1. 数据准备

在文件夹下分别建立训练目录train，验证目录validation，测试目录test，每个目录下建立dogs和cats两个目录，在dogs和cats目录下分别放入拍摄的狗和猫的图片，图片的大小可以不一样。

2. 数据读取

# 存储数据集的目录

base_dir = 'E:/python learn/dog_and_cat/data/'

# 训练、验证数据集的目录

train_dir = os.path.join(base_dir, 'train')

validation_dir = os.path.join(base_dir, 'validation')

test_dir = os.path.join(base_dir, 'test')

# 猫训练图片所在目录

train_cats_dir = os.path.join(train_dir, 'cats')

# 狗训练图片所在目录

train_dogs_dir = os.path.join(train_dir, 'dogs')

# 猫验证图片所在目录

validation_cats_dir = os.path.join(validation_dir, 'cats')

# 狗验证数据集所在目录

validation_dogs_dir = os.path.join(validation_dir, 'dogs')

print('total training cat images:', len(os.listdir(train_cats_dir)))

print('total training dog images:', len(os.listdir(train_dogs_dir)))

print('total validation cat images:', len(os.listdir(validation_cats_dir)))

print('total validation dog images:', len(os.listdir(validation_dogs_dir)))

3. 模型建立

# 搭建模型

model = Sequential()

model.add(Conv2D(32, (3, 3), activation='relu',

input_shape=(150, 150, 3)))

model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(64, (3, 3), activation='relu'))

model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(128, (3, 3), activation='relu'))

model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(128, (3, 3), activation='relu'))

model.add(MaxPooling2D((2, 2)))

model.add(Flatten())

model.add(Dense(512, activation='relu'))

model.add(Dense(1, activation='sigmoid'))

print(model.summary())

model.compile(loss='binary_crossentropy',

optimizer=RMSprop(lr=1e-4),

metrics=['acc'])

4. 模型训练

train_datagen = ImageDataGenerator(rescale=1./255)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(

train_dir, # target directory

target_size=(150, 150), # resize图片

batch_size=20,

class_mode='binary'

)

validation_generator = test_datagen.flow_from_directory(

validation_dir,

target_size=(150, 150),

batch_size=20,

class_mode='binary'

)

for data_batch, labels_batch in train_generator:

print('data batch shape:', data_batch.shape)

print('labels batch shape:', labels_batch.shape)

break

hist = model.fit_generator(

train_generator,

steps_per_epoch=100,

epochs=10,

validation_data=validation_generator,

validation_steps=50

)

model.save('cats_and_dogs_small_1.h5')

5. 模型评估

acc = hist.history['acc']

val_acc = hist.history['val_acc']

loss = hist.history['loss']

val_loss = hist.history['val_loss']

epochs = range(len(acc))

plt.plot(epochs, acc, 'bo', label='Training acc')

plt.plot(epochs, val_acc, 'b', label='Validation acc')

plt.title('Training and validation accuracy')

plt.legend()

plt.figure()

plt.figure()

plt.plot(epochs, loss, 'bo', label='Training loss')

plt.plot(epochs, val_loss, 'b', label='Validation loss')

plt.legend()

plt.show()

6. 预测

imagename = 'E:/python learn/dog_and_cat/data/validation/dogs/dog.2026.jpg'

test_image = image.load_img(imagename, target_size = (150, 150))

test_image = image.img_to_array(test_image)

test_image = np.expand_dims(test_image, axis=0)

result = model.predict(test_image)

if result[0][0] == 1:

prediction ='dog'

else:

prediction ='cat'

print(prediction)

代码在spyder下运行正常，一般情况下，可以将文件分为两个部分，一部分为Train.py，包含深度学习模型建立、训练和模型的存储，另一部分Predict.py，包含模型的读取，评价和预测

补充知识：keras 猫狗大战自搭网络以及vgg16应用

导入模块

import os

import numpy as np

import tensorflow as tf

import random

import seaborn as sns

import matplotlib.pyplot as plt

import keras

from keras.models import Sequential, Model

from keras.layers import Dense, Dropout, Activation, Flatten, Input,BatchNormalization

from keras.layers.convolutional import Conv2D, MaxPooling2D

from keras.optimizers import RMSprop, Adam, SGD

from keras.preprocessing import image

from keras.preprocessing.image import ImageDataGenerator

from keras.applications.vgg16 import VGG16, preprocess_input

from sklearn.model_selection import train_test_split

加载数据集

def read_and_process_image(data_dir,width=64, height=64, channels=3, preprocess=False):

train_images= [data_dir + i for i in os.listdir(data_dir)]

random.shuffle(train_images)

def read_image(file_path, preprocess):

img = image.load_img(file_path, target_size=(height, width))

x = image.img_to_array(img)

x = np.expand_dims(x, axis=0)

# if preprocess:

# x = preprocess_input(x)

return x

def prep_data(images, proprocess):

count = len(images)

data = np.ndarray((count, height, width, channels), dtype = np.float32)

for i, image_file in enumerate(images):

image = read_image(image_file, preprocess)

data[i] = image

return data

def read_labels(file_path):

labels = []

for i in file_path:

label = 1 if 'dog' in i else 0

labels.append(label)

return labels

X = prep_data(train_images, preprocess)

labels = read_labels(train_images)

assert X.shape[0] == len(labels)

print("Train shape: {}".format(X.shape))

return X, labels

读取数据集

# 读取图片

WIDTH = 150

HEIGHT = 150

CHANNELS = 3

X, y = read_and_process_image('D:\\Python_Project\\train\\',width=WIDTH, height=HEIGHT, channels=CHANNELS)

查看数据集信息

# 统计y

sns.countplot(y)

# 显示图片

def show_cats_and_dogs(X, idx):

plt.figure(figsize=(10,5), frameon=True)

img = X[idx,:,:,::-1]

img = img/255

plt.imshow(img)

plt.show()

for idx in range(0,3):

show_cats_and_dogs(X, idx)

train_X = X[0:17500,:,:,:]

train_y = y[0:17500]

test_X = X[17500:25000,:,:,:]

test_y = y[17500:25000]

train_X.shape

test_X.shape

自定义神经网络层数

input_layer = Input((WIDTH, HEIGHT, CHANNELS))

# 第一层

z = input_layer

z = Conv2D(64, (3,3))(z)

z = BatchNormalization()(z)

z = Activation('relu')(z)

z = MaxPooling2D(pool_size = (2,2))(z)

z = Conv2D(64, (3,3))(z)

z = BatchNormalization()(z)

z = Activation('relu')(z)

z = MaxPooling2D(pool_size = (2,2))(z)

z = Conv2D(128, (3,3))(z)

z = BatchNormalization()(z)

z = Activation('relu')(z)

z = MaxPooling2D(pool_size = (2,2))(z)

z = Conv2D(128, (3,3))(z)

z = BatchNormalization()(z)

z = Activation('relu')(z)

z = MaxPooling2D(pool_size = (2,2))(z)

z = Flatten()(z)

z = Dense(64)(z)

z = BatchNormalization()(z)

z = Activation('relu')(z)

z = Dropout(0.5)(z)

z = Dense(1)(z)

z = Activation('sigmoid')(z)

model = Model(input_layer, z)

model.compile(

optimizer = keras.optimizers.RMSprop(),

loss = keras.losses.binary_crossentropy,

metrics = [keras.metrics.binary_accuracy]

)

model.summary()

训练模型

history = model.fit(train_X,train_y, validation_data=(test_X, test_y),epochs=10,batch_size=128,verbose=True)

score = model.evaluate(test_X, test_y, verbose=0)

print("Large CNN Error: %.2f%%" %(100-score[1]*100))

复用vgg16模型

def vgg16_model(input_shape= (HEIGHT,WIDTH,CHANNELS)):

vgg16 = VGG16(include_top=False, weights='imagenet',input_shape=input_shape)

for layer in vgg16.layers:

layer.trainable = False

last = vgg16.output

# 后面加入自己的模型

x = Flatten()(last)

x = Dense(256, activation='relu')(x)

x = Dropout(0.5)(x)

x = Dense(256, activation='relu')(x)

x = Dropout(0.5)(x)

x = Dense(1, activation='sigmoid')(x)

model = Model(inputs=vgg16.input, outputs=x)

return model

编译模型

model_vgg16 = vgg16_model()

model_vgg16.summary()

model_vgg16.compile(loss='binary_crossentropy',optimizer = Adam(0.0001), metrics = ['accuracy'])

训练模型

# 训练模型

history = model_vgg16.fit(train_X,train_y, validation_data=(test_X, test_y),epochs=5,batch_size=128,verbose=True)

score = model_vgg16.evaluate(test_X, test_y, verbose=0)

print("Large CNN Error: %.2f%%" %(100-score[1]*100))

以上这篇keras分类之二分类实例(Cat and dog)就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。