我正在使用 Mnist 資料集開發 GAN。我開發了生成器和鑒別器。但是，當我將它們組合在一起時，會出現此錯誤：Input 0 of layer "conv2d" is incompatible with the layer: expected axis -1 of input shape to have value 1, but received input with shape (None, 57, 57, 1024). 有誰知道為什么會這樣？我必須添加其他內容嗎？
預處理：
(x_train, _), (x_test, _) = mnist.load_data()
x_train = x_train.reshape(60000, 28, 28, 1)
x_test = x_test.reshape(10000, 28, 28, 1)
x_train = x_train.astype('float32')/255
x_test = x_test.astype('float32')/255
img_rows, img_cols = 28, 28
channels = 1
img_shape = (img_rows, img_cols, channels)
發生器：
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(64,(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(64,(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(Dense(512, activation=LeakyReLU(alpha=0.2)))
model.add(BatchNormalization(momentum=0.7))
model.add(Dense(1024, activation='tanh'))
model.summary()
model.compile(loss=keras.losses.binary_crossentropy, optimizer=Adam(learning_rate=0.02))
return model
generator = generator()
鑒別器：
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(MaxPooling2D(pool_size=(2, 2)))
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(BatchNormalization(momentum=0.7))
model.add(Dense(1024, activation='tanh'))
model.summary()
model.compile(loss=keras.losses.binary_crossentropy, optimizer=Adam(learning_rate=0.02))
return model
discriminator = discriminator()
兩種模型相結合（我得到錯誤的地方）：
def GAN(generator, discriminator):
model = Sequential()
model.add(generator)
discriminator.trainable = False
model.add(discriminator)
model.summary()
model.compile()
return model
gan = GAN(generator, discriminator)
uj5u.com熱心網友回復：
您的生成器需要生成影像，因此生成器的輸出形狀必須與影像的形狀相同。激活還必須與影像中的范圍兼容。我認為您的影像不會從 -1 變為 1，因此您不應使用“tanh”。您必須選擇與影像兼容的激活。
最后一個生成器層：
Dense(img_shape[-1], ...)
你的鑒別器需要判斷影像是真還是假，因此它的輸出必須只有一個值，0 或 1。
最后一個鑒別器層：
Dense(1, activation="sigmoid")
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