如何在keras层中使用SVD？
How to use SVD inside keras layers?
我的目标是在将潜在层传递给自动编码器的解码器模块之前，使用 SVD 对 PCA 进行白化。我使用了 tf.linalg.svd 但它不起作用，因为它不包含必要的 Keras 参数。因此，作为一种解决方法，我试图将它包装在 Lambda 中，但出现了这个错误
AttributeError: 'tuple' object has no attribute 'shape'.
我尝试了 SO（例如 Using SVD in a custom layer in Keras/tensorflow）并 Google 在 Keras 中搜索 SVD 但找不到任何答案。我在这里附上了一个剥离但有效的代码：
import numpy as np
import tensorflow as tf
from sklearn import preprocessing
from keras.layers import Lambda, Input, Dense, Multiply, Subtract
from keras.models import Model
from keras import backend as K
from keras.losses import mse
from keras import optimizers
from keras.callbacks import EarlyStopping
x = np.random.randn(100, 5)
train_data = preprocessing.scale(x)
input_shape = (5, )
original_dim = train_data.shape[1]
intermediate_dim_1 = 64
intermediate_dim_2 = 16
latent_dim = 2
batch_size = 10
epochs = 15
# build encoder model
inputs = Input(shape=input_shape, name='encoder_input')
layer_1 = Dense(intermediate_dim_1, activation='tanh') (inputs)
layer_2 = Dense(intermediate_dim_2, activation='tanh') (layer_1)
encoded_layer = Dense(latent_dim, name='latent_layer') (layer_2)
encoder = Model(inputs, encoded_layer, name='encoder')
encoder.summary()
# build decoder model
latent_inputs = Input(shape=(latent_dim,))
layer_1 = Dense(intermediate_dim_1, activation='tanh') (latent_inputs)
layer_2 = Dense(intermediate_dim_2, activation='tanh') (layer_1)
outputs = Dense(original_dim,activation='sigmoid') (layer_2)
decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()
# mean removal and pca whitening
meanX = Lambda(lambda x: tf.reduce_mean(x, axis=0, keepdims=True))(encoded_layer)
standardized = Subtract()([encoded_layer, meanX])
sigma2 = K.dot(K.transpose(standardized), standardized)
sigma2 = Lambda(lambda x: x / batch_size)(sigma2)
s, u ,v = tf.linalg.svd(sigma2,compute_uv=True)
# s ,u ,v = Lambda(lambda x: tf.linalg.svd(x,compute_uv=True))(sigma2)
epsilon = 1e-6
# sqrt of number close to 0 leads to problem hence replace it with epsilon
si = tf.where(tf.less(s, epsilon), tf.sqrt(1 / epsilon) * tf.ones_like(s),
tf.math.truediv(1.0, tf.sqrt(s)))
whitening_layer = u @ tf.linalg.diag(si) @ tf.transpose(v)
whitened_encoding = K.dot(standardized, whitening_layer)
# Connect models
z_decoded = decoder(standardized)
# z_decoded = decoder(whitened_encoding)
# Define losses
reconstruction_loss = mse(inputs,z_decoded)
# Instantiate autoencoder
ae = Model(inputs, z_decoded, name='autoencoder')
ae.add_loss(reconstruction_loss)
# callback = EarlyStopping(monitor='val_loss', patience=5)
adam = optimizers.adam(learning_rate=0.002)
ae.compile(optimizer=adam)
ae.summary()
ae.fit(train_data, epochs=epochs, batch_size=batch_size,
validation_split=0.2, shuffle=True)
要重现错误，请取消注释这些行并注释其前面的行：
z_decoded = decoder(whitened_encoding)
s ,u ,v = Lambda(lambda x: tf.linalg.svd(x,compute_uv=True))(sigma2)
如果有人能告诉我如何将 SVD 包装在 Keras 层或替代实现中，我将不胜感激。
请注意，我没有包括重新参数化技巧来计算损失以保持代码简单。
谢谢！
我解决了这个问题。要在 Keras 中使用 SVD，我们需要使用 Lambda 层。但是，由于 Lambda return 是一个带有一些附加属性的张量，因此最好在 lambda 函数和 return 一个张量内部做额外的工作。我的代码的另一个问题是编码器和解码器模型的组合，我通过将编码器的输出组合到解码器模型的输入来解决这个问题。工作代码如下：
import numpy as np
import tensorflow as tf
from sklearn import preprocessing
from keras.layers import Lambda, Input, Dense, Multiply, Subtract
from keras.models import Model
from keras import backend as K
from keras.losses import mse
from keras import optimizers
from keras.callbacks import EarlyStopping
def SVD(sigma2):
s ,u ,v = tf.linalg.svd(sigma2,compute_uv=True)
epsilon = 1e-6
# sqrt of number close to 0 leads to problem hence replace it with epsilon
si = tf.where(tf.less(s, epsilon),
tf.sqrt(1 / epsilon) * tf.ones_like(s),
tf.math.truediv(1.0, tf.sqrt(s)))
whitening_layer = u @ tf.linalg.diag(si) @ tf.transpose(v)
return whitening_layer
x = np.random.randn(100, 5)
train_data = preprocessing.scale(x)
input_shape = (5, )
original_dim = train_data.shape[1]
intermediate_dim_1 = 64
intermediate_dim_2 = 16
latent_dim = 2
batch_size = 10
epochs = 15
# build encoder model
inputs = Input(shape=input_shape, name='encoder_input')
layer_1 = Dense(intermediate_dim_1, activation='tanh') (inputs)
layer_2 = Dense(intermediate_dim_2, activation='tanh') (layer_1)
encoded_layer = Dense(latent_dim, name='latent_layer') (layer_2)
encoder = Model(inputs, encoded_layer, name='encoder')
encoder.summary()
# build decoder model
latent_inputs = Input(shape=(latent_dim,))
layer_1 = Dense(intermediate_dim_1, activation='tanh') (latent_inputs)
layer_2 = Dense(intermediate_dim_2, activation='tanh') (layer_1)
outputs = Dense(original_dim,activation='sigmoid') (layer_2)
decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()
# mean removal and pca whitening
meanX = Lambda(lambda x: tf.reduce_mean(x, axis=0, keepdims=True))(encoded_layer)
standardized = Subtract()([encoded_layer, meanX])
sigma2 = K.dot(K.transpose(standardized), standardized)
sigma2 = Lambda(lambda x: x / batch_size)(sigma2)
# s, u ,v = tf.linalg.svd(sigma2,compute_uv=True)
whitening_layer = Lambda(SVD)(sigma2)
'''
s ,u ,v = Lambda(lambda x: tf.linalg.svd(x,compute_uv=True))(sigma2)
epsilon = 1e-6
# sqrt of number close to 0 leads to problem hence replace it with epsilon
si = tf.where(tf.less(s, epsilon),
tf.sqrt(1 / epsilon) * tf.ones_like(s),
tf.math.truediv(1.0, tf.sqrt(s)))
whitening_layer = u @ tf.linalg.diag(si) @ tf.transpose(v)
'''
print('whitening_layer shape=', np.shape(whitening_layer))
print('standardized shape=', np.shape(standardized))
whitened_encoding = K.dot(standardized, whitening_layer)
# Connect models
# z_decoded = decoder(standardized)
z_decoded = decoder(encoder(inputs))
# Define losses
reconstruction_loss = mse(inputs,z_decoded)
# Instantiate autoencoder
ae = Model(inputs, z_decoded, name='autoencoder')
ae.add_loss(reconstruction_loss)
# callback = EarlyStopping(monitor='val_loss', patience=5)
adam = optimizers.adam(learning_rate=0.002)
ae.compile(optimizer=adam)
ae.summary()
ae.fit(train_data, epochs=epochs, batch_size=batch_size,
validation_split=0.2, shuffle=True)
运行代码输出结果如下：
Model: "encoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
encoder_input (InputLayer) (None, 5) 0
_________________________________________________________________
dense_1 (Dense) (None, 64) 384
_________________________________________________________________
dense_2 (Dense) (None, 16) 1040
_________________________________________________________________
latent_layer (Dense) (None, 2) 34
=================================================================
Total params: 1,458
Trainable params: 1,458
Non-trainable params: 0
_________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 2) 0
_________________________________________________________________
dense_3 (Dense) (None, 64) 192
_________________________________________________________________
dense_4 (Dense) (None, 16) 1040
_________________________________________________________________
dense_5 (Dense) (None, 5) 85
=================================================================
Total params: 1,317
Trainable params: 1,317
Non-trainable params: 0
_________________________________________________________________
whitening_layer shape= (2, 2)
standardized shape= (None, 2)
/home/manish/anaconda3/envs/ica_gpu/lib/python3.7/site-packages/keras/engine/training_utils.py:819: UserWarning: Output decoder missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to decoder.
'be expecting any data to be passed to {0}.'.format(name))
Model: "autoencoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
encoder_input (InputLayer) (None, 5) 0
_________________________________________________________________
encoder (Model) (None, 2) 1458
_________________________________________________________________
decoder (Model) (None, 5) 1317
=================================================================
Total params: 2,775
Trainable params: 2,775
Non-trainable params: 0
_________________________________________________________________
Train on 80 samples, validate on 20 samples
Epoch 1/15
2020-05-16 16:01:55.443061: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
80/80 [==============================] - 0s 3ms/step - loss: 1.1739 - val_loss: 1.2238
Epoch 2/15
80/80 [==============================] - 0s 228us/step - loss: 1.0601 - val_loss: 1.0921
Epoch 3/15
80/80 [==============================] - 0s 261us/step - loss: 0.9772 - val_loss: 1.0291
Epoch 4/15
80/80 [==============================] - 0s 223us/step - loss: 0.9385 - val_loss: 0.9875
Epoch 5/15
80/80 [==============================] - 0s 262us/step - loss: 0.9105 - val_loss: 0.9560
Epoch 6/15
80/80 [==============================] - 0s 240us/step - loss: 0.8873 - val_loss: 0.9335
Epoch 7/15
80/80 [==============================] - 0s 217us/step - loss: 0.8731 - val_loss: 0.9156
Epoch 8/15
80/80 [==============================] - 0s 253us/step - loss: 0.8564 - val_loss: 0.9061
Epoch 9/15
80/80 [==============================] - 0s 273us/step - loss: 0.8445 - val_loss: 0.8993
Epoch 10/15
80/80 [==============================] - 0s 235us/step - loss: 0.8363 - val_loss: 0.8937
Epoch 11/15
80/80 [==============================] - 0s 283us/step - loss: 0.8299 - val_loss: 0.8874
Epoch 12/15
80/80 [==============================] - 0s 254us/step - loss: 0.8227 - val_loss: 0.8832
Epoch 13/15
80/80 [==============================] - 0s 227us/step - loss: 0.8177 - val_loss: 0.8789
Epoch 14/15
80/80 [==============================] - 0s 241us/step - loss: 0.8142 - val_loss: 0.8725
Epoch 15/15
80/80 [==============================] - 0s 212us/step - loss: 0.8089 - val_loss: 0.8679
希望对您有所帮助。
我的目标是在将潜在层传递给自动编码器的解码器模块之前，使用 SVD 对 PCA 进行白化。我使用了 tf.linalg.svd 但它不起作用，因为它不包含必要的 Keras 参数。因此，作为一种解决方法，我试图将它包装在 Lambda 中，但出现了这个错误
AttributeError: 'tuple' object has no attribute 'shape'.
我尝试了 SO（例如 Using SVD in a custom layer in Keras/tensorflow）并 Google 在 Keras 中搜索 SVD 但找不到任何答案。我在这里附上了一个剥离但有效的代码：
import numpy as np
import tensorflow as tf
from sklearn import preprocessing
from keras.layers import Lambda, Input, Dense, Multiply, Subtract
from keras.models import Model
from keras import backend as K
from keras.losses import mse
from keras import optimizers
from keras.callbacks import EarlyStopping
x = np.random.randn(100, 5)
train_data = preprocessing.scale(x)
input_shape = (5, )
original_dim = train_data.shape[1]
intermediate_dim_1 = 64
intermediate_dim_2 = 16
latent_dim = 2
batch_size = 10
epochs = 15
# build encoder model
inputs = Input(shape=input_shape, name='encoder_input')
layer_1 = Dense(intermediate_dim_1, activation='tanh') (inputs)
layer_2 = Dense(intermediate_dim_2, activation='tanh') (layer_1)
encoded_layer = Dense(latent_dim, name='latent_layer') (layer_2)
encoder = Model(inputs, encoded_layer, name='encoder')
encoder.summary()
# build decoder model
latent_inputs = Input(shape=(latent_dim,))
layer_1 = Dense(intermediate_dim_1, activation='tanh') (latent_inputs)
layer_2 = Dense(intermediate_dim_2, activation='tanh') (layer_1)
outputs = Dense(original_dim,activation='sigmoid') (layer_2)
decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()
# mean removal and pca whitening
meanX = Lambda(lambda x: tf.reduce_mean(x, axis=0, keepdims=True))(encoded_layer)
standardized = Subtract()([encoded_layer, meanX])
sigma2 = K.dot(K.transpose(standardized), standardized)
sigma2 = Lambda(lambda x: x / batch_size)(sigma2)
s, u ,v = tf.linalg.svd(sigma2,compute_uv=True)
# s ,u ,v = Lambda(lambda x: tf.linalg.svd(x,compute_uv=True))(sigma2)
epsilon = 1e-6
# sqrt of number close to 0 leads to problem hence replace it with epsilon
si = tf.where(tf.less(s, epsilon), tf.sqrt(1 / epsilon) * tf.ones_like(s),
tf.math.truediv(1.0, tf.sqrt(s)))
whitening_layer = u @ tf.linalg.diag(si) @ tf.transpose(v)
whitened_encoding = K.dot(standardized, whitening_layer)
# Connect models
z_decoded = decoder(standardized)
# z_decoded = decoder(whitened_encoding)
# Define losses
reconstruction_loss = mse(inputs,z_decoded)
# Instantiate autoencoder
ae = Model(inputs, z_decoded, name='autoencoder')
ae.add_loss(reconstruction_loss)
# callback = EarlyStopping(monitor='val_loss', patience=5)
adam = optimizers.adam(learning_rate=0.002)
ae.compile(optimizer=adam)
ae.summary()
ae.fit(train_data, epochs=epochs, batch_size=batch_size,
validation_split=0.2, shuffle=True)
要重现错误，请取消注释这些行并注释其前面的行：
z_decoded = decoder(whitened_encoding)
s ,u ,v = Lambda(lambda x: tf.linalg.svd(x,compute_uv=True))(sigma2)
如果有人能告诉我如何将 SVD 包装在 Keras 层或替代实现中，我将不胜感激。 请注意，我没有包括重新参数化技巧来计算损失以保持代码简单。 谢谢！
我解决了这个问题。要在 Keras 中使用 SVD，我们需要使用 Lambda 层。但是，由于 Lambda return 是一个带有一些附加属性的张量，因此最好在 lambda 函数和 return 一个张量内部做额外的工作。我的代码的另一个问题是编码器和解码器模型的组合，我通过将编码器的输出组合到解码器模型的输入来解决这个问题。工作代码如下：
import numpy as np
import tensorflow as tf
from sklearn import preprocessing
from keras.layers import Lambda, Input, Dense, Multiply, Subtract
from keras.models import Model
from keras import backend as K
from keras.losses import mse
from keras import optimizers
from keras.callbacks import EarlyStopping
def SVD(sigma2):
s ,u ,v = tf.linalg.svd(sigma2,compute_uv=True)
epsilon = 1e-6
# sqrt of number close to 0 leads to problem hence replace it with epsilon
si = tf.where(tf.less(s, epsilon),
tf.sqrt(1 / epsilon) * tf.ones_like(s),
tf.math.truediv(1.0, tf.sqrt(s)))
whitening_layer = u @ tf.linalg.diag(si) @ tf.transpose(v)
return whitening_layer
x = np.random.randn(100, 5)
train_data = preprocessing.scale(x)
input_shape = (5, )
original_dim = train_data.shape[1]
intermediate_dim_1 = 64
intermediate_dim_2 = 16
latent_dim = 2
batch_size = 10
epochs = 15
# build encoder model
inputs = Input(shape=input_shape, name='encoder_input')
layer_1 = Dense(intermediate_dim_1, activation='tanh') (inputs)
layer_2 = Dense(intermediate_dim_2, activation='tanh') (layer_1)
encoded_layer = Dense(latent_dim, name='latent_layer') (layer_2)
encoder = Model(inputs, encoded_layer, name='encoder')
encoder.summary()
# build decoder model
latent_inputs = Input(shape=(latent_dim,))
layer_1 = Dense(intermediate_dim_1, activation='tanh') (latent_inputs)
layer_2 = Dense(intermediate_dim_2, activation='tanh') (layer_1)
outputs = Dense(original_dim,activation='sigmoid') (layer_2)
decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()
# mean removal and pca whitening
meanX = Lambda(lambda x: tf.reduce_mean(x, axis=0, keepdims=True))(encoded_layer)
standardized = Subtract()([encoded_layer, meanX])
sigma2 = K.dot(K.transpose(standardized), standardized)
sigma2 = Lambda(lambda x: x / batch_size)(sigma2)
# s, u ,v = tf.linalg.svd(sigma2,compute_uv=True)
whitening_layer = Lambda(SVD)(sigma2)
'''
s ,u ,v = Lambda(lambda x: tf.linalg.svd(x,compute_uv=True))(sigma2)
epsilon = 1e-6
# sqrt of number close to 0 leads to problem hence replace it with epsilon
si = tf.where(tf.less(s, epsilon),
tf.sqrt(1 / epsilon) * tf.ones_like(s),
tf.math.truediv(1.0, tf.sqrt(s)))
whitening_layer = u @ tf.linalg.diag(si) @ tf.transpose(v)
'''
print('whitening_layer shape=', np.shape(whitening_layer))
print('standardized shape=', np.shape(standardized))
whitened_encoding = K.dot(standardized, whitening_layer)
# Connect models
# z_decoded = decoder(standardized)
z_decoded = decoder(encoder(inputs))
# Define losses
reconstruction_loss = mse(inputs,z_decoded)
# Instantiate autoencoder
ae = Model(inputs, z_decoded, name='autoencoder')
ae.add_loss(reconstruction_loss)
# callback = EarlyStopping(monitor='val_loss', patience=5)
adam = optimizers.adam(learning_rate=0.002)
ae.compile(optimizer=adam)
ae.summary()
ae.fit(train_data, epochs=epochs, batch_size=batch_size,
validation_split=0.2, shuffle=True)
运行代码输出结果如下：
Model: "encoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
encoder_input (InputLayer) (None, 5) 0
_________________________________________________________________
dense_1 (Dense) (None, 64) 384
_________________________________________________________________
dense_2 (Dense) (None, 16) 1040
_________________________________________________________________
latent_layer (Dense) (None, 2) 34
=================================================================
Total params: 1,458
Trainable params: 1,458
Non-trainable params: 0
_________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 2) 0
_________________________________________________________________
dense_3 (Dense) (None, 64) 192
_________________________________________________________________
dense_4 (Dense) (None, 16) 1040
_________________________________________________________________
dense_5 (Dense) (None, 5) 85
=================================================================
Total params: 1,317
Trainable params: 1,317
Non-trainable params: 0
_________________________________________________________________
whitening_layer shape= (2, 2)
standardized shape= (None, 2)
/home/manish/anaconda3/envs/ica_gpu/lib/python3.7/site-packages/keras/engine/training_utils.py:819: UserWarning: Output decoder missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to decoder.
'be expecting any data to be passed to {0}.'.format(name))
Model: "autoencoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
encoder_input (InputLayer) (None, 5) 0
_________________________________________________________________
encoder (Model) (None, 2) 1458
_________________________________________________________________
decoder (Model) (None, 5) 1317
=================================================================
Total params: 2,775
Trainable params: 2,775
Non-trainable params: 0
_________________________________________________________________
Train on 80 samples, validate on 20 samples
Epoch 1/15
2020-05-16 16:01:55.443061: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
80/80 [==============================] - 0s 3ms/step - loss: 1.1739 - val_loss: 1.2238
Epoch 2/15
80/80 [==============================] - 0s 228us/step - loss: 1.0601 - val_loss: 1.0921
Epoch 3/15
80/80 [==============================] - 0s 261us/step - loss: 0.9772 - val_loss: 1.0291
Epoch 4/15
80/80 [==============================] - 0s 223us/step - loss: 0.9385 - val_loss: 0.9875
Epoch 5/15
80/80 [==============================] - 0s 262us/step - loss: 0.9105 - val_loss: 0.9560
Epoch 6/15
80/80 [==============================] - 0s 240us/step - loss: 0.8873 - val_loss: 0.9335
Epoch 7/15
80/80 [==============================] - 0s 217us/step - loss: 0.8731 - val_loss: 0.9156
Epoch 8/15
80/80 [==============================] - 0s 253us/step - loss: 0.8564 - val_loss: 0.9061
Epoch 9/15
80/80 [==============================] - 0s 273us/step - loss: 0.8445 - val_loss: 0.8993
Epoch 10/15
80/80 [==============================] - 0s 235us/step - loss: 0.8363 - val_loss: 0.8937
Epoch 11/15
80/80 [==============================] - 0s 283us/step - loss: 0.8299 - val_loss: 0.8874
Epoch 12/15
80/80 [==============================] - 0s 254us/step - loss: 0.8227 - val_loss: 0.8832
Epoch 13/15
80/80 [==============================] - 0s 227us/step - loss: 0.8177 - val_loss: 0.8789
Epoch 14/15
80/80 [==============================] - 0s 241us/step - loss: 0.8142 - val_loss: 0.8725
Epoch 15/15
80/80 [==============================] - 0s 212us/step - loss: 0.8089 - val_loss: 0.8679
希望对您有所帮助。