def conv_relu_forward_pool_bn_droput(x,w,b,conv_param,pool_param,droput_param,gamma, beta,bn_param):

  #convolve
  a, conv_cache = conv_forward_fast(x, w, b, conv_param)
  #batchnormalize
  out_conv,cache_batch = spatial_batchnorm_forward(a, gamma , beta, bn_param)
  #nonlinearity
  out_relu,cache_relu = relu_forward(out_conv)
  #dropout
  out_drop, cache_dropout = dropout_forward(out_relu,droput_param)
  #pool
  out_pool, pool_cache = max_pool_forward_fast(out_drop, pool_param)

  out = out_pool

  cache = (conv_cache, cache_batch, cache_relu,cache_dropout,pool_cache)

  return out,cache

def conv_relu_bn_dropout_backward(dout,cache):

  conv_cache, cache_batch, cache_relu,cache_dropout,pool_cache = cache

  ds = max_pool_backward_fast(dout, pool_cache)

  dx_drop = dropout_backward(ds,cache_dropout)

  dx_relu = relu_backward(dx_drop, cache_relu)

  dx_batch, dx_gamma, dx_beta = spatial_batchnorm_backward(dx_relu, cache_batch)

  dx, dw ,db = conv_backward_fast(dx_batch,conv_cache)

  return dx ,dw ,db ,dx_gamma , dx_beta