1. 數據準備

在文件夾下分別建立訓練目錄train，驗證目錄validation，測試目錄test，每個目錄下建立dogs和cats兩個目錄，在dogs和cats目錄下分別放入拍攝的狗和貓的圖片，圖片的大小可以不一樣。

2. 數據讀取

# 存儲數據集的目錄

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

# 訓練、驗證數據集的目錄

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

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

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

# 貓訓練圖片所在目錄

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

# 狗訓練圖片所在目錄

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

# 貓驗證圖片所在目錄

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

# 狗驗證數據集所在目錄

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. 模型訓練

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圖片

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. 模型評估

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. 預測

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)

代碼在spyder下運行正常，一般情況下，可以將文件分為兩個部分，一部分為Train.py，包含深度學習模型建立、訓練和模型的存儲，另一部分Predict.py，包含模型的讀取，評價和預測

補充知識：keras 貓狗大戰自搭網絡以及vgg16應用

導入模塊

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

加載數據集

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

讀取數據集

# 讀取圖片

WIDTH = 150

HEIGHT = 150

CHANNELS = 3

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

查看數據集信息

# 統計y

sns.countplot(y)

# 顯示圖片

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

自定義神經網絡層數

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

# 第一層

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()

訓練模型

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))

復用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

編譯模型

model_vgg16 = vgg16_model()

model_vgg16.summary()

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

訓練模型

# 訓練模型

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))

以上這篇keras分類之二分類實例(Cat and dog)就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持腳本之家。

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