122 lines
4.4 KiB
Python
122 lines
4.4 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Contains the flatten layer."""
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import functools
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import operator
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import numpy as np
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import tensorflow.compat.v2 as tf
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from keras.engine.base_layer import Layer
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from keras.engine.input_spec import InputSpec
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from keras.utils import conv_utils
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.layers.Flatten")
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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,
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one of `channels_last` (default) or `channels_first`.
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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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It defaults to the `image_data_format` value found in your
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Keras config file at `~/.keras/keras.json`.
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If you never set it, then it will be "channels_last".
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Example:
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>>> model = tf.keras.Sequential()
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>>> model.add(tf.keras.layers.Conv2D(64, 3, 3, input_shape=(3, 32, 32)))
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>>> model.output_shape
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(None, 1, 10, 64)
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>>> model.add(Flatten())
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>>> model.output_shape
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(None, 640)
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"""
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def __init__(self, data_format=None, **kwargs):
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super().__init__(**kwargs)
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self.data_format = conv_utils.normalize_data_format(data_format)
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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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if self._channels_first:
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rank = inputs.shape.rank
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if rank and rank > 1:
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# Switch to channels-last format.
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permutation = [0]
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permutation.extend(range(2, rank))
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permutation.append(1)
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inputs = tf.transpose(inputs, perm=permutation)
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if tf.executing_eagerly():
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# Full static shape is guaranteed to be available.
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# Performance: Using `constant_op` is much faster than passing a
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# list.
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flattened_shape = tf.constant([inputs.shape[0], -1])
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return tf.reshape(inputs, flattened_shape)
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else:
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input_shape = inputs.shape
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rank = input_shape.rank
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if rank == 1:
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return tf.expand_dims(inputs, axis=1)
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else:
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batch_dim = tf.compat.dimension_value(input_shape[0])
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non_batch_dims = input_shape[1:]
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# Reshape in a way that preserves as much shape info as
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# possible.
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if non_batch_dims.is_fully_defined():
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last_dim = int(
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functools.reduce(operator.mul, non_batch_dims)
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)
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flattened_shape = tf.constant([-1, last_dim])
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elif batch_dim is not None:
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flattened_shape = tf.constant([int(batch_dim), -1])
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else:
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flattened_shape = [tf.shape(inputs)[0], -1]
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return tf.reshape(inputs, flattened_shape)
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def compute_output_shape(self, input_shape):
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input_shape = tf.TensorShape(input_shape).as_list()
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if not input_shape:
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output_shape = tf.TensorShape([1])
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else:
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output_shape = [input_shape[0]]
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if np.all(input_shape[1:]):
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output_shape += [np.prod(input_shape[1:], dtype=int)]
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else:
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output_shape += [None]
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return tf.TensorShape(output_shape)
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def get_config(self):
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config = super().get_config()
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config.update({"data_format": self.data_format})
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return config
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