從零基礎入門Tensorflow2.0 ----六、32cifar10數據訓練

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0. 前言

cifar10 訓練

1. 代碼部分

1. 導入模塊

%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn
import os,sys
import tensorflow as tf
import time
from tensorflow import keras
os.environ['CUDA_VISIBLE_DEVICES'] = '/gpu:0'
print(tf.__version__)
print(sys.version_info)
for module in mpl,pd,sklearn,tf,keras:
    print(module.__name__,module.__version__)

2. 讀取數據

class_names = [
    'airplane',
    'automobile',
    'bird',
    'cat',
    'deer',
    'dog',
    'frog',
    'horse',
    'ship',
    'truck'
]
train_labels_file = './cifar10/trainLabels.csv'
test_csv_file = './cifar10/sampleSubmission.csv'
train_folder = './cifar10/train'
test_folder = './cifar10/test'

def parse_csv_file(filepath,folder):
    """parsers csv files into(filename(path),label) format"""
    results = []
    with open(filepath,'r') as f:
        lines = f.readlines()[1:]
    for line in lines:
        image_id,label_str = line.strip('\n').split(',')
        image_full_path = os.path.join(folder,image_id + '.png')
        results.append((image_full_path,label_str))
    return results


train_labels_info = parse_csv_file(train_labels_file,train_folder)
test_csv_info = parse_csv_file(test_csv_file,test_folder)

import pprint
pprint.pprint(train_labels_info[0:5])
pprint.pprint(test_csv_info[0:5])
print(len(train_labels_info),len(test_csv_info))

2.2 劃分數據

# train_df = pd.DataFrame(train_labels_info)
train_df = pd.DataFrame(train_labels_info[0:45000])
valid_df = pd.DataFrame(train_labels_info[45000:])
test_df = pd.DataFrame(test_csv_info)

# 修改列名
train_df.columns = ['filepath','class']
valid_df.columns = ['filepath','class']
test_df.columns = ['filepath','class']

print(train_df.head())
print(valid_df.head())
print(test_df.head())

3. 讀取圖片

# 讀取圖片
height = 32
width = 32
channels = 3
batch_size = 32
num_classes = 10

train_datagen = keras.preprocessing.image.ImageDataGenerator(
rescale = 1. / 255,
    rotation_range=40,
    width_shift_range=0.2,
    height_shift_range = 0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip = True,
    fill_mode = 'nearest',
)
train_generator = train_datagen.flow_from_dataframe(train_df,directory='./',x_col='filepath',y_col='class',classes=class_names,
                                                    target_size=(height,width),batch_size=batch_size,seed=7,shuffle=True,
                                                    class_mode='sparse',)


valid_datagen = keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
valid_generator = valid_datagen.flow_from_dataframe(valid_df,directory='./',x_col='filepath',y_col='class',classes=class_names,
                                                    target_size=(height,width),batch_size=batch_size,seed=7,shuffle=False,
                                                    class_mode="sparse")

train_num = train_generator.samples
valid_num = valid_generator.samples
print(train_num,valid_num)

# 讀取數據
for i in range(2):
    x,y = train_generator.next()
    print(x.shape,y.shape)
    print(y)

4. 模型搭建

model = keras.models.Sequential([
    keras.layers.Conv2D(filters=128,kernel_size=3,padding='same',activation='relu',input_shape=[width,height,channels]),
    keras.layers.BatchNormalization(),
    keras.layers.Conv2D(filters=128,kernel_size=3,padding='same',activation='relu'),
    keras.layers.BatchNormalization(),
    keras.layers.MaxPool2D(pool_size=2),
    
    keras.layers.Conv2D(filters=256,kernel_size=3,padding='same',activation='relu'),
    keras.layers.BatchNormalization(),
    keras.layers.Conv2D(filters=256,kernel_size=3,padding='same',activation='relu'),
    keras.layers.BatchNormalization(),
    keras.layers.MaxPool2D(pool_size=2),
    
    keras.layers.Conv2D(filters=512,kernel_size=3,padding='same',activation='relu'),
    keras.layers.BatchNormalization(),
    keras.layers.Conv2D(filters=512,kernel_size=3,padding='same',activation='relu'),
    keras.layers.BatchNormalization(),
    keras.layers.MaxPool2D(pool_size=2),
    
    # 展平
    keras.layers.Flatten(),
    keras.layers.Dense(512,activation='relu'),
    keras.layers.Dense(num_classes,activation='softmax'),
])

model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
model.summary()

5. 訓練

epochs = 20
history = model.fit_generator(train_generator,steps_per_epoch = train_num // batch_size,epochs=epochs,
                            validation_data = valid_generator,validation_steps=valid_num // batch_size)

6. 學習曲線

# 學習曲線
def plot_learning_curves(hsitory,label,epochs,min_value,max_value):
    data = {}
    data[label] = history.history[label]
    data['val_' + label] = hsitory.history['val_' + label]
    pd.DataFrame(data).plot(figsize=(8,5))
    plt.grid(True)
    plt.axis([0,epochs,min_value,max_value])
    plt.show()

plot_learning_curves(history,'accuracy',epochs,0,1)
plot_learning_curves(history,'loss',epochs,1.5,2.5)

7. 測試集上

test_datagen = keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_dataframe(test_df,directory='./',x_col='filepath',y_col='class',classes=class_names,
                                                    target_size=(height,width),batch_size=batch_size,seed=7,shuffle=False,
                                                    class_mode="sparse")
test_num = test_generator.samples
print(test_num)

test_predict = model.predict_generator(test_generator,workers=10,use_multiprocessing=False)
# True 進程； False:線程

print(test_predict.shape)

print(test_predict[0:5])

test_predict_class_indices = np.argmax(test_predict,axis=1)
print(test_predict_class_indices[0:5])

test_predict_class=[class_names[index] for index in test_predict_class_indices]
print(test_predict_class[0:5])

def generate_submissions(fielname,predict_class):
    with open(filename,'w') as f:
        f.write('id,label\n')
        for i in rangelen((predict_class)):
            f.write('%d,%s\n'%(i+1,predict_class[i]))

output_file = './cifar10/submission.csv'
generate_submissions(output_file,test_predict_class)