我想尝试使用Keras Sequential模型来训练一个图像分类问题的预测 . 我的训练集是18K图像455x255，它可能太大而无法放入内存中，因此我想使用某种批处理管道 .
在我最初的tensorflow实现中，我有这个代码，类似于MNIST tensorflow example
如何将此管道提供给Sequential模型，以创建类似Keras cifa10_cnn example的内容
with tf.name_scope('input'):
# Input data
images_initializer = tf.placeholder(
dtype=tf.string,
shape=[len_all_filepaths])
labels_initializer = tf.placeholder(
dtype=tf.int32,
shape=[len_all_filepaths])
input_images = tf.Variable(
images_initializer, trainable=False, collections=[])
input_labels = tf.Variable(
labels_initializer, trainable=False, collections=[])
image, label = tf.train.slice_input_producer(
[input_images, input_labels], num_epochs=FLAGS.num_epochs)
# process path and string tensor into an image and a label
file_contents = tf.read_file(image)
image_contents = tf.image.decode_jpeg(file_contents, channels=NUM_CHANNELS)
image_contents.set_shape([None, None, NUM_CHANNELS])
# Rotate if necessary
rotated_image_contents, = tf.py_func(rotate, [image_contents], [tf.uint8])
rotated_image_contents.set_shape([IMAGE_HEIGHT, IMAGE_WIDTH, NUM_CHANNELS])
rotated_image_contents = tf.image.per_image_whitening(rotated_image_contents)
images, labels = tf.train.batch(
[rotated_image_contents, label],
batch_size=FLAGS.batch_size,
num_threads=16,
capacity=3 * FLAGS.batch_size
)
# Build a Graph that computes predictions from the inference model.
logits = model.inference(images, len(correct_labels))
# Add to the Graph the Ops for loss calculation.
loss = model.loss(logits, labels)
# Add to the Graph the Ops that calculate and apply gradients.
train_op = model.training(loss, FLAGS.learning_rate)
...
1 回答
我认为Keras的ImageDataGenerator已经为您进行了批处理 . 我不明白为什么具有指定批量大小的Keras datagen.fit（）和标准生成器不适用于您的用例 .