keras训练浅层卷积网络并保存和加载模型实例

这里我们使用keras定义简单的神经网络全连接层训练MNIST数据集和cifar10数据集：

keras_mnist.py

from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from keras.models import Sequential
from keras.layers.core import Dense
from keras.optimizers import SGD
from sklearn import datasets
import matplotlib.pyplot as plt
import numpy as np
import argparse
# 命令行参数运行
ap = argparse.ArgumentParser()
ap.add_argument("-o", "--output", required=True, help="path to the output loss/accuracy plot")
args =vars(ap.parse_args())
# 加载数据MNIST，然后归一化到【0,1】，同时使用75%做训练，25%做测试
print("[INFO] loading MNIST (full) dataset")
dataset = datasets.fetch_mldata("MNIST Original", data_home="/home/king/test/python/train/pyimagesearch/nn/data/")
data = dataset.data.astype("float") / 255.0
(trainX, testX, trainY, testY) = train_test_split(data, dataset.target, test_size=0.25)
# 将label进行one-hot编码
lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.transform(testY)
# keras定义网络结构784--256--128--10
model = Sequential()
model.add(Dense(256, input_shape=(784,), activation="relu"))
model.add(Dense(128, activation="relu"))
model.add(Dense(10, activation="softmax"))
# 开始训练
print("[INFO] training network...")
# 0.01的学习率
sgd = SGD(0.01)
# 交叉验证
model.compile(loss="categorical_crossentropy", optimizer=sgd, metrics=['accuracy'])
H = model.fit(trainX, trainY, validation_data=(testX, testY), epochs=100, batch_size=128)
# 测试模型和评估
print("[INFO] evaluating network...")
predictions = model.predict(testX, batch_size=128)
print(classification_report(testY.argmax(axis=1), predictions.argmax(axis=1),
	target_names=[str(x) for x in lb.classes_]))
# 保存可视化训练结果
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, 100), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, 100), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, 100), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, 100), H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy")
plt.xlabel("# Epoch")
plt.ylabel("Loss/Accuracy")
plt.legend()
plt.savefig(args["output"])

使用relu做激活函数：

使用sigmoid做激活函数：

接着我们自己定义一些modules去实现一个简单的卷基层去训练cifar10数据集：

imagetoarraypreprocessor.py

'''
该函数主要是实现keras的一个细节转换，因为训练的图像时RGB三颜色通道，读取进来的数据是有depth的，keras为了兼容一些后台，默认是按照（height， width， depth）读取，但有时候就要改变成（depth， height， width）
'''
from keras.preprocessing.image import img_to_array
class ImageToArrayPreprocessor:
	def __init__(self, dataFormat=None):
		self.dataFormat = dataFormat

	def preprocess(self, image):
		return img_to_array(image, data_format=self.dataFormat)
 

shallownet.py

'''
定义一个简单的卷基层：
input->conv->Relu->FC
'''
from keras.models import Sequential
from keras.layers.convolutional import Conv2D
from keras.layers.core import Activation, Flatten, Dense
from keras import backend as K

class ShallowNet:
	@staticmethod
	def build(width, height, depth, classes):
		model = Sequential()
		inputShape = (height, width, depth)

		if K.image_data_format() == "channels_first":
			inputShape = (depth, height, width)

		model.add(Conv2D(32, (3, 3), padding="same", input_shape=inputShape))
		model.add(Activation("relu"))

		model.add(Flatten())
		model.add(Dense(classes))
		model.add(Activation("softmax"))

		return model

然后就是训练代码：

keras_cifar10.py

from sklearn.preprocessing import LabelBinarizer
from sklearn.metrics import classification_report
from shallownet import ShallowNet
from keras.optimizers import SGD
from keras.datasets import cifar10
import matplotlib.pyplot as plt
import numpy as np
import argparse

ap = argparse.ArgumentParser()
ap.add_argument("-o", "--output", required=True, help="path to the output loss/accuracy plot")
args = vars(ap.parse_args())

print("[INFO] loading CIFAR-10 dataset")
((trainX, trainY), (testX, testY)) = cifar10.load_data()
trainX = trainX.astype("float") / 255.0
testX = testX.astype("float") / 255.0

lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.transform(testY)
# 标签0-9代表的类别string
labelNames = ['airplane', 'automobile', 'bird', 'cat',
	'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

print("[INFO] compiling model...")
opt = SGD(lr=0.0001)
model = ShallowNet.build(width=32, height=32, depth=3, classes=10)
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])

print("[INFO] training network...")
H = model.fit(trainX, trainY, validation_data=(testX, testY), batch_size=32, epochs=1000, verbose=1)

print("[INFO] evaluating network...")
predictions = model.predict(testX, batch_size=32)
print(classification_report(testY.argmax(axis=1), predictions.argmax(axis=1),
	target_names=labelNames))

# 保存可视化训练结果
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, 1000), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, 1000), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, 1000), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, 1000), H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy")
plt.xlabel("# Epoch")
plt.ylabel("Loss/Accuracy")
plt.legend()
plt.savefig(args["output"])
 

代码中可以对训练的learning rate进行微调，大概可以接近60%的准确率。

然后修改下代码可以保存训练模型：

from sklearn.preprocessing import LabelBinarizer
from sklearn.metrics import classification_report
from shallownet import ShallowNet
from keras.optimizers import SGD
from keras.datasets import cifar10
import matplotlib.pyplot as plt
import numpy as np
import argparse

ap = argparse.ArgumentParser()
ap.add_argument("-o", "--output", required=True, help="path to the output loss/accuracy plot")
ap.add_argument("-m", "--model", required=True, help="path to save train model")
args = vars(ap.parse_args())

print("[INFO] loading CIFAR-10 dataset")
((trainX, trainY), (testX, testY)) = cifar10.load_data()
trainX = trainX.astype("float") / 255.0
testX = testX.astype("float") / 255.0

lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.transform(testY)
# 标签0-9代表的类别string
labelNames = ['airplane', 'automobile', 'bird', 'cat',
	'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

print("[INFO] compiling model...")
opt = SGD(lr=0.005)
model = ShallowNet.build(width=32, height=32, depth=3, classes=10)
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])

print("[INFO] training network...")
H = model.fit(trainX, trainY, validation_data=(testX, testY), batch_size=32, epochs=50, verbose=1)

model.save(args["model"])

print("[INFO] evaluating network...")
predictions = model.predict(testX, batch_size=32)
print(classification_report(testY.argmax(axis=1), predictions.argmax(axis=1),
	target_names=labelNames))

# 保存可视化训练结果
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, 5), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, 5), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, 5), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, 5), H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy")
plt.xlabel("# Epoch")
plt.ylabel("Loss/Accuracy")
plt.legend()
plt.savefig(args["output"])
 

命令行运行：

我们使用另一个程序来加载上一次训练保存的模型，然后进行测试：

test.py

from sklearn.preprocessing import LabelBinarizer
from sklearn.metrics import classification_report
from shallownet import ShallowNet
from keras.optimizers import SGD
from keras.datasets import cifar10
from keras.models import load_model
import matplotlib.pyplot as plt
import numpy as np
import argparse

ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", required=True, help="path to save train model")
args = vars(ap.parse_args())

# 标签0-9代表的类别string
labelNames = ['airplane', 'automobile', 'bird', 'cat',
	'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

print("[INFO] loading CIFAR-10 dataset")
((trainX, trainY), (testX, testY)) = cifar10.load_data()

idxs = np.random.randint(0, len(testX), size=(10,))
testX = testX[idxs]
testY = testY[idxs]

trainX = trainX.astype("float") / 255.0
testX = testX.astype("float") / 255.0

lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.transform(testY)

print("[INFO] loading pre-trained network...")
model = load_model(args["model"])

print("[INFO] evaluating network...")
predictions = model.predict(testX, batch_size=32).argmax(axis=1)
print("predictions\n", predictions)
for i in range(len(testY)):
	print("label:{}".format(labelNames[predictions[i]]))

trueLabel = []
for i in range(len(testY)):
	for j in range(len(testY[i])):
		if testY[i][j] != 0:
			trueLabel.append(j)
print(trueLabel)

print("ground truth testY:")
for i in range(len(trueLabel)):
	print("label:{}".format(labelNames[trueLabel[i]]))

print("TestY\n", testY)

以上这篇keras训练浅层卷积网络并保存和加载模型实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持我们。