【人工智能项目】- 深度学习实现猫狗大战

【人工智能项目】- 深度学习实现猫狗大战

【人工智能项目】- 深度学习实现猫狗大战
本次实现猫狗大战,实质上就是猫狗的二分类任务。

环境

!nvidia-smi
Mon Jun 22 04:24:29 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.36.06    Driver Version: 418.67       CUDA Version: 10.1     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |
| N/A   38C    P8    26W / 149W |      0MiB / 11441MiB |      0%      Default |
|                               |                      |                 ERR! |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
!unzip cats_and_dogs.zip
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
import time
import datetime
import sys
import glob

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import class_weight as cw

from keras import Sequential

from keras.models import Model

from keras.layers import LSTM,Activation,Dense,Dropout,Input,Embedding,BatchNormalization,Add,concatenate,Flatten
from keras.layers import Conv1D,Conv2D,Convolution1D,MaxPool1D,SeparableConv1D,SpatialDropout1D,GlobalAvgPool1D,GlobalMaxPool1D,GlobalMaxPooling1D
from keras.layers.pooling import _GlobalPooling1D
from keras.layers import MaxPooling2D,GlobalMaxPooling2D,GlobalAveragePooling2D

from keras.optimizers import RMSprop,Adam

from keras.preprocessing.text import Tokenizer
from keras.preprocessing import sequence

from keras.utils import to_categorical

from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
from keras.callbacks import ReduceLROnPlateau


from keras import __version__
from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam

%matplotlib inline

import warnings
warnings.filterwarnings("ignore")
/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm
Using TensorFlow backend.

读取数据

img_width,img_height = 299, 299    #修正 InceptionV3 的尺寸参数
epochs = 5
batch_size = 32
fc_size = 1024
def get_nb_files(directory):
    """Get number of files by searching directory recursively"""
    if not os.path.exists(directory):
        return 0
    cnt = 0
    for r, dirs, files in os.walk(directory):
        for dr in dirs:
            cnt += len(glob.glob(os.path.join(r, dr + "/*")))       # glob模块是用来查找匹配文件的,后面接匹配规则。
    return cnt
    
# 定义增加最后一个全连接层的函数
def add_new_last_layer(base_model, nb_classes):
    """Add last layer to the convnet

    Args:
        base_model: keras model excluding top
        nb_classes: # of classes

    Returns:
        new keras model with last layer
    """
    x = base_model.output
    x = GlobalAveragePooling2D()(x)
    x = Dense(fc_size, activation='relu')(x) #new FC layer, random init
    predictions = Dense(nb_classes, activation='softmax')(x) #new softmax layer
    model = Model(inputs=base_model.input, outputs=predictions)
    return model

# 定义微调函数
def setup_to_finetune(model):
    """Freeze the bottom NB_IV3_LAYERS and retrain the remaining top layers.

        note: NB_IV3_LAYERS corresponds to the top 2 inception blocks in the inceptionv3 arch

    Args:
        model: keras model
    """
    # for layer in model.layers[:NB_IV3_LAYERS_TO_FREEZE]:
    #     layer.trainable = False
    for layer in model.layers[:]:
        layer.trainable = True
    model.compile(optimizer=Adam(lr=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
train_dir= "./train"
valid_dir = "./validation"
nb_classes = len(glob.glob(train_dir + "/*"))
# nb_val_samples = get_nb_files(val_dir)
epochs = int(epochs)
batch_size = int(batch_size)
train_data = ImageDataGenerator(
    # 浮点数,剪切强度(逆时针方向的剪切变换角度)
    shear_range=0.1,
    # 随机缩放的幅度,若为浮点数,则相当于[lower,upper] = [1 - zoom_range, 1+zoom_range]
    zoom_range=0.1,
    # 浮点数,图片宽度的某个比例,数据提升时图片水平偏移的幅度
    width_shift_range=0.1,
    # 浮点数,图片高度的某个比例,数据提升时图片竖直偏移的幅度
    height_shift_range=0.1,
    # 布尔值,进行随机水平翻转
    horizontal_flip=True,
    # 布尔值,进行随机竖直翻转
    vertical_flip=True,
    # 在 0 和 1 之间浮动。用作验证集的训练数据的比例
#         validation_split=0.3
    )

# 接下来生成验证集,可以参考训练集的写法
validation_data = ImageDataGenerator(
    # 浮点数,剪切强度(逆时针方向的剪切变换角度)
    shear_range=0.1,
    # 随机缩放的幅度,若为浮点数,则相当于[lower,upper] = [1 - zoom_range, 1+zoom_range]
    zoom_range=0.1,
    # 浮点数,图片宽度的某个比例,数据提升时图片水平偏移的幅度
    width_shift_range=0.1,
    # 浮点数,图片高度的某个比例,数据提升时图片竖直偏移的幅度
    height_shift_range=0.1,
    # 布尔值,进行随机水平翻转
    horizontal_flip=True,
    # 布尔值,进行随机竖直翻转
    vertical_flip=True,
)

train_generator = train_data.flow_from_directory(
    # 提供的路径下面需要有子目录
    train_dir,
    # 整数元组 (height, width),默认:(256, 256)。 所有的图像将被调整到的尺寸。
    target_size=(299,299),
    # 一批数据的大小
    batch_size=batch_size,
    # "categorical", "binary", "sparse", "input" 或 None 之一。
    # 默认:"categorical",返回one-hot 编码标签。
    class_mode='categorical',
    seed=0)

validation_generator = train_data.flow_from_directory(
    valid_dir,
    target_size=(299,299),
    batch_size=batch_size,
    class_mode='categorical',
    seed=0)
Found 1800 images belonging to 2 classes.
Found 600 images belonging to 2 classes.

模型准备

# 准备跑起来,首先给 base_model 和 model 赋值,迁移学习和微调都是使用 InceptionV3 的 notop 模型(看 inception_v3.py 源码,此模型是打开了最后一个全连接层),利用 add_new_last_layer 函数增加最后一个全连接层。

base_model = InceptionV3(weights="imagenet", include_top=False)  # include_top=False excludes final FC layer
model = add_new_last_layer(base_model, nb_classes)


print("开始微调:\n")

# fine-tuning
setup_to_finetune(model)
Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
87916544/87910968 [==============================] - 1s 0us/step
开始微调:
model.summary()
Model: "model_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            (None, None, None, 3 0                                            
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, None, None, 3 864         input_1[0][0]                    
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, None, None, 3 96          conv2d_1[0][0]                   
__________________________________________________________________________________________________
activation_1 (Activation)       (None, None, None, 3 0           batch_normalization_1[0][0]      
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, None, None, 3 9216        activation_1[0][0]               
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, None, None, 3 96          conv2d_2[0][0]                   
__________________________________________________________________________________________________
activation_2 (Activation)       (None, None, None, 3 0           batch_normalization_2[0][0]      
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, None, None, 6 18432       activation_2[0][0]               
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, None, None, 6 192         conv2d_3[0][0]                   
__________________________________________________________________________________________________
activation_3 (Activation)       (None, None, None, 6 0           batch_normalization_3[0][0]      
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)  (None, None, None, 6 0           activation_3[0][0]               
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, None, None, 8 5120        max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, None, None, 8 240         conv2d_4[0][0]                   
 
__________________________________________________________________________________________________
global_average_pooling2d_1 (Glo (None, 2048)         0           mixed10[0][0]                    
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 1024)         2098176     global_average_pooling2d_1[0][0] 
__________________________________________________________________________________________________
dense_2 (Dense)                 (None, 2)            2050        dense_1[0][0]                    
==================================================================================================
Total params: 23,903,010
Trainable params: 23,868,578
Non-trainable params: 34,432
__________________________________________________________________________________________________
print("Setting Callbacks")

checkpoint = ModelCheckpoint("model.h5",
                                                     monitor="val_loss",
                                                     save_best_only=True,
                                                     mode="min")

early_stopping = EarlyStopping(monitor="val_loss",
                                                     patience=3,
                                                     verbose=1,
                                                     restore_best_weights=True,
                                                     mode="min")

reduce_lr = ReduceLROnPlateau(monitor="val_loss",
                                                      factor=0.6,
                                                      patience=2,
                                                      verbose=1,
                                                      mode="min")

callbacks=[checkpoint,early_stopping,reduce_lr]
Setting Callbacks

训练

history_ft = model.fit_generator(
    train_generator,
    steps_per_epoch=1800   // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=600    // batch_size,
    callbacks=callbacks)
Epoch 1/20
56/56 [==============================] - 139s 2s/step - loss: 0.1774 - accuracy: 0.9259 - val_loss: 0.0389 - val_accuracy: 0.9358
Epoch 2/20
56/56 [==============================] - 99s 2s/step - loss: 0.0824 - accuracy: 0.9729 - val_loss: 0.1625 - val_accuracy: 0.9648
Epoch 3/20
56/56 [==============================] - 98s 2s/step - loss: 0.0448 - accuracy: 0.9825 - val_loss: 0.0261 - val_accuracy: 0.9577
Epoch 4/20
56/56 [==============================] - 98s 2s/step - loss: 0.0432 - accuracy: 0.9898 - val_loss: 0.1232 - val_accuracy: 0.9789
Epoch 5/20
56/56 [==============================] - 97s 2s/step - loss: 0.0311 - accuracy: 0.9893 - val_loss: 0.1762 - val_accuracy: 0.9366

Epoch 00005: ReduceLROnPlateau reducing learning rate to 5.999999848427251e-05.
Epoch 6/20
56/56 [==============================] - 96s 2s/step - loss: 0.0182 - accuracy: 0.9926 - val_loss: 0.4272 - val_accuracy: 0.9630
Restoring model weights from the end of the best epoch
Epoch 00006: early stopping

展示

# 画曲线
def plot_performance(history=None,figure_directory=None,ylim_pad=[0,0]):
    xlabel="Epoch"
    legends=["Training","Validation"]
    
    plt.figure(figsize=(20,5))
    
    y1=history.history["accuracy"]
    y2=history.history["val_accuracy"]
    
    min_y=min(min(y1),min(y2))-ylim_pad[0]
    max_y=max(max(y1),max(y2))+ylim_pad[0]
    
    plt.subplot(121)
    
    plt.plot(y1)
    plt.plot(y2)
    
    plt.title("Model Accuracy\n",fontsize=17)
    plt.xlabel(xlabel,fontsize=15)
    plt.ylabel("Accuracy",fontsize=15)
    plt.ylim(min_y,max_y)
    plt.legend(legends,loc="upper left")
    plt.grid()
    
    y1=history.history["loss"]
    y2=history.history["val_loss"]
    
    min_y=min(min(y1),min(y2))-ylim_pad[1]
    max_y=max(max(y1),max(y2))+ylim_pad[1]
    
    plt.subplot(122)
    
    plt.plot(y1)
    plt.plot(y2)
    
    plt.title("Model Loss:\n",fontsize=17)
    plt.xlabel(xlabel,fontsize=15)
    plt.ylabel("Loss",fontsize=15)
    plt.ylim(min_y,max_y)
    plt.legend(legends,loc="upper left")
    plt.grid()
    plt.show()

plot_performance(history_ft)

【人工智能项目】- 深度学习实现猫狗大战


import numpy as np
import pandas as pd
import os
import random
import matplotlib.pyplot as plt
from keras import regularizers
from PIL import Image
from tensorflow.keras.preprocessing import image
import glob
# Image processing
from PIL import Image, ImageFile
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from keras.layers import Dropout
from keras.layers import BatchNormalization
from keras.callbacks import EarlyStopping
import keras.backend as K
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPool2D, Flatten
from keras.layers import Activation, Dense
from keras.utils.np_utils import to_categorical
from keras.optimizers import SGD, Adam, Adagrad, RMSprop
from sklearn.metrics import confusion_matrix
import seaborn as sns
import tensorflow as tf
from keras.models import load_model
from keras.preprocessing import image
from sklearn.metrics import accuracy_score

true=[]
pred=[]

garbage_types = ['cats','dogs']
labels = {0:'cats',1:'dogs'}

model_path = 'model.h5'
model = load_model(model_path)

for garbage in garbage_types:
    files = glob.glob("./test/" + str(garbage) + "/*.jpg")
    for myFile in files:
        t = list(labels.keys())[list(labels.values()).index(str(garbage))]
        true.append(t)
        img = image.load_img(myFile,target_size=(224,224))
        input_image = image.img_to_array(img)
        
    
        # 加载模型,加载请注意 model_path 是相对路径, 与当前文件同级。
        # 如果你的模型是在 results 文件夹下的 dnn.h5 模型,则 model_path = 'results/dnn.h5'
        # -------------------------- 实现模型预测部分的代码 ---------------------------
        # expand_dims的作用是把img.shape转换成(1, img.shape[0], img.shape[1], img.shape[2])
        x = np.expand_dims(img, axis=0)
     # 模型预测
        y = model.predict(x)
        predict = labels[np.argmax(y)]
        pred.append(np.argmax(y))


acc = accuracy_score(true,pred)
print(acc)
con_matrix = confusion_matrix(true, pred,
                              labels=[0, 1])
plt.figure(figsize=(10, 10))
plt.title('Prediction of garbage types')
plt.ylabel('True label')
plt.xlabel('Predicted label')
# plt.show(sns.heatmap(con_matrix, annot=True, fmt="d",annot_kws={"size": 7},cmap='Blues',square=True))
ax = sns.heatmap(con_matrix, annot=True, fmt="d", annot_kws={"size": 7}, cmap='Blues', square=True)
bottom, top = ax.get_ylim()
ax.set_ylim(bottom + 0.5, top - 0.5)
plt.show()
0.9716666666666667

【人工智能项目】- 深度学习实现猫狗大战

小结

本次就用迁移学习的方式完成猫狗二分类任务,很简单的任务。
瓷们,还是点赞收藏评论走起来!!!
【人工智能项目】- 深度学习实现猫狗大战

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