minimal.vgg2lstm-stack.py 文件源码

def train():
  c_i = pickle.loads( open("c_i.pkl", "rb").read() )
  i_c = {i:c for c,i in c_i.items() }
  xss = []
  yss = []
  for gi, pkl in enumerate(glob.glob("data/*.pkl")):
    if gi > 500:
      break
    o    = pickle.loads( open(pkl, "rb").read() )
    img  = o["image"] 
    kana = o["kana"]
    print( kana )
    xss.append( np.array(img) )
    ys    = [[0. for i in range(128) ] for j in range(50)]

    for i,k in enumerate(list(kana[:50])):
      try:
        ys[i][c_i[k]] = 1.
      except KeyError as e:
        print(e)
    yss.append( ys )
  Xs = np.array( xss )
  Ys = np.array( yss )
  print(Xs.shape)
  #optims = [Adam(lr=0.001), SGD(lr=0.01)]
  optims = [Adam(), SGD(), RMSprop()]
  if '--resume' in sys.argv:
    """
    optims = [  Adam(lr=0.001), \
                                Adam(lr=0.0005), \
                                Adam(lr=0.0001), \
                                Adam(lr=0.00005), \
                                SGD(lr=0.01), \
                                SGD(lr=0.005), \
                                SGD(lr=0.001), \
                                SGD(lr=0.0005), \
                                ]
    """
    model = sorted( glob.glob("models/*.h5") ).pop(0)
    print("loaded model is ", model)
    t2i.load_weights(model)

  for i in range(2000):
    print_callback = LambdaCallback(on_epoch_end=callbacks)
    batch_size = random.choice( [8] )
    random_optim = random.choice( optims )
    print( random_optim )
    t2i.optimizer = random_optim
    t2i.fit( Xs, Ys,  shuffle=True, batch_size=batch_size, epochs=20, callbacks=[print_callback] )
    if i%50 == 0:
      t2i.save("models/%9f_%09d.h5"%(buff['loss'], i))
    lossrate = buff["loss"]
    os.system("echo \"{} {}\" `date` >> loss.log".format(i, lossrate))
    print("saved ..")
    print("logs...", buff )