The authors of the DCGAN paper tell that they scaled the images to a range of [-1, 1] and used tanh as the activation function in the last layer of the generator. Also use the alpha(slope) of LeakyRelu as 0.2.
So I did these things and got rid of the Dense layers as the authors tell to eliminate the fully connected(Dense) layers.
The ReLU activation (Nair & Hinton, 2010) is used in the generator with the exception of the output layer which uses the Tanh function. We observed that using a bounded activation allowed the model to learn more quickly to saturate and cover the color space of the training distribution Within the discriminator we found the leaky rectified activation (Maas et al., 2013) (Xu et al., 2015) to work well, especially for higher resolution modeling. This is in contrast to the original GAN paper, which used the maxout activation (Goodfellow et al., 2013).