Traces
All Ops
Filter
inputs
output
Trace
Feedback
Status
code_snippet
input
model
query
self
top_k
N/A
N/A
N/A
You are an experienced machine learning engineer expert in python, and keras3.
You will be provided with documentation from another framework and you need to
retrieve the corresponding operation for the target framework.
Here are some rules:
1. All functions and classes that are imported from `keras3` should be considered
to be `keras3` operations.
2. You should simply return the names of the operation and not its usage or documentation.
3. Ensure that the names of the ops consist of the en...
2
[{"file_path":"sources/api/layers/core_layers/einsum_dense.md","text":"# EinsumDense layer\n\n<span style=\"float:right;\">[[source]](https://github.com/keras-team/keras/tree/v3.6.0/keras/src/layers/core/einsum_dense.py#L19)</span>\n\n### `EinsumDense` class\n\n\n```python\nkeras.layers.EinsumDense(\n equation,\n output_shape,\n activation=None,\n bias_axes=None,\n kernel_initializer=\"glorot_uniform\",\n bias_initializer=\"zeros\",\n kernel_regularizer=None,\n bias_regul...
N/A
N/A
N/A
mlx.nn.Linear
*************
class Linear(input_dims: int, output_dims: int, bias: bool = True)
Applies an affine transformation to the input.
Concretely:
y = x W^\top + b
where: where W has shape "[output_dims, input_dims]" and b has
shape "[output_dims]".
The values are initialized from the uniform distribution
\mathcal{U}(-{k}, {k}), where k = \frac{1}{\sqrt{D_i}} and D_i is
equal to "input_dims".
Parameters:
* **input_dims** (*int*) -- The dimensiona...
N/A
[{"_type":"NodeWithScore","node":{"_type":"TextNode","id_":"9a2e22a3-df49-4da3-90c1-d44ad13dbb60","embedding":null,"metadata":{"file_path":"sources/api/layers/core_layers/einsum_dense.md"},"excluded_embed_metadata_keys":[],"excluded_llm_metadata_keys":[],"relationships":{"1":{"_type":"RelatedNodeInfo","node_id":"e803bccb-f817-4e04-a641-da0e736e91b3","node_type":"4","metadata":{"file_path":"sources/api/layers/core_layers/einsum_dense.md"},"hash":"fce014cdee619210068462b2981df875c2fd2b92ac65bc904f...
N/A
["file_path: sources/api/ops/numpy/index.md ---- <span style=\"float:right;\">[[source]](https://github.com/keras-team/keras/tree/v3.6.0/keras/src/ops/numpy.py#L983)</span> ### `array` function ```python keras.ops.array(x, dtype=None) ``` Create a tensor. __Arguments__ - __x__: Input tensor. - __dtype__: The desired data-type for the tensor. __Returns__ A tensor. __Examples__ ```python >>> keras.ops.array([1, 2, 3]) array([1, 2, 3], dtype=int32) ``` ```python >>> keras.ops.array([1...
text-embedding-ada-002
N/A
<openai.resources.embeddings.Embeddings object at 0x30263d300>
N/A
N/A
N/A
["file_path: /Users/sauravmaheshkar/dev/ml-framework-bot/artifacts/keras3-docs:v0/sources/api/ops/numpy/index.md <span style=\"float:right;\">[[source]](https://github.com/keras-team/keras/tree/v3.6.0/keras/src/ops/numpy.py#L2948)</span> ### `greater` function ```python keras.ops.greater(x1, x2) ``` Return the truth value of `x1 > x2` element-wise. __Arguments__ - __x1__: First input tensor. - __x2__: Second input tensor. __Returns__ Output tensor, element-wise comparison of `x1` and `...
text-embedding-ada-002
N/A
<openai.resources.embeddings.Embeddings object at 0x30263d300>
N/A
N/A
N/A
["file_path: /Users/sauravmaheshkar/dev/ml-framework-bot/artifacts/keras3-docs:v0/sources/api/ops/nn/index.md <span style=\"float:right;\">[[source]](https://github.com/keras-team/keras/tree/v3.6.0/keras/src/ops/nn.py#L227)</span> ### `log_sigmoid` function ```python keras.ops.log_sigmoid(x) ``` Logarithm of the sigmoid activation function. It is defined as `f(x) = log(1 / (1 + exp(-x)))`. __Arguments__ - __x__: Input tensor. __Returns__ A tensor with the same shape as `x`. __Example...
text-embedding-ada-002
N/A
<openai.resources.embeddings.Embeddings object at 0x30263d300>
N/A
N/A
N/A
["file_path: sources/api/applications/convnext.md # ConvNeXt Tiny, Small, Base, Large, XLarge <span style=\"float:right;\">[[source]](https://github.com/keras-team/keras/tree/v3.6.0/keras/src/applications/convnext.py#L550)</span> ### `ConvNeXtTiny` function ```python keras.applications.ConvNeXtTiny( include_top=True, include_preprocessing=True, weights=\"imagenet\", input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation=\"softm...
text-embedding-ada-002
N/A
<openai.resources.embeddings.Embeddings object at 0x30263d300>
N/A
N/A
N/A
["file_path: sources/api/mixed_precision/policy.md ---- <span style=\"float:right;\">[[source]](https://github.com/keras-team/keras/tree/v3.6.0/keras/src/dtype_policies/dtype_policy.py#L259)</span> ### `QuantizedFloat8DTypePolicy` class ```python keras.dtype_policies.QuantizedFloat8DTypePolicy( mode, source_name=None, amax_history_length=1024 ) ``` A dtype policy for a Keras layer. A dtype policy determines a layer's computation and variable dtypes. Each layer has a policy. Policies ...
text-embedding-ada-002
N/A
<openai.resources.embeddings.Embeddings object at 0x30263d300>
N/A
N/A
N/A
["file_path: sources/api/keras_cv/models/tasks/feature_extractor.md linear_layer = SimpleDense(4) # This will also call `build(input_shape)` and create the weights. y = linear_layer(ops.ones((2, 2))) assert len(linear_layer.weights) == 2 # These weights are trainable, so they're listed in `trainable_weights`: assert len(linear_layer.trainable_weights) == 2 ``` Besides trainable weights, updated via backpropagation during training, layers can also have non-trainable weights. These weights are...
text-embedding-ada-002
N/A
<openai.resources.embeddings.Embeddings object at 0x30263d300>
N/A
N/A
N/A
["file_path: sources/api/keras_nlp/models/gpt2/gpt2_causal_lm_preprocessor.md # GPT2CausalLMPreprocessor layer <span style=\"float:right;\">[[source]](https://github.com/keras-team/keras-nlp/tree/v0.15.1/keras_nlp/src/models/gpt2/gpt2_causal_lm_preprocessor.py#L21)</span> ### `GPT2CausalLMPreprocessor` class ```python keras_nlp.models.GPT2CausalLMPreprocessor( tokenizer, sequence_length=1024, add_start_token=True, add_end_token=True, **kwargs ) ``` GPT2 Causal LM preprocessor. This p...
text-embedding-ada-002
N/A
<openai.resources.embeddings.Embeddings object at 0x30263d300>
N/A
N/A
N/A
["file_path: sources/api/keras_nlp/models/bloom/bloom_tokenizer.md # BloomTokenizer <span style=\"float:right;\">[[source]](https://github.com/keras-team/keras-nlp/tree/v0.15.1/keras_nlp/src/models/bloom/bloom_tokenizer.py#L21)</span> ### `BloomTokenizer` class ```python keras_nlp.tokenizers.BloomTokenizer(vocabulary=None, merges=None, **kwargs) ``` A BLOOM tokenizer using Byte-Pair Encoding subword segmentation. This tokenizer class will tokenize raw strings into integer sequences and i...
text-embedding-ada-002
N/A
<openai.resources.embeddings.Embeddings object at 0x30263d300>
N/A
N/A
N/A
["file_path: sources/api/keras_nlp/preprocessing_layers/random_deletion.md ----","file_path: sources/api/keras_nlp/preprocessing_layers/start_end_packer.md # StartEndPacker layer <span style=\"float:right;\">[[source]](https://github.com/keras-team/keras-nlp/tree/v0.15.1/keras_nlp/src/layers/preprocessing/start_end_packer.py#L29)</span> ### `StartEndPacker` class ```python keras_nlp.layers.StartEndPacker( sequence_length, start_value=None, end_value=None, pad_value=None, ...
text-embedding-ada-002
N/A
<openai.resources.embeddings.Embeddings object at 0x30263d300>
N/A
N/A
N/A
["file_path: sources/api/layers/pooling_layers/global_average_pooling3d.md # GlobalAveragePooling3D layer <span style=\"float:right;\">[[source]](https://github.com/keras-team/keras/tree/v3.6.0/keras/src/layers/pooling/global_average_pooling3d.py#L6)</span> ### `GlobalAveragePooling3D` class ```python keras.layers.GlobalAveragePooling3D(data_format=None, keepdims=False, **kwargs) ``` Global average pooling operation for 3D data. __Arguments__ - __data_format__: string, either `\"channel...
text-embedding-ada-002
N/A
<openai.resources.embeddings.Embeddings object at 0x30263d300>
N/A
N/A
1-50 of 685
Per page:
50