Question

我跟随this tutorial创建了以下数据生成器。但是，培训花费了太多时间。知道已经创建了reader对象读取的所有数据文件，如何使其运行更快？

ps ：方法__data_generation每次执行两次磁盘访问。

import numpy as np
import keras

class DataGenerator(keras.utils.Sequence):
    """
    Generates data for Keras
    :return: data generator object
    """
    def __init__(self, reader, list_IDs, labels, relations_list, batch_size=32, shuffle=True):
        # Initialization
        self.reader = reader
        self.batch_size = batch_size
        self.labels = labels
        self.list_IDs = list_IDs
        self.shuffle = shuffle
        self.on_epoch_end()
        self.relations = relations_list
        self.data_num = 0

    def __len__(self):
        """
        Denotes the number of batches per epoch
        :return: int
        """
        return int(np.floor(len(self.list_IDs) / self.batch_size))

    def __getitem__(self, index):
        """
        Generate one batch of data
        :param index: index of the current training item
        :return: tuple
        """
        # Generate indexes of the batch
        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]

        # Find list of IDs
        list_IDs_temp = [self.list_IDs[k] for k in indexes]

        # Generate data
        X, y = self.__data_generation(list_IDs_temp)

        return X, y

    def on_epoch_end(self):
        """
        Updates indexes after each epoch
        :return:
        """
        self.indexes = np.arange(len(self.list_IDs))
        if self.shuffle:
            np.random.shuffle(self.indexes)

    def __data_generation(self, list_IDs_temp):
        """
        Generates data containing batch_size samples'
        :param list_IDs_temp: the list of IDs of the target batch
        :return: tuple
        """
        # Initialization
        y = []
        v_q_words = []
        v_d_words = []

        # Generate data
        for i, ID in enumerate(list_IDs_temp):
            # Store sample
            q_words = self.reader.get_query(self.relations[ID][0])  # corresponds to 1 file read from disc
            v_q_words.append(q_words)
            d_words = self.reader.get_document(self.relations[ID][1])  # corresponds to another file read from disc
            v_d_words.append(d_words)
            # Store class
            y.append(self.labels[ID])

        X = [np.array(v_q_words), np.array(v_d_words)]

        return X, np.array(y)

预先感谢您的回答。

Answer 1

您应该在GPU上并行处理数据读取和算法。由于tensorflow以其在GPU卡上的速度而闻名，因此最好使用tensorflow中包含的keras模块。

Answer 2

来自your link

由于我们的代码是多核友好的，因此请注意，您可以执行更复杂的操作（例如，从源文件进行计算），而不必担心数据生成会成为训练过程中的瓶颈。

根据@nabiltos的建议，加快培训速度的最有效方法是使用Keras backend的GPU版本，这意味着在其上安装了兼容的GPU设备您的机器。

安装后，运行此代码应列出您的工作站GPU

>>> from keras import backend as K
>>> K.tensorflow_backend._get_available_gpus()


name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:0b:00.0
totalMemory: 10.92GiB freeMemory: 10.32GiB
2018-07-17 14:09:36.190143: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1212] Found device 1 with properties: 
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:13:00.0
totalMemory: 10.92GiB freeMemory: 10.54GiB
2018-07-17 14:09:36.395138: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1212] Found device 2 with properties: 
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:1b:00.0
totalMemory: 11.91GiB freeMemory: 11.54GiB
2018-07-17 14:09:36.395451: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1312] Adding visible gpu devices: 0, 1, 2
2018-07-17 14:09:37.394013: I tensorflow/core/common_runtime/gpu/gpu_device.cc:993] Creating TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9990 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:0b:00.0, compute capability: 6.1)
2018-07-17 14:09:37.563166: I tensorflow/core/common_runtime/gpu/gpu_device.cc:993] Creating TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 10203 MB memory) -> physical GPU (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:13:00.0, compute capability: 6.1)
2018-07-17 14:09:37.735253: I tensorflow/core/common_runtime/gpu/gpu_device.cc:993] Creating TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:2 with 11170 MB memory) -> physical GPU (device: 2, name: TITAN X (Pascal), pci bus id: 0000:1b:00.0, compute capability: 6.1)
['/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1', '/job:localhost/replica:0/task:0/device:GPU:2']

您可以在这里看到我的机器上有3个GPU设备（2个GeForce GTX 1080 Ti和1个TITAN X（帕斯卡））。如果TensorFlow操作同时具有CPU和GPU实施，则GPU设备将被赋予优先级（read more）

在训练keras模型时如何使用数据生成器更快地生成数据？

2 个答案: