1c74ae46cf
* add base merge layer * format docstrings * add layer * add test cases for layer * Add import for layer * fix build function * add dynamic and static tests * fix pytest import * fix pytest decorator * remove batch size from dynamic shape test * fix keras reference * refactor test class * fix tf tests, and linting issues * add subtract layer * add tests for subtract layer * fix linting issues * add average layer * add maximum layer * dd minimum layer * add multiply layer * add tests for average, minimum, maximum, and multiply layers * add concatenate layer * add dot layer * add tests for dot layer * format files * fix tests * fix bug in concatenate layer * fix build method * add missing tests for concatenate layer and dot layer
154 lines
5.6 KiB
Python
154 lines
5.6 KiB
Python
from keras_core import operations as ops
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from keras_core.api_export import keras_core_export
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from keras_core.layers.merging.base_merge import Merge
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@keras_core_export("keras_core.layers.Concatenate")
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class Concatenate(Merge):
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"""Concatenates a list of inputs.
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It takes as input a list of tensors, all of the same shape except
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for the concatenation axis, and returns a single tensor that is the
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concatenation of all inputs.
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Examples:
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>>> x = np.arange(20).reshape(2, 2, 5)
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>>> y = np.arange(20, 30).reshape(2, 1, 5)
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>>> keras_core.layers.Concatenate(axis=1)([x, y])
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Usage in a Keras model:
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>>> x1 = keras_core.layers.Dense(8)(np.arange(10).reshape(5, 2))
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>>> x2 = keras_core.layers.Dense(8)(np.arange(10, 20).reshape(5, 2))
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>>> y = keras_core.layers.Concatenate()([x1, x2])
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Args:
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axis: Axis along which to concatenate.
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**kwargs: Standard layer keyword arguments.
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Returns:
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A tensor, the concatenation of the inputs alongside axis `axis`.
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"""
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def __init__(self, axis=-1, **kwargs):
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super().__init__(**kwargs)
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self.axis = axis
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self.supports_masking = True
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self._reshape_required = False
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def build(self, input_shape):
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super().build(input_shape)
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# Used purely for shape validation.
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if len(input_shape) < 1 or not isinstance(input_shape[0], tuple):
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raise ValueError(
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"A `Concatenate` layer should be called on a list of "
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f"at least 1 input. Received: input_shape={input_shape}"
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)
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if all(shape is None for shape in input_shape):
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return
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reduced_inputs_shapes = [list(shape) for shape in input_shape]
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shape_set = set()
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for i in range(len(reduced_inputs_shapes)):
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del reduced_inputs_shapes[i][self.axis]
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shape_set.add(tuple(reduced_inputs_shapes[i]))
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if len(shape_set) != 1:
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err_msg = (
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"A `Concatenate` layer requires inputs with matching shapes "
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"except for the concatenation axis. "
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f"Received: input_shape={input_shape}"
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)
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# Make sure all the shapes have same ranks.
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ranks = set(len(shape) for shape in shape_set)
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if len(ranks) != 1:
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raise ValueError(err_msg)
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# Get the only rank for the set.
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(rank,) = ranks
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for axis in range(rank):
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# Skip the Nones in the shape since they are dynamic, also the
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# axis for concat has been removed above.
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unique_dims = set(
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shape[axis]
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for shape in shape_set
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if shape[axis] is not None
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)
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if len(unique_dims) > 1:
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raise ValueError(err_msg)
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def _merge_function(self, inputs):
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return ops.concatenate(inputs, axis=self.axis)
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def compute_output_shape(self, input_shape):
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if (not isinstance(input_shape, (tuple, list))) or (
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not isinstance(input_shape[0], (tuple, list))
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):
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raise ValueError(
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"A `Concatenate` layer should be called on a list of inputs. "
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f"Received: input_shape={input_shape}"
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)
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input_shapes = input_shape
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output_shape = list(input_shapes[0])
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for shape in input_shapes[1:]:
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if output_shape[self.axis] is None or shape[self.axis] is None:
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output_shape[self.axis] = None
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break
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output_shape[self.axis] += shape[self.axis]
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return tuple(output_shape)
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def compute_mask(self, inputs, mask=None):
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if mask is None:
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return None
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if not isinstance(mask, (tuple, list)):
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raise ValueError(f"`mask` should be a list. Received mask={mask}")
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if not isinstance(inputs, (tuple, list)):
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raise ValueError(
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f"`inputs` should be a list. Received: inputs={inputs}"
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)
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if len(mask) != len(inputs):
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raise ValueError(
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"The lists `inputs` and `mask` should have the same length. "
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f"Received: inputs={inputs} of length {len(inputs)}, and "
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f"mask={mask} of length {len(mask)}"
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)
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if all(m is None for m in mask):
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return None
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# Make a list of masks while making sure
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# the dimensionality of each mask
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# is the same as the corresponding input.
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masks = []
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for input_i, mask_i in zip(inputs, mask):
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if mask_i is None:
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# Input is unmasked. Append all 1s to masks,
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masks.append(ops.ones_like(input_i, dtype="bool"))
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elif mask_i.ndim < input_i.ndim:
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# Mask is smaller than the input, expand it
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masks.append(ops.expand_dims(mask_i, axis=-1))
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else:
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masks.append(mask_i)
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concatenated = ops.concatenate(masks, axis=self.axis)
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return ops.all(concatenated, axis=-1, keepdims=False)
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def get_config(self):
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config = {"axis": self.axis}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@keras_core_export("keras_core.layers.concatenate")
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def concatenate(inputs, axis=-1, **kwargs):
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"""Functional interface to the `Concatenate` layer.
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Args:
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inputs: A list of input tensors.
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axis: Concatenation axis.
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**kwargs: Standard layer keyword arguments.
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Returns:
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A tensor, the concatenation of the inputs alongside axis `axis`.
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"""
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return Concatenate(axis=axis, **kwargs)(inputs)
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