tensorflow识别猫与狗

问题

现在有很多的图片,里面分别有猫与狗,识别这些图片,区分猫与狗

设计解决这个问题的思路

1、下载与放置训练图片

2、现在对应的依赖,tensorflow、numpy等等

3、解析文件名,识别dog还是cat

4、建模

5、对模型进行训练

6、用测试模型进行验证

7、输出结果

8、优化模型 to step4

[1]图片地址

Dogs vs. Cats | Kaggle 现在数据,现在速度比较慢,可以使用网盘。

网盘地址(提取码:lhrr)

tensorflow识别猫与狗_第1张图片

【2】处理训练集的数据结构

import os

filenames = os.listdir('./dogs-vs-cats/train’)

# 动物类型

categories = []

for filename in filenames:

    category = filename.split('.')[0]

    categories.append(category)


import pandas as pd

# 结构化数据

df = pd.DataFrame({

    'filename':filenames,

    'category':categories
})

#展示对应的数据

import random

from keras.preprocessing import image

import matplotlib.pyplot as plt

## 看看结构化之后的结果

print(df.head())

print(df.tail())

print(df['category'].value_counts())

df['category'].value_counts().plot(kind = 'bar')

plt.show()

# 展示个图片看看

sample = random.choice(filenames)
image = image.load_img('./dogs-vs-cats/train/' + sample)
plt.imshow(image)
plt.show()

【3】出来训练集与验证集

# 切割训练集合

train_df, validate_df = train_test_split(df, test_size = 0.20, random_state = 42)

train_df = train_df.reset_index(drop=True)
validate_df = validate_df.reset_index(drop=True)

print(train_df.head())
print(validate_df.head())

total_train = train_df.shape[0]
total_validate = validate_df.shape[0]

print("Total number of example in training dataset : {0}".format(total_train))
print("Total number of example in validation dataset : {0}".format(total_validate))

【4】创建模型

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, BatchNormalization, Flatten,Dropout
from tensorflow.keras import optimizers


## 创建第一个模型
class Model:

def __init__(self, IMG_WIDTH, IMG_HEIGHT, IMG_CHANNELS):
    self.IMG_WIDTH = IMG_WIDTH
    self.IMG_HEIGHT = IMG_HEIGHT
    self.IMG_CHANNELS = IMG_CHANNELS

def create_model(self):
    model = Sequential()
    #第一层
    #图像空间的2维卷积 32个卷积输出滤波器,卷积窗口的高度和宽度(3,3),输入像素150*150
    model.add(Conv2D(32, (3,3), activation = 'relu', kernel_initializer='he_uniform',
    padding='same',input_shape = (150, 150, 3)))

    #卷积窗口的高度和宽度降低为(2,2)
    model.add(MaxPooling2D((2,2)))

    #第二层
    model.add(Conv2D(64, (3,3), activation = 'relu'))
    model.add(MaxPooling2D((2,2)))

    #第三层
    model.add(Conv2D(128, (3,3), activation = 'relu'))
    model.add(MaxPooling2D((2,2)))

    #第四层
    model.add(Conv2D(128, (3,3), activation = 'relu'))
    model.add(MaxPooling2D((2,2)))

    #Flatten层用来将输入“压平”,即把多维的输入一维化
    model.add(Flatten())
    #全链接层,输出空间的维数
    model.add(Dense(512, activation = 'relu'))
    model.add(Dense(1, activation = 'sigmoid'))
    
    from keras import optimizers
    # 设置损失算法与优化
    model.compile(loss = 'binary_crossentropy', optimizer = optimizers.RMSprop(lr = 1e-4), metrics =['acc'])
    return model

【5】训练模型

# 初始化模型
IMG_WIDTH = 150
IMG_HEIGHT = 150
IMG_CHANNELS = 3

model = Model(IMG_WIDTH, IMG_HEIGHT, IMG_CHANNELS)
model_1 = model.create_model()
model_1.summary()


from keras.preprocessing.image import ImageDataGenerator

#原来是255的像素做 0与1的处理
train_imgdatagen = ImageDataGenerator(rescale = 1./255)
valid_imgdatagen = ImageDataGenerator(rescale = 1./255)


train_generator_m1 = train_imgdatagen.flow_from_dataframe(
                        train_df,
                        directory="./dogs-vs-cats/train",
                        x_col='filename',
                        y_col='category',
                        target_size = (150, 150), # resize image to 150x150
                        batch_size = 64,
                        class_mode = 'binary'
                    )

validation_generator_m1 = valid_imgdatagen.flow_from_dataframe(
                            validate_df,
                            directory="./dogs-vs-cats/train",
                            x_col='filename',
                            y_col='category',
                            target_size = (150, 150), # resize image to 150x150
                            batch_size = 64,
                            class_mode = 'binary'
                    )


import numpy as np

# model 1 开始训练
history_1 = model_1.fit(
        train_generator_m1,
        epochs = 30,
        steps_per_epoch = 100,
        validation_data = validation_generator_m1,
        validation_steps = 50
)



#保存模型
model_1.save('model_1.h5')

【6】打印训练结果

print(np.mean(history_1.history['acc']))
print(np.mean(history_1.history['val_acc']))

【7】形成图像结果

plt.plot(history_1.history['acc'], color = 'black')
plt.plot(history_1.history['val_acc'], color = 'blue')
plt.title('Training and validation accuracy of model 1')
plt.xlabel('Epochs')
plt.ylabel('Accuracy’)4
plt.show()


plt.plot(history_1.history['loss'], color = 'black')
plt.plot(history_1.history['val_loss'], color = 'blue')
plt.title('Training and validation loss of model 1')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.show()

tensorflow识别猫与狗_第2张图片

tensorflow识别猫与狗_第3张图片

【8】写个测试类

from numpy.core.fromnumeric import ptp
import pandas as pd
import os
import matplotlib.pyplot as plt

from keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import load_model

model = load_model('model_1.h5')

test_filenames = os.listdir('./dogs-vs-cats/test3')

test_df = pd.DataFrame(
         {
             'id' : test_filenames
         } 
         )

print(test_df)
test_gen = ImageDataGenerator(rescale = 1./255)

test_generator = test_gen.flow_from_dataframe(    
                    test_df,
                    "./dogs-vs-cats/test3", 
                    x_col='id',
                    y_col=None,
                    target_size = (128, 128), # resize image to 150x150
                    batch_size = 20,
                    class_mode = None,
                    shuffle=False,
                    validate_filenames=False
                  )

predictions = model.predict(test_generator)
print(predictions)
pred = [1 if p > 0.5 else 0 for p in predictions]

test_df['category'] = pred

test_df['category'].value_counts().plot.bar()
plt.show()

得出的结果

tensorflow识别猫与狗_第4张图片

模型上还是有所偏差,后续可以优化模型提升正确率

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