使用cnn的植物图像识别的验证集代码

以下是使用CNN进行植物图像识别的验证集代码。这份代码使用了Keras库和TensorFlow后端。

首先是导入必要的库和模块：

import os
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras import backend as K

接下来是设置超参数：

# 设置超参数
img_width, img_height = 150, 150
train_data_dir = 'data/train'
validation_data_dir = 'data/validation'
nb_train_samples = 2000
nb_validation_samples = 800
epochs = 50
batch_size = 16

然后是处理数据：

# 如果使用 TensorFlow 后端，需要将通道维度放到最后
if K.image_data_format() == 'channels_last':
    input_shape = (img_width, img_height, 3)
else:
    input_shape = (3, img_width, img_height)

# 数据增强
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')

然后是构建模型：

# 构建模型
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(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

最后是训练模型：

# 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_weights('models/plant_classifier.h5')

以上是使用CNN进行植物图像识别的验证集代码。