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如何实现keras训练浅层卷积网络并保存和加载模型

发布时间:2020-07-02 14:30:50 来源:亿速云 阅读:271 作者:清晨 栏目:开发技术

不懂如何实现keras训练浅层卷积网络并保存和加载模型?其实想解决这个问题也不难,下面让小编带着大家一起学习怎么去解决,希望大家阅读完这篇文章后大所收获。

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:

如何实现keras训练浅层卷积网络并保存和加载模型

使用sigmoid做激活函数:

如何实现keras训练浅层卷积网络并保存和加载模型

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。

如何实现keras训练浅层卷积网络并保存和加载模型

如何实现keras训练浅层卷积网络并保存和加载模型

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:

如何实现keras训练浅层卷积网络并保存和加载模型

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)

如何实现keras训练浅层卷积网络并保存和加载模型

感谢你能够认真阅读完这篇文章,希望小编分享如何实现keras训练浅层卷积网络并保存和加载模型内容对大家有帮助,同时也希望大家多多支持亿速云,关注亿速云行业资讯频道,遇到问题就找亿速云,详细的解决方法等着你来学习!

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