机器学习 深度学习 hw2
作者：互联网
import numpy as np
import os
import sys
import keras.backend as K
from six.moves import cPickle
#import cv2
import numpy as np
import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import SGD, Adadelta, Adagrad
from keras.utils import np_utils, generic_utils
from keras.utils import plot_model
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
from keras import optimizers
def my_loss(y_true, y_pred):
K.mean(K.square(K.abs(y_pred - y_true)), axis=-1)
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
gpu_options = tf.GPUOptions(allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
#【0】设置超参
batch_size = 32
num_classes = 10
epochs = 5
data_augmentation = True
def load_batch(fpath, label_key='labels'):
f = open(fpath, 'rb')
if sys.version_info < (3,):
d = cPickle.load(f)
else:
d = cPickle.load(f, encoding='bytes')
# decode utf8
d_decoded = {}
for k, v in d.items():
d_decoded[k.decode('utf8')] = v
d = d_decoded
f.close()
data = d['data']
labels = d[label_key]
data = data.reshape(data.shape[0], 3, 32, 32)
return data, labels
def load_data():
dirname = 'd:/ppp/python3/cifar-10-python/cifar-10-batches-py'
num_train_samples = 50000
x_train = np.empty((num_train_samples, 3, 32, 32), dtype='uint8')
y_train = np.empty((num_train_samples,), dtype='uint8')
for i in range(1, 6):
fpath = os.path.join(dirname, 'data_batch_' + str(i))
(x_train[(i - 1) * 10000: i * 10000, :, :, :],
y_train[(i - 1) * 10000: i * 10000]) = load_batch(fpath)
fpath = os.path.join(dirname, 'test_batch')
x_test, y_test = load_batch(fpath)
y_train = np.reshape(y_train, (len(y_train), 1))
y_test = np.reshape(y_test, (len(y_test), 1))
if K.image_data_format() == 'channels_last':
x_train = x_train.transpose(0, 2, 3, 1)
x_test = x_test.transpose(0, 2, 3, 1)
return (x_train, y_train), (x_test, y_test)
(x_train, y_train), (x_test, y_test)=load_data()
print('x_train shape:', x_train.shape)
print('y_train shape:', y_train.shape)
print('x_test shape:', x_test.shape)
print('y_test shape:', y_test.shape)
plt.figure(1)
plt.imshow(x_train[0]) # 显示第一张训练图片
plt.figure(2)
plt.imshow(x_test[0]) # 显示第一张测试图片
# 【3】将标签转化成 one-hot 编码
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# 【4】构建深度CNN序贯模型
model = Sequential()
'''
model.add(Conv2D(32, (3, 3), padding='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
'''
model.add(Conv2D(filters=16,kernel_size=(5,5),padding='same',input_shape=(32,32,3),activation='relu'));
model.add(MaxPooling2D(pool_size=(2,2)));
model.add(Conv2D(filters=36,kernel_size=(5,5),padding="same",activation='relu'));
model.add(MaxPooling2D(pool_size=(2,2)));
model.add(Dropout(0.25));
model.add(Flatten());
model.add(Dense(128,activation='relu'));
model.add(Dropout(0.5));
model.add(Dense(10,activation='softmax'));
#print(model.summary()) # 打印模型概况
# 【5】编译模型
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)#初始化一个 RMSprop 优化器
model.compile(loss="categorical_crossentropy",optimizer="adam",metrics=['accuracy']);
#model.compile(loss=my_loss, optimizer="adam", metrics=['accuracy'])
# 【6】数据预处理/增强+模型训练
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
if not data_augmentation:
print('Not using data augmentation.')
train_history=model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
verbose=2
)
else:
print('Using real-time data augmentation.')
# ImageDataGenerator:图片生成器，用以生成一个batch的图像数据，训练时该函数会无限生成数据
# 直到达到规定的epoch次数。图片生成(CPU)和训练（GPU）并行执行。
datagen = ImageDataGenerator(
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=0, # 随机旋转的角度范围
width_shift_range=0.1, # 随机水平偏移的幅度范围
height_shift_range=0.1,
horizontal_flip=True, # 随机水平翻转
vertical_flip=False)
datagen.fit(x_train) # 计算样本的统计信息，进行数据预处理（如去中心化，标准化）
train_history=model.fit_generator(datagen.flow(x_train, y_train, # datagen.flow()不断生成一个batch的数据用于模型训练
batch_size=batch_size),
epochs=epochs,
validation_data=(x_test, y_test),
workers=4,verbose=2,steps_per_epoch=1562)
# 【7】保存模型以及权重
from matplotlib import pyplot as plt
plt.plot(train_history.epoch,train_history.history['acc'],label='acc');
plt.plot(train_history.epoch,train_history.history['val_acc'],label='val_acc');
plt.legend();
plt.savefig("history_hw2.png");
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'keras_cifar10_trained_model.h5'
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
model_path = os.path.join(save_dir, model_name)
model.save(model_path)
print('Saved trained model at %s ' % model_path)
# 【8】测试集评估模型
scores = model.evaluate(x_test, y_test, verbose=2)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
标签：data,学习,add,train,深度,test,import,model,hw2 来源： https://blog.csdn.net/qq_24854861/article/details/94594289