基于Keras搭建cifar10数据集训练预测Pipeline

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0.5412019.01.17 22:52:05字数 227阅读 500

Pipeline

本次训练模型的数据直接使用Keras.datasets.cifar10.load_data()得到，模型建立是通过Sequential搭建。

重点思考的内容是如何应用训练过的模型进行实际预测，里面牵涉到一些细节，需要注意。同时，Keras提供的ImageDataGenerator为模型训练时提供数据输入，之前有总结过这个类，并给出了从文件系统中加载原始图片数据的方法。

模型搭建

from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
import os# 指定超参数
batch_size = 32
num_classes = 10
epochs = 50
data_augmentation = True # 数据增强
num_predictions = 20
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'keras_cifar10_trained_model.h5'# The data, split between train and test sets:
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)# 搭建模型
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'))# initiate RMSprop optimizer
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)# Let's train the model using RMSprop
model.compile(loss='categorical_crossentropy',optimizer=opt,metrics=['accuracy'])x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255# 如果不用模型增强
if not data_augmentation:print('Not using data augmentation.')model.fit(x_train, y_train,batch_size=batch_size,epochs=epochs,validation_data=(x_test, y_test),shuffle=True)# 使用模型增强
else:print('Using real-time data augmentation.')# This will do preprocessing and realtime data augmentation:datagen = ImageDataGenerator(featurewise_center=False,  # set input mean to 0 over the datasetsamplewise_center=False,  # set each sample mean to 0featurewise_std_normalization=False,  # divide inputs by std of the datasetsamplewise_std_normalization=False,  # divide each input by its stdzca_whitening=False,  # apply ZCA whiteningzca_epsilon=1e-06,  # epsilon for ZCA whiteningrotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)# randomly shift images horizontally (fraction of total width)width_shift_range=0.1,# randomly shift images vertically (fraction of total height)height_shift_range=0.1,shear_range=0.,  # set range for random shearzoom_range=0.,  # set range for random zoomchannel_shift_range=0.,  # set range for random channel shifts# set mode for filling points outside the input boundariesfill_mode='nearest',cval=0.,  # value used for fill_mode = "constant"horizontal_flip=True,  # randomly flip imagesvertical_flip=False,  # randomly flip images# set rescaling factor (applied before any other transformation)rescale=None,# set function that will be applied on each inputpreprocessing_function=None,# image data format, either "channels_first" or "channels_last"data_format=None,# fraction of images reserved for validation (strictly between 0 and 1)validation_split=0.0)# Compute quantities required for feature-wise normalization# (std, mean, and principal components if ZCA whitening is applied).datagen.fit(x_train)# Fit the model on the batches generated by datagen.flow().history = model.fit_generator(datagen.flow(x_train, y_train,batch_size=batch_size),epochs=epochs,steps_per_epoch = 600,validation_data=(x_test, y_test),validation_steps = 10,workers=4)# Save model and weights
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)# Score trained model.
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])

训练完毕后，模型保存为：keras_cifar10_trained_model.h5

使用预训练模型

# 使用已经训练好的参数来加载模型from keras.models import load_modelmodel = load_model('./saved_models/keras_cifar10_trained_model.h5')model.summary()'''
Layer (type)                 Output Shape              Param #
=================================================================
conv2d_9 (Conv2D)            (None, 32, 32, 32)        896
_________________________________________________________________
activation_13 (Activation)   (None, 32, 32, 32)        0
_________________________________________________________________
conv2d_10 (Conv2D)           (None, 30, 30, 32)        9248
_________________________________________________________________
activation_14 (Activation)   (None, 30, 30, 32)        0
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 15, 15, 32)        0
_________________________________________________________________
dropout_7 (Dropout)          (None, 15, 15, 32)        0
_________________________________________________________________
conv2d_11 (Conv2D)           (None, 15, 15, 64)        18496
_________________________________________________________________
activation_15 (Activation)   (None, 15, 15, 64)        0
_________________________________________________________________
conv2d_12 (Conv2D)           (None, 13, 13, 64)        36928
_________________________________________________________________
activation_16 (Activation)   (None, 13, 13, 64)        0
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 6, 6, 64)          0
_________________________________________________________________
dropout_8 (Dropout)          (None, 6, 6, 64)          0
_________________________________________________________________
flatten_3 (Flatten)          (None, 2304)              0
_________________________________________________________________
dense_5 (Dense)              (None, 512)               1180160
_________________________________________________________________
activation_17 (Activation)   (None, 512)               0
_________________________________________________________________
dropout_9 (Dropout)          (None, 512)               0
_________________________________________________________________
dense_6 (Dense)              (None, 10)                5130
_________________________________________________________________
activation_18 (Activation)   (None, 10)                0
=================================================================
Total params: 1,250,858
Trainable params: 1,250,858
Non-trainable params: 0
'''

识别测试集图片

lst= ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
def onehot_to_label(res):label = ''for i in range(len(res[0])):if res[0][i] == 1:label = lst[i]return labeldef softmax_to_label(res):label = ''index = res[0].argmax()label = lst[index]return label# 识别测试集图片
test_image = x_test[100].reshape([1,32,32,3])
test_image.shape
res = model.predict(test_image)
label = softmax_to_label(res)
print(label)

本地加载图片识别

# 自己加载raw image进行识别
from PIL import Image
from keras.preprocessing.image import img_to_array
import numpy as npimage = Image.open('./images/airplane.jpeg') # 加载图片
image = image.resize((32,32))
image = img_to_array(image)# 加载进来之后开始预测
image = image.reshape([1,32,32,3]) # 需要reshape到四维张量才行
res = model.predict(image)
label = softmax_to_label(res)
print("The image is: ", label)# 或者整合为一个函数
def image_to_array(path):image = Image.open(path)image = image.resize((32,32),Image.NEAREST) # 会将图像整体缩放到指定大小，不是裁剪image = img_to_array(image) # 变成数组image = image.reshape([1,32,32,3]) # reshape到4维张量return image

使用时注意到输入到网络的数据是张量，且需要reshape到四维，因为按照批量往里输入的时候，也是四维，单独输入一张图片，使用方式相同。

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