本文将介绍：
使用keras实现resnet50模型
实现迁移学习-finetune
一，下载kaggle-cifar10数据
下载dataset到本地目录cifar10中
二，实现tensorflow动态按需分配GPU
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import sklearn
import sys
import tensorflow as tf
import time
from tensorflow import keras
print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
print(module.__name__, module.__version__)
# 一,实现tensorflow动态按需分配GPU
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
三，读取训练集、测试集的csv文件数据和训练集、测试集数据对应关系
# 二，读取训练集、测试集的csv文件数据和训练集、测试集数据对应关系
class_names = [
'airplane',
'automobile',
'bird',
'cat',
'deer',
'dog',
'frog',
'horse',
'ship',
'truck',
]
train_lables_file = './cifar10/trainLabels.csv'
test_csv_file = './cifar10/sampleSubmission.csv'
train_folder = './cifar10/train/'
test_folder = './cifar10/test'
def parse_csv_file(filepath, folder):
"""Parses 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(' ').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_lables_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))
四，将对应关系转换为dataframe类型
# 四，将对应关系转换为dataframe类型
# 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)
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())
五，使用ImageDataGenerator加载数据并做数据增强
# 五，使用ImageDataGenerator加载数据并做数据增强
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)
六，查看generator数据
for i in range(2):
x, y = train_generator.next()
print(x.shape, y.shape)
print(y)
七，构建模型
# 七，构建模型
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),
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()
八，训练模型
# 八，训练模型
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)
九，打印模型训练曲线
# 九，打印模型训练曲线
def plot_learning_curves(history, label, epcohs, min_value, max_value):
data = {}
data[label] = history.history[label]
data['val_'+label] = history.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, 0, 2)
十，使用keras.ImageDataGenerator加载测试集数据
# 十，使用keras.ImageDataGenerator加载测试集数据
test_datagen = keras.preprocessing.image.ImageDataGenerator(
rescale = 1./255)
test_generator = valid_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)
十一，使用测试集预测模型结果
# 十一，使用测试集预测模型结果
test_predict = model.predict_generator(test_generator,
workers = 10,
use_multiprocessing = True)
1，测试集预测模型结果维度形状
print(test_predict.shape)
2，抽取前5条数据查看
print(test_predict[0:5])
3，取结果数值为最大的为预测结果
test_predict_class_indices = np.argmax(test_predict, axis = 1)
4，取前5条结果查看
print(test_predict_class_indices[0:5])
5，将结果转化为特征名称
test_predict_class = [class_names[index]
for index in test_predict_class_indices]
查看前五条结果
print(test_predict_class[0:5])
十二，将预测结果写入到submission.csv文件中，并在kaggle上提交
# 十二，将预测结果写入到submission.csv文件中，并在kaggle上提交
def generate_submissions(filename, predict_class):
with open(filename, 'w') as f:
f.write('id,label ')
for i in range(len(predict_class)):
f.write('%d,%s ' % (i+1, predict_class[i]))
output_file = "./cifar10/submission.csv"
generate_submissions(output_file, test_predict_class)
十三，总结代码
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import sklearn
import sys
import tensorflow as tf
import time
from tensorflow import keras
print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
print(module.__name__, module.__version__)
# 一,实现tensorflow动态按需分配GPU
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
# 二，读取训练集、测试集的csv文件数据和训练集、测试集数据对应关系
class_names = [
'airplane',
'automobile',
'bird',
'cat',
'deer',
'dog',
'frog',
'horse',
'ship',
'truck',
]
train_lables_file = './cifar10/trainLabels.csv'
test_csv_file = './cifar10/sampleSubmission.csv'
train_folder = './cifar10/train/'
test_folder = './cifar10/test'
def parse_csv_file(filepath, folder):
"""Parses 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(' ').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_lables_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))
# 四，将对应关系转换为dataframe类型
# 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)
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())
# 五，使用ImageDataGenerator加载数据并做数据增强
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',
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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)
# 六，查看generator数据
for i in range(2):
x, y = train_generator.next()
print(x.shape, y.shape)
print(y)
# 七，构建模型
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),
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()
# 八，训练模型
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)
# 九，打印模型训练曲线
def plot_learning_curves(history, label, epcohs, min_value, max_value):
data = {}
data[label] = history.history[label]
data['val_'+label] = history.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, 0, 2)
# 十，使用keras.ImageDataGenerator加载测试集数据
test_datagen = keras.preprocessing.image.ImageDataGenerator(
rescale = 1./255)
test_generator = valid_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)
# 十一，使用测试集预测模型结果
test_predict = model.predict_generator(test_generator,
workers = 10,
use_multiprocessing = True)
# 1，测试集预测模型结果维度形状
print(test_predict.shape)
# 2，抽取前5条数据查看
print(test_predict[0:5])
# 3，取结果数值为最大的为预测结果
test_predict_class_indices = np.argmax(test_predict, axis = 1)
# 4，取前5条结果查看
print(test_predict_class_indices[0:5])
# 5，将结果转化为特征名称
test_predict_class = [class_names[index]
for index in test_predict_class_indices]
# 查看前五条结果
print(test_predict_class[0:5])
# 十二，将预测结果写入到submission.csv文件中，并在kaggle上提交
def generate_submissions(filename, predict_class):
with open(filename, 'w') as f:
f.write('id,label ')
for i in range(len(predict_class)):
f.write('%d,%s ' % (i+1, predict_class[i]))
output_file = "./cifar10/submission.csv"
generate_submissions(output_file, test_predict_class)