使用 tfrecord 输入通过自定义 CNN 创建预测

Question

我的目标是将图像分为十类。我有一个 tfrecord 文件作为输入。您可以下载它 here (30 MB)。我根据答案修改了代码：

import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np

def my_cnn(images, num_classes, is_training):  # is_training is not used...
    with slim.arg_scope([slim.max_pool2d], kernel_size=[3, 3], stride=2):
        net = slim.conv2d(images, 64, [5, 5])
        net = slim.max_pool2d(net)
        net = slim.conv2d(net, 64, [5, 5])
        net = slim.max_pool2d(net)
        net = slim.flatten(net)
        net = slim.fully_connected(net, 192)
        net = slim.fully_connected(net, num_classes, activation_fn=None)       
        return net

data_path = 'train-some.tfrecords' 

with tf.Graph().as_default():
    batch_size, height, width, channels = 10, 224, 224, 3  
    feature = {'train/image': tf.FixedLenFeature([], tf.string),
               'train/label': tf.FixedLenFeature([], tf.int64)}
    filename_queue = tf.train.string_input_producer([data_path], num_epochs=1)
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(serialized_example, features=feature)
    image = tf.decode_raw(features['train/image'], tf.float32)
    label = tf.cast(features['train/label'], tf.int32)
    image = tf.reshape(image, [224, 224, 3])
    images, labels = tf.train.shuffle_batch([image, label], batch_size, capacity=30, num_threads=1, min_after_dequeue=10)

    num_classes = 10
    logits = my_cnn(images, num_classes, is_training=True)
    probabilities = tf.nn.softmax(logits)


with tf.Session() as sess:
    init_op = [tf.global_variables_initializer(), tf.local_variables_initializer()]
    # Run the init_op, evaluate the model outputs and print the results:
    sess.run(init_op)
    #probabilities = sess.run(probabilities)

    # Create a coordinator, launch the queue runner threads.
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    try:
        while not coord.should_stop():
            while True:
                prob = sess.run(probabilities)
                print('Probabilities Shape:')
                print(prob.shape) 

    except tf.errors.OutOfRangeError:
        # When done, ask the threads to stop.
        print('Done training -- epoch limit reached')
    finally:
        coord.request_stop()
        # Wait for threads to finish.
    coord.join(threads)

    # Save the model
    saver = tf.train.Saver()
    saver.save(sess, './slim_model/custom_model')

不幸的是，我仍然收到错误消息：

ValueError: Tensor Tensor("Softmax:0", shape=(10, 10), dtype=float32) 不是该图的元素。

ValueError：无法将获取参数解释为张量。（张量 Tensor("Softmax:0", shape=(10, 10), dtype=float32) 不是这个图的元素。）

Answer 1

问题出在你的训练上。您需要使用 tf.train.start_queue_runners 启动队列，这将运行一些线程来处理和排队示例。创建一个 Coordinator 并要求队列运行ner 启动其与协调器的线程。

检查代码更改：

with tf.Session() as sess:
    init_op = [tf.global_variables_initializer(), tf.local_variables_initializer()]
    # Run the init_op, evaluate the model outputs and print the results:
    sess.run(init_op)
    #probabilities = sess.run(probabilities)

    # Create a coordinator, launch the queue runner threads.
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    try:
        while not coord.should_stop():
            while True:
                prob = sess.run(probabilities)
                print('Probabilities Shape:')
                print(prob.shape) 

    except tf.errors.OutOfRangeError:
        # When done, ask the threads to stop.
        print('Done training -- epoch limit reached')
    finally:
        coord.request_stop()
        # Wait for threads to finish.
    coord.join(threads)

    # Save the model
    saver = tf.train.Saver()
    saver.save(sess, './slim_model/custom_model'

输出：

 Probabilities Shape:
 (10, 10)
 Probabilities Shape:
 (10, 10)
 Probabilities Shape:
 (10, 10)
 Probabilities Shape:
 (10, 10)
 Probabilities Shape:
 (10, 10)
Done training -- epoch limit reached

可以从 here 下载具有上述修复以及保存和恢复模型的代码。

使用 tfrecord 输入通过自定义 CNN 创建预测

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