73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
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import math
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from keras_core import backend
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from keras_core import operations as ops
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from keras_core.layers.input_spec import InputSpec
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from keras_core.layers.layer import Layer
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class Flatten(Layer):
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"""Flattens the input. Does not affect the batch size.
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Note: If inputs are shaped `(batch,)` without a feature axis, then
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flattening adds an extra channel dimension and output shape is `(batch, 1)`.
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Args:
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data_format: A string, one of `'channels_last'` (default) or
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`'channels_first'`. The ordering of the dimensions in the inputs.
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'`channels_last`' corresponds to inputs with shape
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`(batch, ..., channels)` while `'channels_first'` corresponds to
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inputs with shape `(batch, channels, ...)`.
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When unspecified, uses `image_data_format` value found in your Keras
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config file at `~/.keras/keras.json` (if exists). Defaults to
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`'channels_last'`.
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Example:
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>>> x = keras_core.Input(shape=(3, 32, 32))
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>>> y = keras_core.layers.Conv2D(64, 3, 3)(x)
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>>> y.shape
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(None, 1, 10, 64)
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>>> y = keras_core.layers.Flatten()(y)
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>>> y.shape
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(None, 640)
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"""
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def __init__(self, data_format=None, name=None, dtype=None):
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super().__init__(name=name, dtype=dtype)
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self.data_format = (
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backend.image_data_format() if data_format is None else data_format
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)
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self.input_spec = InputSpec(min_ndim=1)
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self._channels_first = self.data_format == "channels_first"
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def call(self, inputs):
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input_shape = inputs.shape
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rank = len(input_shape)
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if self._channels_first and rank > 1:
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# Switch to channels-last format.
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inputs = ops.transpose(inputs, axes=(0, *range(2, rank), 1))
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output_shape = tuple(
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dim if dim is not None else -1
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for dim in self.compute_output_shape(input_shape)
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)
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return ops.reshape(inputs, output_shape)
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def compute_output_shape(self, input_shape):
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non_batch_dims = input_shape[1:]
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if len(non_batch_dims) == 0:
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flattened_dim = 1
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elif None in non_batch_dims:
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flattened_dim = None
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else:
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flattened_dim = math.prod(non_batch_dims)
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return (input_shape[0], flattened_dim)
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def get_config(self):
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config = {"data_format": self.data_format}
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base_config = super().get_config()
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return {**base_config, **config}
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