python中利用Keras建几层卷积神经网络来做Kaggle猫狗识别_index=np.arange(2000)

import numpy as np
 
np.random.seed(1336)  # for reproducibility
from keras.datasets import mnist
from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
import h5py
from PIL import Image
import cv2
import matplotlib.pyplot as pyplot
 
# 全局变量
batch_size = 20
nb_classes = 2
epochs = 20
 
trainImagesNum = 4000
testImagesNum = 2000
# input image dimensions
img_rows, img_cols = 128, 128
# number of convolutional filters to use
nb_filters = 128
# size of pooling area for max pooling
pool_size = (2, 2)
# convolution kernel size
kernel_size = (3, 3)
X_train = np.zeros([2000, img_rows, img_cols, 3])
X_test = np.zeros([2000, img_rows, img_cols, 3])
 
print(type(X_train), X_train.shape)
for i in range(2000):
    if (np.mod(i, 50) &#