have problem to get 76% accuarcy on CIFAR10 dataset

[NinaLLI]

Dear I used following setting for my network but I can not get 76% accuracy on validation set , the optimizer is 'Adam ' and also I did mean-subtraction as preprocessing for R, G , B channel separately .would you please help me ?

   model = Sequential()
   model.add(Conv2D(32, (3, 3), border_mode='same',batch_input_shape=(None,32,32,3)))

    model.add(Activation('tanh'))
    model.add(Conv2D(32, ( 3, 3),border_mode='same'))

   model.add(Activation('tanh'))
   model.add(MaxPooling2D(pool_size=(2, 2)))
   model.add(Dropout(0.25))
   model.add(Conv2D(64, (3, 3),border_mode='same')) 
   model.add(Activation('tanh'))
   model.add(Conv2D(64, ( 3, 3),border_mode='same'))
   model.add(Activation('tanh'))
   model.add(MaxPooling2D(pool_size=(2, 2)))
   model.add(Dropout(0.25))

   model.add(Flatten())
   model.add(Dense( 1024,init='normal'))
   model.add(Activation('tanh'))
   model.add(Dropout(0.25))
   model.add(Dense( 10,))
   model.add(Activation('softmax'))

[titu1994]

This isn't related to DenseNets but no problem I guess.

Your network uses tanh activation, swap that for relu. Remove init=normal from the Dense layer after flatten.