I want to improve the loss rate and acc rate of the image discrimination AI model.

import sys
importos
import numpy as np
import pandas aspd
import gc
from keras.models import Sequential
from keras.layers import Convolution 2D, MaxPooling 2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.utils import np_utils

Class TrainModel: 
  def__init__(self):
    input_dir='./Gazo'
    self.nb_classes=len([name for name inos.listdir(input_dir) if name!=".DS_Store"])
    x_train, x_test, y_train, y_test=np.load("./Gakushu.npy")
    # normalize data
    self.x_train=x_train.astype("float")/256
    self.x_test=x_test.astype("float")/256
    self.y_train=np_utils.to_categorical(y_train,self.nb_classes)
    self.y_test=np_utils.to_categorical(y_test,self.nb_classes)
  default train(self, input=None):
    model=Sequential()
    # K=32, M=3, H=3
    if input == None:
      model.add(Convolution2D(32,3,3, border_mode='same', input_shape=self.x_train.shape[1:]))
    else:
      model.add(Convolution2D(32,3, border_mode='same', input_shape=input))
    # K=64, M=3, H=3 (adjustment)
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2,2))))
    model.add (Dropout (0.25))
    model.add(Convolution2D(64,3,3, border_mode='same'))
    model.add(Activation('relu'))
    # K=64, M=3, H=3 (adjustment)
    model.add(Convolution 2D(64,3,3))
    model.add(MaxPooling2D(pool_size=(2,2))))
    model.add (Dropout (0.25))
    model.add(Flatten()) 
    model.add (Dense (512))
    # bias nb_classes
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(self.nb_classes))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
    if input == None:
      # Learn and Save Models
      model.fit(self.x_train, self.y_train, batch_size=32, nb_epoch=int(kaisu), validation_data=(self.x_test, self.y_test))
      hdf5_file="./Aimodel.hdf5"
      model.save_weights(hdf5_file)
      score=model.evaluate(self.x_test, self.y_test, verbose=0)
      print('---------------------------------------------------')
      print("")
      print("Training is over!")
      print("")
      print('loss=', score[0])
      print('Determination Success Rate =', score[1])
      print("")
    return model
if__name__=="__main__":
  args=sys.argv
  train=TrainModel()
  train.train()
  gc.collect()