Keras VAE 的输入维度不正确
Input dimensions to Keras VAE Not correct
我是机器学习新手，使用以下代码在 MNISET 数据集上创建示例 VAE
# We are going to use MINISET Dataset to train our GAN.
# All imports are going to be in this place.
import numpy as np
# Progressbar
from keras.callbacks import TensorBoard
from tqdm import tqdm
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.layers import Conv2D
from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
from keras.layers import LeakyReLU
from keras.layers import Dropout
from keras.layers import MaxPooling2D
from keras.layers import BatchNormalization
from keras.layers import Flatten
# All Defs will be defined here
# Importing training data of MINISET
def loadData():
# y_train contains the labels, numbers are 1, 2 or 5, 7 etc.
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# Normalizing based on color 255
x_train = (x_train.astype(np.float32) - 127.5) / 127.5
# convert shape of x_train from (60000, 28, 28) to (60000, 784)
# 784 columns per row
# x_train = x_train.reshape(60000, 784)
return (x_train, y_train, x_test, y_test)
def getEncoder():
# This returns a tensor
inputs = Input(shape=(28, 28, 1))
# Making a sample AlexNet Model Layer 1
encoder = Conv2D(392, (4, 4), padding='same', activation='relu')(inputs)
encoder = MaxPooling2D((4, 4), strides=(1, 1), padding='same')(encoder)
encoder = BatchNormalization()(encoder)
# Making a sample AlexNet Model Layer 2
encoder = Conv2D(196, (2, 2), padding='same', activation='relu')(encoder)
encoder = MaxPooling2D((4, 4), strides=(1, 1), padding='same')(encoder)
encoder = BatchNormalization()(encoder)
# Making a sample AlexNet Model Layer 3
encoder = Conv2D(98, (2, 2), padding='same', activation='relu')(encoder)
encoder = MaxPooling2D((2, 2), strides=(1, 1), padding='same')(encoder)
encoder = BatchNormalization()(encoder)
#encoder = Flatten()(encoder)
#encoder = Dense(2)(encoder)
#encoder = Dropout(0.4)(encoder)
model = Model(inputs=inputs, outputs=encoder)
model.compile(optimizer='adam', loss='binary_crossentropy')
# model.fit(data, labels) # starts training
print(model.summary())
return model;
def getDecoder():
# This returns a tensor
inputs = Input(shape=(98,))
# a layer instance is callable on a tensor, and returns a tensor
disc = Dense(196)(inputs)
disc = LeakyReLU(alpha=0.2)(disc)
disc = Dropout(0.3)(disc)
disc = Dense(392)(disc)
disc = LeakyReLU(alpha=0.2)(disc)
disc = Dropout(0.3)(disc)
disc = Dense(784, activation='sigmoid')(disc)
disc = LeakyReLU(alpha=0.2)(disc)
model = Model(inputs=inputs, outputs=disc)
model.compile(optimizer='rmsprop', loss='binary_crossentropy')
# starts training
# model.fit(data, labels)
print(model.summary())
return model;
def createVAE(decoder, encoder):
inputs = Input(shape=(28, 28, 1))
gen = encoder(inputs)
output = decoder(gen)
vae = Model(inputs=inputs, outputs=output)
print(vae.summary())
vae.compile(loss='binary_crossentropy', optimizer='rmsprop')
return vae
def doTraining(epochs=1, batchSize=128):
# Loading the data
(mniTrainX, mniTrainY, mniTestX, mniTestY) = loadData()
# Creating GAN
encoder = getEncoder()
decoder = getDecoder()
vae = createVAE(decoder, encoder)
vae.fit(mniTrainX, mniTrainX,
epochs=epochs,
batch_size=batchSize,
shuffle=True,
validation_data=(mniTestX, mniTestX),
callbacks = [TensorBoard(log_dir='/tmp/autoencoder')])
doTraining(10, 128)
VAE的模型总结为
None
Model: "model_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_3 (InputLayer) (None, 28, 28, 1) 0
_________________________________________________________________
model_1 (Model) (None, 28, 28, 98) 393862
_________________________________________________________________
model_2 (Model) multiple 404740
=================================================================
Total params: 798,602
Trainable params: 797,230
Non-trainable params: 1,372
现在报错
ValueError: Error when checking input: expected input_3 to have 4 dimensions, but got array with shape (60000, 28, 28)
我在这里错过了什么，什么是 4 维。
您的问题在 loadData。最后一行需要更改如下。您的编码器需要一个具有一个通道的图像，但您当前的数据没有该通道。
return (np.expand_dims(x_train, -1), y_train, np.expand_dims(x_test, -1), y_test)
但是，您会 运行 遇到更多问题。
例如，您的编码器输出以下内容。
batch_normalization_3 (Batch (None, 28, 28, 98) 392
但是你的解码器需要以下输入
input_2 (InputLayer) (None, 98)
然后您尝试将编码器的输出（None, 28, 28, 98）发送到 (None, 98) 输入层。这是行不通的。
实现卷积 VAE
Warning: If you are just starting with TF/Keras, Convolutional VAE is a lot to take in. I suggest going with simpler models like, image classification / autoencoder , etc models rather than jumping to a complex model like a convolutional VAE. But if you're comfortable with this that's okay.
from tensorflow.keras.layers import Dense, Input, Conv2D, Conv2DTranspose, MaxPooling2D, LeakyReLU, Reshape, BatchNormalization, Flatten, Lambda
from tensorflow.keras.models import Model
import tensorflow.keras.backend as K
from tensorflow.keras.datasets import mnist
import numpy as np
K.clear_session()
def loadData():
# y_train contains the labels, numbers are 1, 2 or 5, 7 etc.
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# Normalizing based on color 255
x_train = x_train.astype(np.float32) /255.0
x_test = x_test.astype(np.float32)/255.0
# convert shape of x_train from (60000, 28, 28) to (60000, 784)
# 784 columns per row
# x_train = x_train.reshape(60000, 784)
return (np.expand_dims(x_train, axis=-1), y_train, np.expand_dims(x_test, axis=-1), y_test)
# use reparameterization trick to push the sampling out as input
# note that "output_shape" isn't necessary with the TensorFlow backend
def sampling(args):
"""Reparameterization trick by sampling from an isotropic unit Gaussian.
# Arguments
args (tensor): mean and log of variance of Q(z|X)
# Returns
z (tensor): sampled latent vector
"""
z_mean, z_log_var = args
batch = K.shape(z_mean)[0]
dim = K.int_shape(z_mean)[1]
# by default, random_normal has mean = 0 and std = 1.0
epsilon = K.random_normal(shape=(batch, dim))
return z_mean + K.exp(0.5 * z_log_var) * epsilon
def getEncoder():
# This returns a tensor
inputs = Input(shape=(28, 28, 1))
# Making a sample AlexNet Model Layer 1
encoder = Conv2D(32, (4, 4), padding='same', activation='relu')(inputs)
encoder = MaxPooling2D((4, 4), strides=(2, 2), padding='same')(encoder) # 14,14
encoder = BatchNormalization()(encoder)
# Making a sample AlexNet Model Layer 2
encoder = Conv2D(64, (2, 2), padding='same', activation='relu')(encoder)
encoder = MaxPooling2D((4, 4), strides=(2, 2), padding='same')(encoder) # 7, 4
encoder = BatchNormalization()(encoder)
latent_dim = 64
encoder = Flatten()(encoder)
z_mean = Dense(latent_dim, name='z_mean')(encoder)
z_log_var = Dense(latent_dim, name='z_log_var')(encoder)
# This is to perform the reparametrization trick
z = Lambda(sampling, output_shape=(latent_dim,), name='z')([z_mean, z_log_var])
encoder = Model(inputs=inputs, outputs=[z_mean, z_log_var, z])
#encoder.summary()
return encoder
def getDecoder():
# This returns a tensor of shape (None, 28, 28, 1) exact same shape as input
latent_dim = 64
inputs = Input(shape=(latent_dim,))
disc = Dense(7*7*1)(inputs)
disc = LeakyReLU(alpha=0.2)(disc)
disc = Reshape([7,7,1])(disc)
# a layer instance is callable on a tensor, and returns a tensor
disc = Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu')(disc)
disc = Conv2DTranspose(1, (3,3), strides=(2,2), padding='same', activation='sigmoid')(disc)
decoder = Model(inputs=inputs, outputs=disc)
#decoder.summary()
return decoder
def kl_loss(z_mean, z_log_var):
kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5
return K.mean(kl_loss)
def createVAE(decoder, encoder):
# We are saying that the decoder takes the last output of the encoder as the input
dec_out = decoder(encoder.outputs[2])
# Defining an end-to-end model with encoder inputs and decoder outputs
vae = Model(inputs=encoder.inputs, outputs=dec_out)
print(vae.summary())
# VAE loss comprises both crossentropy and KL divergence loss
vae.compile(loss='binary_crossentropy', optimizer='rmsprop')
vae.add_loss(kl_loss(encoder.outputs[0], encoder.outputs[1]))
return vae
def doTraining(epochs=1, batchSize=128):
# Loading the data
(mniTrainX, mniTrainY, mniTestX, mniTestY) = loadData()
# Creating GAN
encoder = getEncoder()
decoder = getDecoder()
vae = createVAE(decoder, encoder)
# I have removed the tensorboard callback. If needed add that.
vae.fit(mniTrainX, mniTrainX,
epochs=epochs,
batch_size=batchSize,
shuffle=True,
validation_data=(mniTestX, mniTestX)
)
encoder = getEncoder()
decoder = getDecoder()
vae = createVAE(decoder, encoder)
doTraining()
进一步阅读
那里有一些问题。
您需要将输入形状从 [BATCH_SIZE, 28, 28] 更改为 [BATCH_SIZE, 28, 28, 1] 以便可以通过卷积消化：np.expand_dims(x_test, 3)
编码器输出是 4 维 [BATCH_SIZE, 4, 4, 98]，而解码器需要形状 [BATCH_SIZE, 98] 的输入。您可以添加另一个 maxpooling 层以具有 [BATCH_SIZE, 1, 1, 98]，而不是将其展平为 [BATCH_SIZE, 98] 或重写您的解码器以使其也是卷积的。
如果您让解码器保持完全连接状态，则需要使用 vae.fit(mniTrainX, mniTrainX.reshape(60000, 784), ...)
我建议您从一个非常简单的单层模型（有 1 个卷积层？）开始，然后逐渐将其演化为 VAE。
我是机器学习新手，使用以下代码在 MNISET 数据集上创建示例 VAE
# We are going to use MINISET Dataset to train our GAN.
# All imports are going to be in this place.
import numpy as np
# Progressbar
from keras.callbacks import TensorBoard
from tqdm import tqdm
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.layers import Conv2D
from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
from keras.layers import LeakyReLU
from keras.layers import Dropout
from keras.layers import MaxPooling2D
from keras.layers import BatchNormalization
from keras.layers import Flatten
# All Defs will be defined here
# Importing training data of MINISET
def loadData():
# y_train contains the labels, numbers are 1, 2 or 5, 7 etc.
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# Normalizing based on color 255
x_train = (x_train.astype(np.float32) - 127.5) / 127.5
# convert shape of x_train from (60000, 28, 28) to (60000, 784)
# 784 columns per row
# x_train = x_train.reshape(60000, 784)
return (x_train, y_train, x_test, y_test)
def getEncoder():
# This returns a tensor
inputs = Input(shape=(28, 28, 1))
# Making a sample AlexNet Model Layer 1
encoder = Conv2D(392, (4, 4), padding='same', activation='relu')(inputs)
encoder = MaxPooling2D((4, 4), strides=(1, 1), padding='same')(encoder)
encoder = BatchNormalization()(encoder)
# Making a sample AlexNet Model Layer 2
encoder = Conv2D(196, (2, 2), padding='same', activation='relu')(encoder)
encoder = MaxPooling2D((4, 4), strides=(1, 1), padding='same')(encoder)
encoder = BatchNormalization()(encoder)
# Making a sample AlexNet Model Layer 3
encoder = Conv2D(98, (2, 2), padding='same', activation='relu')(encoder)
encoder = MaxPooling2D((2, 2), strides=(1, 1), padding='same')(encoder)
encoder = BatchNormalization()(encoder)
#encoder = Flatten()(encoder)
#encoder = Dense(2)(encoder)
#encoder = Dropout(0.4)(encoder)
model = Model(inputs=inputs, outputs=encoder)
model.compile(optimizer='adam', loss='binary_crossentropy')
# model.fit(data, labels) # starts training
print(model.summary())
return model;
def getDecoder():
# This returns a tensor
inputs = Input(shape=(98,))
# a layer instance is callable on a tensor, and returns a tensor
disc = Dense(196)(inputs)
disc = LeakyReLU(alpha=0.2)(disc)
disc = Dropout(0.3)(disc)
disc = Dense(392)(disc)
disc = LeakyReLU(alpha=0.2)(disc)
disc = Dropout(0.3)(disc)
disc = Dense(784, activation='sigmoid')(disc)
disc = LeakyReLU(alpha=0.2)(disc)
model = Model(inputs=inputs, outputs=disc)
model.compile(optimizer='rmsprop', loss='binary_crossentropy')
# starts training
# model.fit(data, labels)
print(model.summary())
return model;
def createVAE(decoder, encoder):
inputs = Input(shape=(28, 28, 1))
gen = encoder(inputs)
output = decoder(gen)
vae = Model(inputs=inputs, outputs=output)
print(vae.summary())
vae.compile(loss='binary_crossentropy', optimizer='rmsprop')
return vae
def doTraining(epochs=1, batchSize=128):
# Loading the data
(mniTrainX, mniTrainY, mniTestX, mniTestY) = loadData()
# Creating GAN
encoder = getEncoder()
decoder = getDecoder()
vae = createVAE(decoder, encoder)
vae.fit(mniTrainX, mniTrainX,
epochs=epochs,
batch_size=batchSize,
shuffle=True,
validation_data=(mniTestX, mniTestX),
callbacks = [TensorBoard(log_dir='/tmp/autoencoder')])
doTraining(10, 128)
VAE的模型总结为
None
Model: "model_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_3 (InputLayer) (None, 28, 28, 1) 0
_________________________________________________________________
model_1 (Model) (None, 28, 28, 98) 393862
_________________________________________________________________
model_2 (Model) multiple 404740
=================================================================
Total params: 798,602
Trainable params: 797,230
Non-trainable params: 1,372
现在报错
ValueError: Error when checking input: expected input_3 to have 4 dimensions, but got array with shape (60000, 28, 28)
我在这里错过了什么，什么是 4 维。
您的问题在 loadData。最后一行需要更改如下。您的编码器需要一个具有一个通道的图像，但您当前的数据没有该通道。
return (np.expand_dims(x_train, -1), y_train, np.expand_dims(x_test, -1), y_test)
但是，您会 运行 遇到更多问题。
例如，您的编码器输出以下内容。
batch_normalization_3 (Batch (None, 28, 28, 98) 392
但是你的解码器需要以下输入
input_2 (InputLayer) (None, 98)
然后您尝试将编码器的输出（None, 28, 28, 98）发送到 (None, 98) 输入层。这是行不通的。
实现卷积 VAE
Warning: If you are just starting with TF/Keras, Convolutional VAE is a lot to take in. I suggest going with simpler models like, image classification / autoencoder , etc models rather than jumping to a complex model like a convolutional VAE. But if you're comfortable with this that's okay.
from tensorflow.keras.layers import Dense, Input, Conv2D, Conv2DTranspose, MaxPooling2D, LeakyReLU, Reshape, BatchNormalization, Flatten, Lambda
from tensorflow.keras.models import Model
import tensorflow.keras.backend as K
from tensorflow.keras.datasets import mnist
import numpy as np
K.clear_session()
def loadData():
# y_train contains the labels, numbers are 1, 2 or 5, 7 etc.
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# Normalizing based on color 255
x_train = x_train.astype(np.float32) /255.0
x_test = x_test.astype(np.float32)/255.0
# convert shape of x_train from (60000, 28, 28) to (60000, 784)
# 784 columns per row
# x_train = x_train.reshape(60000, 784)
return (np.expand_dims(x_train, axis=-1), y_train, np.expand_dims(x_test, axis=-1), y_test)
# use reparameterization trick to push the sampling out as input
# note that "output_shape" isn't necessary with the TensorFlow backend
def sampling(args):
"""Reparameterization trick by sampling from an isotropic unit Gaussian.
# Arguments
args (tensor): mean and log of variance of Q(z|X)
# Returns
z (tensor): sampled latent vector
"""
z_mean, z_log_var = args
batch = K.shape(z_mean)[0]
dim = K.int_shape(z_mean)[1]
# by default, random_normal has mean = 0 and std = 1.0
epsilon = K.random_normal(shape=(batch, dim))
return z_mean + K.exp(0.5 * z_log_var) * epsilon
def getEncoder():
# This returns a tensor
inputs = Input(shape=(28, 28, 1))
# Making a sample AlexNet Model Layer 1
encoder = Conv2D(32, (4, 4), padding='same', activation='relu')(inputs)
encoder = MaxPooling2D((4, 4), strides=(2, 2), padding='same')(encoder) # 14,14
encoder = BatchNormalization()(encoder)
# Making a sample AlexNet Model Layer 2
encoder = Conv2D(64, (2, 2), padding='same', activation='relu')(encoder)
encoder = MaxPooling2D((4, 4), strides=(2, 2), padding='same')(encoder) # 7, 4
encoder = BatchNormalization()(encoder)
latent_dim = 64
encoder = Flatten()(encoder)
z_mean = Dense(latent_dim, name='z_mean')(encoder)
z_log_var = Dense(latent_dim, name='z_log_var')(encoder)
# This is to perform the reparametrization trick
z = Lambda(sampling, output_shape=(latent_dim,), name='z')([z_mean, z_log_var])
encoder = Model(inputs=inputs, outputs=[z_mean, z_log_var, z])
#encoder.summary()
return encoder
def getDecoder():
# This returns a tensor of shape (None, 28, 28, 1) exact same shape as input
latent_dim = 64
inputs = Input(shape=(latent_dim,))
disc = Dense(7*7*1)(inputs)
disc = LeakyReLU(alpha=0.2)(disc)
disc = Reshape([7,7,1])(disc)
# a layer instance is callable on a tensor, and returns a tensor
disc = Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu')(disc)
disc = Conv2DTranspose(1, (3,3), strides=(2,2), padding='same', activation='sigmoid')(disc)
decoder = Model(inputs=inputs, outputs=disc)
#decoder.summary()
return decoder
def kl_loss(z_mean, z_log_var):
kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5
return K.mean(kl_loss)
def createVAE(decoder, encoder):
# We are saying that the decoder takes the last output of the encoder as the input
dec_out = decoder(encoder.outputs[2])
# Defining an end-to-end model with encoder inputs and decoder outputs
vae = Model(inputs=encoder.inputs, outputs=dec_out)
print(vae.summary())
# VAE loss comprises both crossentropy and KL divergence loss
vae.compile(loss='binary_crossentropy', optimizer='rmsprop')
vae.add_loss(kl_loss(encoder.outputs[0], encoder.outputs[1]))
return vae
def doTraining(epochs=1, batchSize=128):
# Loading the data
(mniTrainX, mniTrainY, mniTestX, mniTestY) = loadData()
# Creating GAN
encoder = getEncoder()
decoder = getDecoder()
vae = createVAE(decoder, encoder)
# I have removed the tensorboard callback. If needed add that.
vae.fit(mniTrainX, mniTrainX,
epochs=epochs,
batch_size=batchSize,
shuffle=True,
validation_data=(mniTestX, mniTestX)
)
encoder = getEncoder()
decoder = getDecoder()
vae = createVAE(decoder, encoder)
doTraining()
进一步阅读
那里有一些问题。
您需要将输入形状从
[BATCH_SIZE, 28, 28]更改为[BATCH_SIZE, 28, 28, 1]以便可以通过卷积消化：np.expand_dims(x_test, 3)编码器输出是 4 维
[BATCH_SIZE, 4, 4, 98]，而解码器需要形状[BATCH_SIZE, 98]的输入。您可以添加另一个 maxpooling 层以具有[BATCH_SIZE, 1, 1, 98]，而不是将其展平为[BATCH_SIZE, 98]或重写您的解码器以使其也是卷积的。如果您让解码器保持完全连接状态，则需要使用
vae.fit(mniTrainX, mniTrainX.reshape(60000, 784), ...) 展平参考图像
我建议您从一个非常简单的单层模型（有 1 个卷积层？）开始，然后逐渐将其演化为 VAE。