tensorflow - Keras VAE 的输入尺寸不正确

Question

我是机器学习新手，正在使用以下代码在 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

现在出现错误

ValueError: Error when checking input: expected input_3 to have 4 dimensions, but got array with shape (60000, 28, 28)

我在这里缺少什么，第四维是什么。

Answer 1

您的问题出在loadData中。最后一行需要更改如下。您的编码器需要一张具有一个 channel 的图像，但您当前的数据没有该 channel 。

return (np.expand_dims(x_train, -1), y_train, np.expand_dims(x_test, -1), y_test)

但是，您会遇到更多问题。

例如，您的编码器输出以下内容。

batch_normalization_3 (Batch (None, 28, 28, 98)        392       

但是你的解码器接受以下输入

input_2 (InputLayer)         (None, 98)  

然后，您尝试将编码器的输出(即 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

• 1 Variational Autoencoder (Not convolutional)
• 2 Convolutional AE