如何解决错误“ ValueError：检查输入时出错：预期input_1具有4个维，但数组的形状为（12，1）”

Question

我正在对自己的数据集实施通用对抗网络模型，但是在计算真实数据的鉴别器损失时遇到错误，如何解决该错误？是由于彩色图像导致形状不匹配吗？

img_rows = 28
img_cols = 28
channels = 3
latent_dim = 100
img_shape = (img_rows, img_cols, channels)

以下是鉴别函数：

def build_discriminator():

    model = Sequential()

    model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=img_shape, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))
    model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
    model.add(ZeroPadding2D(padding=((0,1),(0,1))))
    model.add(BatchNormalization(momentum=0.8))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))
    model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))
    model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(1, activation='sigmoid'))

    model.summary()

    img = Input(shape=img_shape)
    validity = model(img)

    return Model(img, validity)

以下是训练功能：

def train(epochs, batch_size=128):

    # Adversarial ground truths
    valid = np.ones((batch_size, 1)
    fake = np.zeros((batch_size, 1))

    for epoch in range(epochs):

        # ---------------------
        #  Train Discriminator
        # ---------------------

        # Select a random half of images
        idx = np.random.randint(0, images.shape[0], batch_size)
        imgs = images[idx]

        # Sample noise and generate a batch of new images
        noise = np.random.normal(0, 1, (batch_size, latent_dim))
        gen_imgs = generator.predict(noise)

        d_loss_real = discriminator.train_on_batch(imgs,valid)
        d_loss_fake = discriminator.train_on_batch(gen_imgs, fake)
        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

        # ---------------------
        #  Train Generator
        # ---------------------

        # Train the generator (wants discriminator to mistake images as real)
        g_loss = combined.train_on_batch(noise, valid)

        # Plot the progress
        print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))

train(epochs=2, batch_size=12)