim, shape-validation |
| Invalid image2 rank: expected rank 3 (single image) or rank | validation | error | keras, image, ssim, shape-validation |
| Cholesky decomposition failed: {e} | exception | error | keras, linalg, cholesky, positive-definite |
| Cholesky inverse failed: {e} | exception | error | keras, linalg, cholesky, matrix-inverse |
| LU decomposition failed: {e}. LU decomposition is only suppo | validation | error | keras, linalg, lu, tensorflow, backend-specific |
| Invalid `ord` argument. Expected one of {'fro', 'nuc'} when | validation | error | keras, linalg, norm, argument-validation |
| Invalid `ord` argument for vector norm. Received: ord={self. | validation | error | keras, linalg, norm, argument-validation |
| Invalid `ord` argument for matrix norm. Received: ord={self. | validation | error | |
| `mode` argument value not supported. Expected one of {'reduc | validation | error | |
| Input should have rank >= 2. Received: input.shape = {x.shap | validation | error | |
| Input should have its last 2 dimensions fully-defined. Recei | validation | error | |
| Expected a to have rank 2. Received: a.shape={a.shape} | validation | error | |
| Expected b to have rank 1 or 2. Received: b.shape={b.shape} | validation | error | |
| Expected b.shape[0] to be equal to a.shape[0]. Received: a.s | validation | error | |
| n must be an integer. Received: n={n} of type {type(n)} | validation | error | |
| Expected input to have rank >= 1. Received scalar input {a}. | validation | error | |
| Expected input to have rank >= 2. Received input with shape | validation | error | keras, linalg, shape-validation, rank-error, matrix-ops |
| Expected a square matrix. Received non-square input with sha | validation | error | keras, linalg, square-matrix, shape-validation, eigendecomposition |
| Incompatible shapes between `a` and `b`. Expected `a.shape[- | validation | error | keras, linalg, linear-solve, shape-validation, solver |
| Incompatible shapes between `a` and `b`. Expected `a.shape[- | validation | error | keras, linalg, linear-solve, shape-validation, solver |
| Argument `segment_ids` should be an 1-D vector, got shape: { | validation | error | keras, segment-ops, shape-validation, grouped-reduction, jax-semantics |
| Argument `segment_ids` and `data` should have same leading d | validation | error | keras, segment-ops, shape-validation, data-alignment, grouped-reduction |
| Inputs to `cdist` must have rank >= 2. Received shapes: x.sh | validation | error | keras, cdist, pairwise-distance, shape-validation, rank-error |
| The last dimension of inputs to `cdist` must match. Received | validation | error | keras, cdist, pairwise-distance, shape-validation, feature-dim |
| Batch dimensions of inputs to `cdist` must be broadcastable. | exception | error | keras, cdist, pairwise-distance, broadcasting, shape-validation |
| Input should have rank >= 1. Received: input.shape = {x.shap | exception | error | keras, extract-sequences, rank-error, shape-validation, sequence-ops |
| Input `x` should be a tuple of two tensors - real and imagin | exception | error | keras, fft, input-validation, shape |
| Input `x` should be a tuple of two tensors - real and imagin | exception | error | keras, fft, shape-mismatch, input-validation |
| Input should have rank >= 1. Received: input.shape = {real.s | exception | error | keras, fft, rank, shape |
| Input should have its last dimension fully-defined. Received | exception | error | keras, fft, dynamic-shape, functional-model |
| Input should have rank >= 2. Received: input.shape = {real.s | exception | error | keras, fft2, rank, shape |
| Input should have its {axes} axes fully-defined. Received: i | exception | error | keras, fft2, dynamic-shape, functional-model |
| `sequence_stride` must be a positive integer. Received: sequ | exception | error | keras, stft, input-validation, type-error |