深度学习--猫狗图像分类

1、环境介绍
python3.6，tensorflow1.4版本，pycharm编译器
2、函数库导入

import cv2
import matplotlib.pyplot as plt
import os, PIL, pathlib
import numpy as np
from tensorflow.keras.layers import Conv2D,MaxPooling2D,Dropout,Dense,Flatten,Activation
import pandas as pd
from tensorflow.keras.models import Sequential
import warnings
from tensorflow import keras
import pathlib
from tensorflow.keras.layers import BatchNormalization
import tensorflow as tf

3、数据集获取
数据集链接，含有猫狗两类图像，图像尺寸不等，猫狗图片各1000张。

猫：

狗：
4、神经网络搭建：

def createModel(num_classes):
    model = Sequential()  # 顺序模型
    model.add(
        Conv2D(16, (5, 5), strides=(2, 2), padding="same", input_shape=(256, 256, 3), data_format='channels_last',
               kernel_initializer='uniform', activation="relu"))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(32, (3, 3), strides=(2, 2), activation="relu"))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))

    model.add(Conv2D(64, (3, 3), activation="relu"))
    model.add(Conv2D(128, (3, 3), activation="relu"))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name="pool5"))

    model.add(Conv2D(256, (1, 1), activation="relu"))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(256))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.5))
    model.add(Dense(num_classes))
    model.add(BatchNormalization())
    model.add(Activation("softmax"))
    model.summary()
    return model

神经网