Keras fit_generator 问题

Question

我关注 this tutorial 为我的 Keras 模型创建了一个自定义生成器。这是一个 MWE，显示了我面临的问题：

import sys, keras
import numpy as np
import tensorflow as tf
import pandas as pd
from keras.models import Model
from keras.layers import Dense, Input
from keras.optimizers import Adam
from keras.losses import binary_crossentropy

class DataGenerator(keras.utils.Sequence):
    'Generates data for Keras'
    def __init__(self, list_IDs, batch_size, shuffle=False):
        'Initialization'
        self.batch_size = batch_size
        self.list_IDs = list_IDs
        self.shuffle = shuffle
        self.on_epoch_end()

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

    def __getitem__(self, index):
        'Generate one batch of data'
        # Generate indexes of the batch
        #print('self.batch_size: ', self.batch_size)
        print('index: ', index)
        sys.exit()

    def on_epoch_end(self):
        'Updates indexes after each epoch'
        self.indexes = np.arange(len(self.list_IDs))
        print('self.indexes: ', self.indexes)
        if self.shuffle == True:
            np.random.shuffle(self.indexes)

    def __data_generation(self, list_IDs_temp):
        'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)

        X1 = np.empty((self.batch_size, 10), dtype=float)
        X2 = np.empty((self.batch_size, 12),  dtype=int)

        #Generate data
        for i, ID in enumerate(list_IDs_temp):
            print('i is: ', i, 'ID is: ', ID)

            #Preprocess this sample (omitted)
            X1[i,] = np.repeat(1, X1.shape[1])
            X2[i,] = np.repeat(2, X2.shape[1])

        Y = X1[:,:-1]
        return X1, X2, Y

if __name__=='__main__':
    train_ids_to_use = list(np.arange(1, 321)) #1, 2, ...,320 
    valid_ids_to_use = list(np.arange(321, 481)) #321, 322, ..., 480

    params = {'batch_size': 32}

    train_generator = DataGenerator(train_ids_to_use, **params)
    valid_generator = DataGenerator(valid_ids_to_use, **params)

    #Build a toy model
    input_1 = Input(shape=(3, 10))
    input_2 = Input(shape=(3, 12))
    y_input = Input(shape=(3, 10))

    concat_1 = keras.layers.concatenate([input_1, input_2])
    concat_2 = keras.layers.concatenate([concat_1, y_input])

    dense_1 = Dense(10, activation='relu')(concat_2)
    output_1 = Dense(10, activation='sigmoid')(dense_1)

    model = Model([input_1, input_2, y_input], output_1)
    print(model.summary())

    #Compile and fit_generator
    model.compile(optimizer=Adam(lr=0.001), loss=binary_crossentropy)
    model.fit_generator(generator=train_generator, validation_data = valid_generator, epochs=2, verbose=2)

我不想打乱我的输入数据。我认为这已经得到处理，但是在我的代码中，当我在__get_item__ 中打印出index 时，我得到了随机数。我想要连续的数字。请注意，我正在尝试在 __getitem__ 中使用 sys.exit 来终止进程，以查看发生了什么。

我的问题：

1. 为什么index 不连续？我该如何解决这个问题？

2. 当我在终端使用屏幕运行时，为什么它没有响应 Ctrl+C？

Answer 1

您可以使用fit_generator 方法的shuffle 参数连续生成批次。来自fit_generator()documentation：

随机播放：布尔值。是否在每个 epoch 开始时打乱批次的顺序。仅用于 Sequence (keras.utils.Sequence) 的实例。当steps_per_epoch 不是None 时无效。

只需将shuffle=False 传递给fit_generator：

model.fit_generator(generator=train_generator, shuffle=False, ...)