写一个用Python图像识别的代码用卷积神经网络算法

import numpy as np
import matplotlib.pyplot as plt
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense# 设置图片的尺寸
img_width, img_height = 150, 150# 设置数据集的路径
train_data_dir = 'data/train'
validation_data_dir = 'data/validation'# 设置数据集中图片的数量
nb_train_samples = 2000
nb_validation_samples = 800# 设置每次训练时候所使用的图片数量
batch_size = 16# 设置神经网络的层数
epochs = 30# 设置卷积神经网络的层数
input_shape = (img_width, img_height, 3)# 创建模型
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))# 编译模型
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])# 准备数据
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)test_datagen = ImageDataGenerator(rescale=1. / 255)train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')# 训练模型
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)# 保存模型
model.save('model.h5')答：我已经用Python编写了一段使用卷积神经网络算法进行图像识别的代码，如下所示：