K.set_image_dim_ordering('th')
    model = Sequential()


    #first set of CONV => CONV => CONV => LReLU => MAXPOOL
    model.add(Convolution2D(64, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (d, h, w), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) ))
    model.add(LeakyReLU(alpha=alp))
    model.add(Convolution2D(64, kernel_size=(3, 3), border_mode="same", data_format='channels_first', input_shape = (64, 33, 33), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) ))
    model.add(LeakyReLU(alpha=alp))
    model.add(Convolution2D(64, kernel_size=(3, 3), border_mode="same", data_format='channels_first', input_shape = (64, 33, 33), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) ))
    model.add(LeakyReLU(alpha=alp))
    model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))

    #second set of CONV => CONV => CONV => LReLU => MAXPOOL
    model.add(Convolution2D(128, kernel_size=(3, 3), border_mode="same", data_format='channels_first', input_shape = (64, 16, 16), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) ))
    model.add(LeakyReLU(alpha=alp))
    model.add(Convolution2D(128, kernel_size=(3, 3), border_mode="same", data_format='channels_first', input_shape = (128, 16, 16), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) ))
    model.add(LeakyReLU(alpha=alp))
    model.add(Convolution2D(128, kernel_size=(3, 3), border_mode="same", data_format='channels_first', input_shape = (128, 16, 16), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) ))
    model.add(LeakyReLU(alpha = alp))
    model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))

    #Fully connected layers

    # FC => LReLU => FC => LReLU
    model.add(Flatten())
    model.add(Dense(256, kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1)))
    model.add(LeakyReLU(alp))
    model.add(Dropout(dropout))
    model.add(Dense(256, kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1)))
    model.add(LeakyReLU(alp))
    model.add(Dropout(dropout))

    # FC => SOFTMAX
    model.add(Dense(classes, kernel_initializer = 'glorot_normal', bias_initializer = constant(0.1)))
    model.add(Activation("softmax"))