Question

我尝试使用以下代码将权重捆绑在tensorflow 2.0 keras中。但是显示此错误？有谁知道如何写权重密集层？

tf.random.set_seed(0)
with tf.device('/cpu:0'):
    # This returns a tensor
    inputs = Input(shape=(784,))

# a layer instance is callable on a tensor, and returns a tensor
    layer_1 = Dense(64, activation='relu')
    layer_1_output = layer_1(inputs)
    layer_2 = Dense(64, activation='relu')
    layer_2_output = layer_2(layer_1_output)
    weights = tf.transpose(layer_1.weights[0]).numpy()
    print(weights.shape)
    transpose_layer = Dense(
        784, activation='relu')
    transpose_layer_output = transpose_layer(layer_2_output)
    transpose_layer.set_weights(weights)
    predictions = Dense(10, activation='softmax')(transpose_layer)

    # This creates a model that includes
    # the Input layer and three Dense layers
    model = Model(inputs=inputs, outputs=predictions)
    model.compile(optimizer=tf.keras.optimizers.Adam(0.001),
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])
    # print(model.weights)
model.summary()

错误

Traceback (most recent call last):
File "practice_2.py", line 62, in <module>
transpose_layer.set_weights(weights)
File "/Users/cheesiang_leow/.virtualenvs/tensorflow-2.0/lib/python3.6/site- 
packages/tensorflow/python/keras/engine/base_layer.py", line 934, in set_weights
str(weights)[:50] + '...')
ValueError: You called `set_weights(weights)` on layer "dense_2" with a  weight 
list of length 64, but the layer was expecting 2 weights. Provided weights: 
[[-0.03499636  0.0214913   0.04076344 ... -0.06531...

Answer 1

首先让我们看一下模型架构和模型参数（不加权重）

蓝色箭头代表偏见。因此，具有n个输入的神经元将具有n + 1个权重。

现在，您要将transpose_layer的权重与layer_1绑在一起。您已将layers_1的权重转换为64*784并将其设置为transpose_layers，但是存在几个问题

weight[0]将给出权重，而weight[1]将给出密集层的偏差。所以你在那里很好。但是set_weights需要权重列表。在Dense层的情况下，它将需要两个np数组的列表，第一个列表是大小（64 * 784）的权重，第二个列表是大小为784的np数组用于偏置。那么如何获得784个偏置值呢？

解决方案：

一个不错的选择是通过设置use_bias=False
保持偏差值不变。（通过weight[1]读取偏差值，然后将其传回set_weights）
只需将偏差设置为一些小的随机值（非常不好的主意）

使用解决方案1的代码

import tensorflow as tf
from keras.layers import Dense, Input
from keras.models import Model

with tf.device('/cpu:0'):

    inputs = Input(shape=(784,))

    layer_1 = Dense(64, activation='relu')
    layer_1_output = layer_1(inputs)

    layer_2 = Dense(64, activation='relu')
    layer_2_output = layer_2(layer_1_output)

    transpose_layer = Dense(784, activation='relu', use_bias=False)
    transpose_layer_output = transpose_layer(layer_2_output)

    transpose_layer.set_weights([layer_1.get_weights()[0].T])

    model = Model(inputs=inputs, outputs=transpose_layer_output)
    model.compile('adam', loss='categorical_crossentropy')

    model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_36 (InputLayer)        (None, 784)               0         
_________________________________________________________________
dense_155 (Dense)            (None, 64)                50240     
_________________________________________________________________
dense_156 (Dense)            (None, 64)                4160      
_________________________________________________________________
dense_157 (Dense)            (None, 784)               50176     
=================================================================
Total params: 104,576
Trainable params: 104,576
Non-trainable params: 0

注意：您可以看到use_bias=False中使用transpose_layer的权重是784*64 = 50176，而不是如图中的50960权重（有偏见）

Answer 2

我花了很多时间弄清楚，但是我认为这是通过对Keras Dense层进行子类化来实现“权重”的方法。

class TiedLayer(Dense):
    def __init__(self, layer_sizes, l2_normalize=False, dropout=0.0, *args, **kwargs):
        self.layer_sizes = layer_sizes
        self.l2_normalize = l2_normalize
        self.dropout = dropout
        self.kernels = []
        self.biases = []
        self.biases2 = []
        self.uses_learning_phase = True
        self.activation = kwargs['activation']
        if self.activation == "leaky_relu":
            self.activation = kwargs.pop('activation')
            self.activation = LeakyReLU()
            print(self.activation)
        super().__init__(units=1, *args, **kwargs)  # 'units' not used

    def compute_output_shape(self, input_shape):
        return input_shape

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

        self.input_spec = InputSpec(min_ndim=2, axes={-1: input_dim})
        # print(input_dim)
        for i in range(len(self.layer_sizes)):

            self.kernels.append(
                self.add_weight(
                    shape=(
                        input_dim,
                        self.layer_sizes[i]),
                    initializer=self.kernel_initializer,
                    name='ae_kernel_{}'.format(i),
                    regularizer=self.kernel_regularizer,
                    constraint=self.kernel_constraint))

            if self.use_bias:
                self.biases.append(
                    self.add_weight(
                        shape=(
                            self.layer_sizes[i],
                        ),
                        initializer=self.bias_initializer,
                        name='ae_bias_{}'.format(i),
                        regularizer=self.bias_regularizer,
                        constraint=self.bias_constraint))
            input_dim = self.layer_sizes[i]

        if self.use_bias:
            for n, i in enumerate(range(len(self.layer_sizes)-2, -1, -1)):
                self.biases2.append(
                    self.add_weight(
                        shape=(
                            self.layer_sizes[i],
                        ),
                        initializer=self.bias_initializer,
                        name='ae_bias2_{}'.format(n),
                        regularizer=self.bias_regularizer,
                        constraint=self.bias_constraint))
            self.biases2.append(self.add_weight(
                shape=(
                    int(input_shape[-1]),
                ),
                initializer=self.bias_initializer,
                name='ae_bias2_{}'.format(len(self.layer_sizes)),
                regularizer=self.bias_regularizer,
                constraint=self.bias_constraint))

        self.built = True

    def call(self, inputs):
        return self.decode(self.encode(inputs))

    def _apply_dropout(self, inputs):
        dropped = K.backend.dropout(inputs, self.dropout)
        return K.backend.in_train_phase(dropped, inputs)

    def encode(self, inputs):
        latent = inputs
        for i in range(len(self.layer_sizes)):
            if self.dropout > 0:
                latent = self._apply_dropout(latent)
            print(self.kernels[i])
            latent = K.backend.dot(latent, self.kernels[i])
            if self.use_bias:
                print(self.biases[i])
                latent = K.backend.bias_add(latent, self.biases[i])
            if self.activation is not None:
                latent = self.activation(latent)
        if self.l2_normalize:
            latent = latent / K.backend.l2_normalize(latent, axis=-1)
        return latent

    def decode(self, latent):
        recon = latent
        for i in range(len(self.layer_sizes)):
            if self.dropout > 0:
                recon = self._apply_dropout(recon)
            print(self.kernels[len(self.layer_sizes) - i - 1])
            recon = K.backend.dot(recon, K.backend.transpose(
                self.kernels[len(self.layer_sizes) - i - 1]))
            if self.use_bias:
                print(self.biases2[i])
                recon = K.backend.bias_add(recon, self.biases2[i])
            if self.activation is not None:
                recon = self.activation(recon)
        return recon

    def get_config(self):
        config = {
            'layer_sizes': self.layer_sizes
        }
        base_config = super().get_config()
        base_config.pop('units', None)
        return dict(list(base_config.items()) + list(config.items()))

    @classmethod
    def from_config(cls, config):
        return cls(**config)

希望它可以帮助其他人。

如何在换位图层之间绑定权重？

2 个答案: