python - Keras 自动编码器 : Tying Weights from Encoder To Decoder not working

Question

我正在创建一个自动编码器作为我的 Kaggle 竞赛完整模型的一部分。我试图将编码器的重量联系起来，转移到解码器。在第一个纪元之前，权重正确同步，之后，解码器权重只是卡住，并且跟不上梯度下降更新的编码器权重。

我在谷歌上几乎每篇关于这个问题的帖子都找了 12 个小时，但似乎没有人知道我的案例的答案。最接近的是这个 Tying Autoencoder Weights in a Dense Keras Layer但是问题是通过不使用可变张量作为内核来解决的，但是我已经没有使用那种类型的张量作为我的解码器内核，所以没有用。

我正在使用本文中定义的 DenseTied Keras 自定义图层类 https://towardsdatascience.com/build-the-right-autoencoder-tune-and-optimize-using-pca-principles-part-ii-24b9cca69bd6 ，完全一样，只是改变了我引用 Keras 支持的方式以适应我的导入风格。

import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os

这是自定义层定义

class DenseTied(tf.keras.layers.Layer):

    def __init__(self, units,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 tied_to=None,
                 **kwargs):
        self.tied_to = tied_to
        if 'input_shape' not in kwargs and 'input_dim' in kwargs:
            kwargs['input_shape'] = (kwargs.pop('input_dim'),)
        super().__init__(**kwargs)
        self.units = units
        self.activation = tf.keras.activations.get(activation)
        self.use_bias = use_bias
        self.kernel_initializer = tf.keras.initializers.get(kernel_initializer)
        self.bias_initializer = tf.keras.initializers.get(bias_initializer)
        self.kernel_regularizer = tf.keras.regularizers.get(kernel_regularizer)
        self.bias_regularizer = tf.keras.regularizers.get(bias_regularizer)
        self.activity_regularizer = tf.keras.regularizers.get(activity_regularizer)
        self.kernel_constraint = tf.keras.constraints.get(kernel_constraint)
        self.bias_constraint = tf.keras.constraints.get(bias_constraint)
        self.input_spec = tf.keras.layers.InputSpec(min_ndim=2)
        self.supports_masking = True

    def build(self, input_shape):
        assert len(input_shape) >= 2
        input_dim = input_shape[-1]

        if self.tied_to is not None:
            self.kernel = tf.keras.backend.transpose(self.tied_to.kernel)
            self.non_trainable_weights.append(self.kernel)
        else:
            self.kernel = self.add_weight(shape=(input_dim, self.units),
                                          initializer=self.kernel_initializer,
                                          name='kernel',
                                          regularizer=self.kernel_regularizer,
                                          constraint=self.kernel_constraint)
        if self.use_bias:
            self.bias = self.add_weight(shape=(self.units,),
                                        initializer=self.bias_initializer,
                                        name='bias',
                                        regularizer=self.bias_regularizer,
                                        constraint=self.bias_constraint)
        else:
            self.bias = None
        self.input_spec = tf.keras.layers.InputSpec(min_ndim=2, axes={-1: input_dim})
        self.built = True

    def compute_output_shape(self, input_shape):
        assert input_shape and len(input_shape) >= 2
        output_shape = list(input_shape)
        output_shape[-1] = self.units
        return tuple(output_shape)

    def call(self, inputs):
        output = tf.keras.backend.dot(inputs, self.kernel)
        if self.use_bias:
            output = tf.keras.backend.bias_add(output, self.bias, data_format='channels_last')
        if self.activation is not None:
            output = self.activation(output)
        return output

这是使用虚拟数据集进行模型训练和测试

rand_samples = np.random.rand(16, 51)
dummy_ds = tf.data.Dataset.from_tensor_slices((rand_samples, rand_samples)).shuffle(16).batch(16)

encoder = tf.keras.layers.Dense(1, activation="linear", input_shape=(51,), use_bias=True)
decoder = DenseTied(51, activation="linear", tied_to=encoder, use_bias=True)

autoencoder = tf.keras.Sequential()
autoencoder.add(encoder)
autoencoder.add(decoder)

autoencoder.compile(metrics=['accuracy'],
                    loss='mean_squared_error',
                    optimizer='sgd')

autoencoder.summary()

print("Encoder Kernel Before 1 Epoch", encoder.kernel[0])
print("Decoder Kernel Before 1 Epoch", decoder.kernel[0][0])

autoencoder.fit(dummy_ds, epochs=1)

print("Encoder Kernel After 1 Epoch", encoder.kernel[0])
print("Decoder Kernel After 1 Epoch", decoder.kernel[0][0])

预期输出是两个内核在第一个元素中完全相同(为简单起见，只打印一个权重)

当前的输出显示 Decoder Kernel 没有像 Transposed Encoder Kernel 一样更新

2019-09-06 14:55:42.070003: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library nvcuda.dll
2019-09-06 14:55:42.984580: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce GTX 1060 major: 6 minor: 1 memoryClockRate(GHz): 1.733
pciBusID: 0000:01:00.0
2019-09-06 14:55:43.088109: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.        
2019-09-06 14:55:43.166145: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2019-09-06 14:55:43.203865: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-09-06 14:55:43.277988: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce GTX 1060 major: 6 minor: 1 memoryClockRate(GHz): 1.733
pciBusID: 0000:01:00.0
2019-09-06 14:55:43.300888: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.        
2019-09-06 14:55:43.309040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2019-09-06 14:55:44.077814: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-09-06 14:55:44.094542: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0
2019-09-06 14:55:44.099411: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N
2019-09-06 14:55:44.103424: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4712 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060, pci bus id: 0000:01:00.0, compute capability: 6.1)
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense (Dense)                (None, 1)                 52
_________________________________________________________________
dense_tied (DenseTied)       (None, 51)                103
=================================================================
Total params: 103
Trainable params: 103
Non-trainable params: 0
_________________________________________________________________
Encoder Kernel Before 1 Epoch tf.Tensor([0.20486075], shape=(1,), dtype=float32)
Decoder Kernel Before 1 Epoch tf.Tensor(0.20486075, shape=(), dtype=float32)
1/1 [==============================] - 1s 657ms/step - loss: 0.3396 - accuracy: 0.0000e+00
Encoder Kernel After 1 Epoch tf.Tensor([0.20530733], shape=(1,), dtype=float32)
Decoder Kernel After 1 Epoch tf.Tensor(0.20486075, shape=(), dtype=float32)
PS C:\Users\whitm\Desktop\CodeProjects\ForestClassifier-DEC>

我不明白我做错了什么。

Answer 1

为了绑定(bind)权重，我建议使用 Keras functional API可以共享图层。也就是说，这是一种将编码器和解码器之间的权重联系起来的替代实现:

class TransposableDense(tf.keras.layers.Dense):

    def __init__(self, units, **kwargs):
        super().__init__(units, **kwargs)

    def build(self, input_shape):
        assert len(input_shape) >= 2
        input_dim = input_shape[-1]
        self.t_output_dim = input_dim

        self.kernel = self.add_weight(shape=(int(input_dim), self.units),
                                      initializer=self.kernel_initializer,
                                      name='kernel',
                                      regularizer=self.kernel_regularizer,
                                      constraint=self.kernel_constraint)
        if self.use_bias:
            self.bias = self.add_weight(shape=(self.units,),
                                        initializer=self.bias_initializer,
                                        name='bias',
                                        regularizer=self.bias_regularizer,
                                        constraint=self.bias_constraint)
            self.bias_t = self.add_weight(shape=(input_dim,),
                                          initializer=self.bias_initializer,
                                          name='bias_t',
                                          regularizer=self.bias_regularizer,
                                          constraint=self.bias_constraint)
        else:
            self.bias = None
            self.bias_t = None
        # self.input_spec = tf.keras.layers.InputSpec(min_ndim=2, axes={-1: input_dim})
        self.built = True

    def call(self, inputs, transpose=False):
        bs, input_dim = inputs.get_shape()

        kernel = self.kernel
        bias = self.bias
        if transpose:
            assert input_dim == self.units
            kernel = tf.keras.backend.transpose(kernel)
            bias = self.bias_t

        output = tf.keras.backend.dot(inputs, kernel)
        if self.use_bias:
            output = tf.keras.backend.bias_add(output, bias, data_format='channels_last')
        if self.activation is not None:
            output = self.activation(output)
        return output

    def compute_output_shape(self, input_shape):
        bs, input_dim = input_shape
        output_dim = self.units
        if input_dim == self.units:
            output_dim = self.t_output_dim
        return bs, output_dim

可以通过使用 transpose=True 调用该层来转置该密集层的内核。请注意，这可能破坏一些基本的 Keras 原则(例如，该层具有多个输出形状)，但它应该适用于您的情况。

---

这是一个示例，展示了如何使用它来定义模型:

a = tf.keras.layers.Input((51,))
dense = TransposableDense(1, activation='linear', use_bias=True)
encoder_out = dense(a)
decoder_out = dense(encoder_out, transpose=True)
encoder = tf.keras.Model(a, encoder_out)
autoencoder = tf.keras.Model(a, decoder_out)