6、 Project practice --- identifying cats and dogs

One 、 Prepare the dataset

kagglecatsanddogs The Internet is full of them , I won't upload here , You can send a private letter if you need it

Guide pack

import os
import zipfile
import random
import shutil
import tensorflow as tf
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from shutil import copyfile

Pictures of cats and dogs 12500 Zhang

print(len(os.listdir('./temp/cats/')))
print(len(os.listdir('./temp/dogs/')))
""" 12500 12500 """

Generate training data folder and test data folder

import os
import zipfile
import random
import shutil
import tensorflow as tf
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from shutil import copyfile


def create_dir(file_dir):
    if os.path.exists(file_dir):
        print("True")
        shutil.rmtree(file_dir)# Delete and create 
        os.makedirs(file_dir)
    else:
        os.makedirs(file_dir)


cat_source_dir = "./temp/cats/"
train_cats_dir = "./temp/train/cats/"
test_cats_dir = "./temp/test/cats/"

dot_source_dir = "./temp/dogs/"
train_dogs_dir = "./temp/train/dogs/"
test_dogs_dir = "./temp/test/dogs/"


create_dir(train_cats_dir)# Create a cat's training set folder 
create_dir(test_cats_dir)# Create the cat's test set folder 
create_dir(train_dogs_dir)# Create a dog's training set folder 
create_dir(test_dogs_dir)# Create the dog's test set folder 

""" True True True True """

Press... For the total dog and cat image 9：1 Divided into training set and test set , Cat and dog each 12500 Zhang
Final temp/train/cats and temp/train/dogs Each of the two folders 12500 * 0.9=11250 Zhang
temp/test/cats and temp/test/dogs These two folders are 12500 * 0.1=1250 Zhang
cats and dogs For a total of cat and dog images
test and train Dataset file prepared for

import os
import zipfile
import random
import shutil
import tensorflow as tf
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from shutil import copyfile


def split_data(source,train,test,split_size):
    files = []
    for filename in os.listdir(source):
        file = source + filename
        if os.path.getsize(file)>0:
            files.append(filename)
        else:
            print(filename + "is zero file,please ignoring")
            
        train_length = int(len(files)*split_size)
        test_length = int(len(files)-train_length)
        shuffled_set = random.sample(files,len(files))
        train_set = shuffled_set[0:train_length]
        test_set = shuffled_set[-test_length:]
        
        for filename in train_set:
            this_file = source + filename
            destination = train + filename
            copyfile(this_file,destination)
            
        for filename in test_set:
            this_file = source + filename
            destination = test + filename
            copyfile(this_file,destination)


cat_source_dir = "./temp/cats/"
train_cats_dir = "./temp/train/cats/"
test_cats_dir = "./temp/test/cats/"

dot_source_dir = "./temp/dogs/"
train_dogs_dir = "./temp/train/dogs/"
test_dogs_dir = "./temp/test/dogs/"


split_size = 0.9
split_data(cat_source_dir,train_cats_dir,test_cats_dir,split_size)
split_data(dog_source_dir,train_dogs_dir,test_dogs_dir,split_size)

Two 、 Model building and training

First, normalize the data , Optimize the pretreatment

import os
import zipfile
import random
import shutil
import tensorflow as tf
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from shutil import copyfile


train_dir = "./temp/train/"
train_datagen = ImageDataGenerator(rescale=1.0/255.0)# Optimize the network , Normalize first 
train_generator = train_datagen.flow_from_directory(train_dir,batch_size=100,class_mode='binary',target_size=(150,150))# Two classification , The input of training samples should be consistent 

validation_dir = "./temp/test/"
validation_datagen = ImageDataGenerator(rescale=1.0/255.0)
validation_generator = validation_datagen.flow_from_directory(validation_dir,batch_size=100,class_mode='binary',target_size=(150,150))
""" Found 22500 images belonging to 2 classes. Found 2500 images belonging to 2 classes. """

Build model architecture

model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(16,(3,3),activation='relu',input_shape=(150,150,3)),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(32,(3,3),activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(64,(3,3),activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),

    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512,activation='relu'),
    tf.keras.layers.Dense(1,activation='sigmoid')
])
model.compile(optimizer=RMSprop(lr=0.001),loss='binary_crossentropy',metrics=['acc'])

Training models
225： Because the data is 22500 Zhang , Cat and dog each 12500 Zhang , It is important for the training set 11250 Zhang , So the training set is 22500 Zhang , In the first piece of preprocessing code ,batch_size=100 Set up a batch of 100 individual , So there should be 225 batch
epochs=2： Two rounds , That is, all samples are trained once
Each round contains 225 batch , Each batch has 100 Samples

history = model.fit_generator(train_generator,
                             epochs=2,# Conduct 2 Round training , Every round 255 batch 
                             verbose=1,# Don't log every workout ,1 To record 
                             validation_data=validation_generator)
                             
""" Instructions for updating: Use tf.cast instead. Epoch 1/2 131/225 [================>.............] - ETA: 2:03 - loss: 0.7204 - acc: 0.6093 """

history It is the result of the model running process

3、 ... and 、 Analyze the training results

import matplotlib.image as mpimg
import matplotlib.pyplot as plt

acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(len(acc))

epoch It's too short , The result is a straight line , A few more rounds of training should actually be a line chart
Accuracy rate

plt.plot(epochs,acc,'r',"training accuracy")
plt.plot(epochs,val_acc,'b',"validation accuracy")
plt.title("training and validation accuracy")
plt.figure()

Loss value

plt.plot(epochs,loss,'r',"training loss")
plt.plot(epochs,val_loss,'b',"validation loss")
plt.figure()

Four 、 Validation of the model

import numpy as np
from google.colab import files
from tensorflow.keras.preprocessing import image

uploaded = files.upload()
for fn in uploaded.keys():
    path = 'G:/Juptyer_workspace/Tensorflow_mooc/sjj/test/' + fn# This path is the path to be tested with the model 
    img = image.load_img(path,target_size=(150,150))
    x = image.img_to_array(img)# Multidimensional arrays 
    x = np.expand_dims(x,axis=0)# The tensile 
    
    images = np.vstack([x])# Straighten horizontally 
    classes = model.predict(images,batch_size=10)
    print(classes[0])
    if classes[0]>0.5:
        print(fn + "it is a dog")
    else:
        print(fn + "it is a cat")