将Keras增强数据保存为numpy数组

本文介绍了将Keras增强数据保存为numpy数组的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

使用keras ImageDataGenerator ，我们可以将增强图像另存为png或jpg:

using keras ImageDataGenerator,we can save augmented images as png or jpg :

    for X_batch, y_batch in datagen.flow(train_data, train_labels, batch_size=batch_size,\
                save_to_dir='images', save_prefix='aug', save_format='png'):

我有一个形状为(1600，4，100,100)的数据集，这意味着1600个具有4个100x100像素通道的图像.如何将扩充后的数据另存为形状为(N，4,100,100)的numpy数组，而不是单个图像?

I have a dataset of the shape (1600, 4, 100,100), which means 1600 images with 4 channels of 100x100 pixels. How can I save the augmented data as numpy array of shape (N,4,100,100) instead of individual images?

推荐答案

由于您知道样本数量= 1600，因此只要达到该数量，就可以停止datagen.flow().

Since you know the number of samples = 1600, you can stop datagen.flow() as long as this number is reached.

augmented_data = []
num_augmented = 0
for X_batch, y_batch in datagen.flow(train_data, train_labels, batch_size=batch_size, shuffle=False):
    augmented_data.append(X_batch)
    num_augmented += batch_size
    if num_augmented == train_data.shape[0]:
        break
augmented_data = np.concatenate(augmented_data)
np.save(...)

请注意，您应正确设置batch_size(例如batch_size=10)，以免生成额外的增强图像.

Note that you should set batch_size properly (e.g. batch_size=10) so that no extra augmented images are generated.

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