我正在尝试开发一个GAN，我已经创建了生成器和鉴别器，现在我正在尝试训练它。我正在使用Mnist数据集，但我计划使用更多数据集。问题是，当我训练它时，我得到以下错误：Input 0 of layer "conv2d_transpose_4" is incompatible with the layer: expected ndim=4, found ndim=2. Full shape received: (None, 100)
我真的不知道问题是在网络中还是在用于训练GAN的数据中，有人能告诉我应该如何训练它或者问题在哪里吗？
进口：

import tensorflow
import keras
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Flatten, Input, BatchNormalization, 
LeakyReLU, Reshape
from keras.layers import Conv2D, Conv2DTranspose, MaxPooling2D
from tensorflow.keras.optimizers import Adam
from keras import backend as K
from keras.utils import np_utils

from keras.datasets import mnist

import numpy as np
import matplotlib.pyplot as plt
import os
import cv2

发电机：

def generator():
   model = Sequential()
   model.add(Conv2DTranspose(32, (3,3), strides=(2, 
   2), activation='relu', use_bias=False, 
   input_shape=img_shape))
   model.add(BatchNormalization(momentum=0.3))
   model.add(Conv2DTranspose(128,(3,3),strides= 
   (2,2), activation='relu', padding='same', 
   use_bias=False)) 
   model.add(MaxPooling2D(pool_size=(2, 2)))
   model.add(LeakyReLU(alpha=0.2))

   model.add(Conv2DTranspose(128,(3,3),strides= 
   (2,2), activation='relu', padding='same', 
   use_bias=False))
   model.add(MaxPooling2D(pool_size=(2, 2)))
   model.add(Dropout(0.5))
   model.add(BatchNormalization(momentum=0.3))
   model.add(LeakyReLU(alpha=0.2))

   model.add(Conv2DTranspose(128,(3,3),strides= 
   (2,2), activation='relu', padding='same', 
   use_bias=False))
   model.add(BatchNormalization())
   model.add(Dense(512, 
   activation=LeakyReLU(alpha=0.2)))
   model.add(BatchNormalization(momentum=0.7))

   model.build()
   model.summary()
   return model

鉴别器：

def discriminator():
   model = Sequential()
   model.add(Conv2D(32, (5,5), strides=(2, 2), 
   activation='relu', use_bias=False, 
   input_shape=img_shape))
   model.add(BatchNormalization(momentum=0.3))
   model.add(Conv2D(64,(5,5),strides=(2,2), 
   activation='relu', padding='same', 
   use_bias=False))
   model.add(MaxPooling2D(pool_size=(2, 2)))
   model.add(LeakyReLU(alpha=0.2))

   model.add(Conv2D(64,(5,5),strides=(2,2), 
   activation='relu', padding='same', 
   use_bias=False))
   model.add(Dropout(0.5))
   model.add(BatchNormalization(momentum=0.3))
   model.add(LeakyReLU(alpha=0.2))

   model.add(Dense(512, 
   activation=LeakyReLU(alpha=0.2)))
   model.add(Flatten())
   model.add(BatchNormalization(momentum=0.7))
   model.add(Dense(1, activation='sigmoid'))

   model.build()
   model.summary()

   return model

列车功能：

def train(epochs, batch_size, save_interval):

(x_train, _), (_, _) = mnist.load_data()

x_train = (x_train.astype(np.float32) - 127.5) / 127.5
x_train = np.expand_dims(x_train, axis=3) 
half_batch = int(batch_size / 2)

for epoch in range(epochs):
    idx = np.random.randint(0, x_train.shape[0], half_batch)
    imgs = x_train[idx]

    noise = np.random.normal(0, 1, (half_batch, 100))

    gen_imgs = generator.predict(noise)

    d_loss_real = discriminator.train_on_batch(imgs, np.ones((half_batch, 1)))
    d_loss_fake = discriminator.train_on_batch(gen_imgs, np.zeros((half_batch, 1)))

    d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) 
    noise = np.random.normal(0, 1, (batch_size, 100)) 
    valid_y = np.array([1] * batch_size)
    g_loss = combined.train_on_batch(noise, valid_y)

    print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
    if epoch % save_interval == 0:
        save_imgs(epoch)

所用数据：

img_rows = 28
img_cols = 28
channels = 1
img_shape = (img_rows, img_cols, channels)

optimizer = Adam(0.0002, 0.5)
discriminator = discriminator()
discriminator.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])

generator = generator()
generator.compile(loss='binary_crossentropy', 
optimizer=optimizer)

z = Input(shape=(100,))
img = generator(z) #error

discriminator.trainable = False  

valid = discriminator(img)

combined = Model(z, valid)
combined.compile(loss='binary_crossentropy', 
optimizer=optimizer)

train(epochs=100000, batch_size=32, 
save_interval=10)

generator.save('generator_model.h5')