mnis的精细调谐深度自动编码器模型

用户

我为mnist数据集开发了一个3层深度的自动编码器模型,因为我只是在这个玩具数据集上练习,因为我是这个微调范例的初学者

下面是代码

from keras import  layers
from keras.layers import Input, Dense
from keras.models import Model,Sequential
from keras.datasets import mnist
import numpy as np

# Deep Autoencoder


# this is the size of our encoded representations
encoding_dim = 32   # 32 floats -> compression factor 24.5, assuming the input is 784 floats

# this is our input placeholder; 784 = 28 x 28
input_img = Input(shape=(784, ))

my_epochs = 100

# "encoded" is the encoded representation of the inputs
encoded = Dense(encoding_dim * 4, activation='relu')(input_img)
encoded = Dense(encoding_dim * 2, activation='relu')(encoded)
encoded = Dense(encoding_dim, activation='relu')(encoded)

# "decoded" is the lossy reconstruction of the input
decoded = Dense(encoding_dim * 2, activation='relu')(encoded)
decoded = Dense(encoding_dim * 4, activation='relu')(decoded)
decoded = Dense(784, activation='sigmoid')(decoded)

# this model maps an input to its reconstruction
autoencoder = Model(input_img, decoded)

# Separate Encoder model

# this model maps an input to its encoded representation
encoder = Model(input_img, encoded)

# Separate Decoder model

# create a placeholder for an encoded (32-dimensional) input
encoded_input = Input(shape=(encoding_dim, ))
# retrieve the layers of the autoencoder model
decoder_layer1 = autoencoder.layers[-3]
decoder_layer2 = autoencoder.layers[-2]
decoder_layer3 = autoencoder.layers[-1]
# create the decoder model
decoder = Model(encoded_input, decoder_layer3(decoder_layer2(decoder_layer1(encoded_input))))

# Train to reconstruct MNIST digits

# configure model to use a per-pixel binary crossentropy loss, and the Adadelta optimizer
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

# prepare input data
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# normalize all values between 0 and 1 and flatten the 28x28 images into vectors of size 784
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))

# Train autoencoder for 50 epochs

autoencoder.fit(x_train, x_train, epochs=my_epochs, batch_size=256, shuffle=True, validation_data=(x_test, x_test),
                verbose=2)

# after 100 epochs the autoencoder seems to reach a stable train/test lost value

# Visualize the reconstructed encoded representations

# encode and decode some digits
# note that we take them from the *test* set
encodedTrainImages=encoder.predict(x_train)
encoded_imgs = encoder.predict(x_test)
decoded_imgs = decoder.predict(encoded_imgs)





# From here I want to fine tune just the encoder model
model=Sequential()
model=Sequential()
for layer in encoder.layers:
  model.add(layer)
model.add(layers.Flatten())
model.add(layers.Dense(20, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(10, activation='softmax'))

以下是我想要微调的编码器型号。

^{pr2}$

问题:1

在建立了自动编码器模型后,我只想使用编码器模型并对其进行微调,以用于mnist数据集中的分类任务,但我得到了错误。在

错误:

Traceback (most recent call last):
  File "C:\Users\samer\Anaconda3\envs\tensorflow-gpu\lib\site-packages\IPython\core\interactiveshell.py", line 3267, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-15-528c079e5325>", line 3, in <module>
    model.add(layers.Flatten())
  File "C:\Users\samer\Anaconda3\envs\tensorflow-gpu\lib\site-packages\keras\engine\sequential.py", line 181, in add
    output_tensor = layer(self.outputs[0])
  File "C:\Users\samer\Anaconda3\envs\tensorflow-gpu\lib\site-packages\keras\engine\base_layer.py", line 414, in __call__
    self.assert_input_compatibility(inputs)
  File "C:\Users\samer\Anaconda3\envs\tensorflow-gpu\lib\site-packages\keras\engine\base_layer.py", line 327, in assert_input_compatibility
    str(K.ndim(x)))
ValueError: Input 0 is incompatible with layer flatten_4: expected min_ndim=3, found ndim=2

问题2:

类似地,我以后会使用预先训练的模型,其中每个自动编码器都将以贪婪的方式进行训练,然后最终的模型将被微调。有人能指导我如何进一步完成这两项任务吗。在

问候


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更新日期: 2020-10-22 22:13:05
1 个回答
fefe Tyson

问题1

问题是,您试图将已经平坦的层变平:编码器由一维Desnse层组成,这些层具有形状(batch_size, dim)。在

扁平层至少需要一个二维输入,即具有三维形状(batch_size, dim1, dim2)(例如,Conv2D层的输出),通过移除它,模型将正确构建:

encoding_dim = 32
input_img = layers.Input(shape=(784, ))

encoded = layers.Dense(encoding_dim * 4, activation='relu')(input_img)
encoded = layers.Dense(encoding_dim * 2, activation='relu')(encoded)
encoded = layers.Dense(encoding_dim, activation='relu')(encoded)

encoder = Model(input_img, encoded)

[...]

model = Sequential()
for layer in encoder.layers:
    print(layer.name)
    model.add(layer)
model.add(layers.Dense(20, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(10, activation='softmax'))

model.summary()

哪些输出:

^{pr2}$

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编辑:在评论中整合问题的答案

Q:我如何确保新模型将使用与先前训练过的编码器相同的权重?

答:在您的代码中,您所做的是迭代编码器内部包含的层,然后将每个层传递给model.add()。您在这里所做的是将引用直接传递给每个层,因此您将在新模型中拥有相同的层。以下是使用图层名称的概念证明:

encoding_dim = 32

input_img = Input(shape=(784, ))

encoded = Dense(encoding_dim * 4, activation='relu')(input_img)
encoded = Dense(encoding_dim * 2, activation='relu')(encoded)

encoded = Dense(encoding_dim, activation='relu')(encoded)

decoded = Dense(encoding_dim * 2, activation='relu')(encoded)
decoded = Dense(encoding_dim * 4, activation='relu')(decoded)
decoded = Dense(784, activation='sigmoid')(decoded)

autoencoder = Model(input_img, decoded)

print("autoencoder first Dense layer reference:", autoencoder.layers[1])

encoder = Model(input_img, encoded)

print("encoder first Dense layer reference:", encoder.layers[1])

new_model = Sequential()
for i, layer in enumerate(encoder.layers):
  print("Before: ", layer.name)
  new_model.add(layer)
  if i != 0:
    new_model.layers[i-1].name = "new_model_"+layer.name
    print("After: ", layer.name)

哪些输出:

autoencoder first Dense layer reference: <keras.layers.core.Dense object at 
0x7fb5f138e278>
encoder first Dense layer reference: <keras.layers.core.Dense object at 
0x7fb5f138e278>
Before:  input_1
Before:  dense_1
After:  new_model_dense_1
Before:  dense_2
After:  new_model_dense_2
Before:  dense_3
After:  new_model_dense_3

如您所见,编码器和自动编码器中的层引用是相同的。另外,通过改变新模型内部的层名称,我们也改变了编码器对应层的层名称。有关通过引用传递的python参数的详细信息,请查看answer。在


Q:我的数据需要一个热编码吗?如果是,那怎么办?

答:你确实需要一个热编码,因为你正在处理一个多标签的分类问题。只需使用一个方便的keras函数即可完成编码:

from keras.utils import np_utils

one_hot = np_utils.to_categorical(y_train)

这是指向documentation的链接。在

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问题2

关于你的第二个问题,你的目标不是很清楚,但是在我看来,你想要构建一个包含几个并行自动编码器的体系结构,这些编码器专门用于不同的任务,然后通过添加一些最终的公共层来连接它们的输出。在

无论如何,到目前为止,我能做的是建议您研究一下这个guide,它解释了如何构建多输入和多输出模型,并将其用作开始定制实现的基线。在

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编辑2:问题2答案整合

对于贪婪的训练任务,方法是一次训练一层,在附加新层时冻结之前的所有层。下面是一个3(+1)贪心训练层网络的示例,该网络后来用作新模型的基础:

(x_train, y_train), (x_test, y_test) = mnist.load_data()
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
x_train = np.reshape(x_train, (x_train.shape[0], -1))
x_test = np.reshape(x_test, (x_test.shape[0], -1))

model = Sequential()
model.add(Dense(256, activation="relu", kernel_initializer="he_uniform", input_shape=(28*28,)))
model.add(Dense(10, activation="softmax"))

model.compile(optimizer=SGD(lr=0.01, momentum=0.9), loss="categorical_crossentropy", metrics=["accuracy"])
model.fit(x_train, y_train, batch_size=64, epochs=50, verbose=1)

# Remove last layer
model.pop()

# 'Freeze' previous layers, so to single-train the new one
for layer in model.layers:
    layer.trainable = False

# Append new layer + classification layer
model.add(Dense(64, activation="relu", kernel_initializer="he_uniform"))
model.add(Dense(10, activation="softmax"))

model.fit(x_train, y_train, batch_size=64, epochs=50, verbose=0)

#  Remove last layer
model.pop()

# 'Freeze' previous layers, so to single-train the new one
for layer in model.layers:
    layer.trainable = False

# Append new layer + classification layer
model.add(Dense(32, activation="relu", kernel_initializer="he_uniform"))
model.add(Dense(10, activation="softmax"))

model.fit(x_train, y_train, batch_size=64, epochs=50, verbose=0)

# Create new model which will use the pre-trained layers
new_model = Sequential()

# Discard the last layer from the previous model
model.pop()

# Optional: you can decide to set the pre-trained layers as trainable, in 
# which case it would be like having initialized their weights, or not.
for l in model.layers:
    l.trainable = True
new_model.add(model)

new_model.add(Dense(20, activation='relu'))
new_model.add(Dropout(0.5))
new_model.add(Dense(10, activation='softmax'))

new_model.compile(optimizer=SGD(lr=0.01, momentum=0.9), loss="categorical_crossentropy", metrics=["accuracy"])
new_model.fit(x_train, y_train, batch_size=64, epochs=100, verbose=1)

大概就是这样,但是我必须说贪婪层训练不再是一个合适的解决方案:现在的ReLU、Dropout和其他正则化技术使得贪婪层训练成为过时和耗时的权重初始化,因此,在进行贪婪的训练之前,你可能还想看看其他的可能性。在

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评论 - 2020年9月22日 13:38

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