无法将float32序列化为JSON

Question

我有一个trainingmonitor.py，它正在检查keras模型的进度并将其保存到文件中。它抛出一个错误消息

     TypeError: Object of type 'float32' is not JSON serializable

在本部分的代码中

if self.jsonPath is not None:
            f = open(self.jsonPath, "w")
            f.write(json.dumps(self.H))
            f.close()

有没有办法让它发挥作用？你知道吗

这是我的完整代码，我在主model文件中作为

model file

# construct the set of callbacks

figPath = os.path.sep.join([args["output"], "{}.png".format(

    os.getpid())])

jsonPath = os.path.sep.join([args["output"], "{}.json".format(

    os.getpid())])

callbacks = [TrainingMonitor(figPath, jsonPath=jsonPath),

    LearningRateScheduler(poly_decay)]

trainmonitor.py

class TrainingMonitor(BaseLogger):
    def __init__(self, figPath, jsonPath=None, startAt=0):
        # store the output path for the figure, the path to the JSON
        # serialized file, and the starting epoch
        super(TrainingMonitor, self).__init__()
        self.figPath = figPath
        self.jsonPath = jsonPath
        self.startAt = startAt

    def on_train_begin(self, logs={}):
        # initialize the history dictionary
        self.H = {}

        # if the JSON history path exists, load the training history
        if self.jsonPath is not None:
            if os.path.exists(self.jsonPath):
                self.H = json.loads(open(self.jsonPath).read())

                # check to see if a starting epoch was supplied
                if self.startAt > 0:
                    # loop over the entries in the history log and
                    # trim any entries that are past the starting
                    # epoch
                    for k in self.H.keys():
                        self.H[k] = self.H[k][:self.startAt]

    def on_epoch_end(self, epoch, logs={}):
        # loop over the logs and update the loss, accuracy, etc.
        # for the entire training process
        for (k, v) in logs.items():
            l = self.H.get(k, [])
            l.append(v)
            self.H[k] = l

        # check to see if the training history should be serialized
        # to file
        if self.jsonPath is not None:
            f = open(self.jsonPath, "w")
            f.write(json.dumps(self.H))
            f.close()

        # ensure at least two epochs have passed before plotting
        # (epoch starts at zero)
        if len(self.H["loss"]) > 1:
            # plot the training loss and accuracy
            N = np.arange(0, len(self.H["loss"]))
            plt.style.use("ggplot")
            plt.figure()
            plt.plot(N, self.H["loss"], label="train_loss")
            plt.plot(N, self.H["val_loss"], label="val_loss")
            plt.plot(N, self.H["acc"], label="train_acc")
            plt.plot(N, self.H["val_acc"], label="val_acc")
            plt.title("Training Loss and Accuracy [Epoch {}]".format(
                len(self.H["loss"])))
            plt.xlabel("Epoch #")
            plt.ylabel("Loss/Accuracy")
            plt.legend()

            # save the figure
            plt.savefig(self.figPath)
            plt.close()