new_contents: import cupy as cp
  from files import mnist
  from files import neural_layer as neural
  from files import tenki_num as t
  import matplotlib.pyplot as plt
  import gc
  import chainer.cuda as cuda
  import time
  
  print("Now Loading...")
  mas,tenki,mastest,tenkitest = t.loadPic()
  mas = mas.astype(cp.float32)
  mas /= 255.0
  x_train = mas[:350]
  t_train = tenki[:350]
  x_test = mastest[:200]
  t_test = tenkitest[:200]
  A = time.time()
  
  del mas,tenki,mastest,tenkitest
  gc.collect()
  
  network = neural.NeuralNet()
  ans = 0
  print("Start Train")
  for i in range(350):
      a = [i]
      x_batch = x_train[a]
      t_batch = t_train[a]
      grad = network.gradient(x_batch,t_batch)
      for key in ('W1','b1','W2','b2','W3','b3'):
          network.params[key] -= 0.01 * grad[key]
      #network.update(x_batch,t_batch)
  
      y = network.predict(x_batch)
      if cp.argmax(y[0,:]) == cp.argmax(t_batch[0,:]):
          answer = "〇"
      else:
          answer = "×"
      print(i,"seikai",cp.argmax(t_batch[0,:]),"yosoku",cp.argmax(y[0,:]),answer)
  B = time.time()
  print(B-A)
  #for key in ('W1','b1','W2','b2','W3','b3'):
      #network.params[key] = cuda.to_cpu(network.params[key])
      #print(type(network.params[key]))
  print("Start Test")
  for i in range(200):
      a = [i]
      x_batch = x_test[a]
      t_batch = t_test[a]
      #network.update(x_batch,t_batch)
  
      y = network.predict(x_batch)
      if cp.argmax(y[0,:]) == cp.argmax(t_batch[0,:]):
          answer = "〇"
          ans += 1
      else:
          answer = "×"
      print(i,"seikai",cp.argmax(t_batch[0,:]),"yosoku",cp.argmax(y[0,:]),answer)
  print((ans/200)*100)
  #300,200->47.5
  
subject: save run0