这篇文章主要介绍了keras训练浅层卷积网络并保存和加载模型实例，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
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("f
来源gaodai.ma#com搞##代!^码@网
loat") / 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, verbo
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