Tensorflow Keras阀的输入尺寸不正确

我是机器学习新手，使用以下代码在MNISET数据集上创建一个示例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的模型概述如下：

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

现在它给出了错误

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

---

我在这里遗漏了什么，4维是什么。

您的问题在于

加载数据。最后一行需要更改如下。编码器希望图像有一个通道，但当前数据没有该通道

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

但是，您将遇到更多问题

例如，编码器输出以下内容

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

但是解码器接受以下输入

input_2 (InputLayer)         (None, 98)  

然后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

卷积VAE的实现
警告：如果您刚刚开始使用TF/Keras，卷积VAE有很多需要考虑的地方。我建议使用更简单的模型，如图像分类/自动编码器等，而不是跳转到复杂的模型，如卷积VAE。但如果你对这个很满意，那没关系

进一步阅读



这里有一些问题

您需要将输入形状从
[BATCH\u SIZE，28，28]
更改为
[BATCH\u SIZE，28，28，1]
，这样就可以通过卷积来消化：
np.展开dims（x\u测试，3）

编码器输出为4维
[BATCH\u SIZE，4,4,98]
，而解码器需要输入形状
[BATCH\u SIZE，98]
。您可以添加另一个maxpooling层，使其具有
[BATCH\u SIZE，1，1，98]
，然后将其展平为
[BATCH\u SIZE，98]
，或者将解码器重写为卷积

如果解码器保持完全连接，则需要使用
vae.fit（mniTrainX，mniTrainX.reformate（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conv层？）开始，然后逐步将其发展为VAE。
仅创建一个运行MNISET的VAE，这是我第一次尝试，并试图了解所有内容。@MuhammadUmar您是在尝试创建VAE还是简单的AE（自动编码器）？VAE应该是最终结果，走吧，我要试试看。但可能需要一段时间。我记不起VAE的确切细节了。AE会更容易在这里发布。我认为整形更适合于此，在
x_列和x_测试x_t=x_t.整形（x_t.shape[0]，x_t.shape[1]，x_t.shape[2]，1）
，至少在转换后不必再次检查形状
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()