我正在尝试使用管道将图像读取到CNN。我使用string_input_producer()
来获取文件名的队列,但似乎没有做任何事情就挂在那里。以下是我的代码,请给我一些如何使其工作的建议。
def read_image_file(filename_queue, labels):
reader = tf.WholeFileReader()
key, value = reader.read(filename_queue)
image = tf.image.decode_png(value, channels=3)
image = tf.cast(image, tf.float32)
resized_image = tf.image.resize_images(image, [224, 112])
with tf.Session() as sess:
label = getLabel(labels, key.eval())
return resized_image, label
def input_pipeline(filename_queue, queue_names, batch_size, num_epochs, labels):
image, label = read_image_file(filename_queue, labels)
min_after_dequeue = 10 * batch_size
capacity = 20 * batch_size
image_batch, label_batch = tf.train.shuffle_batch(
[image, label], batch_size=batch_size, num_threads=1, capacity=capacity,
min_after_dequeue=min_after_dequeue)
return image_batch, label_batch
train_queue = tf.train.string_input_producer(trainnames, shuffle=True, num_epochs=epochs)
train_batch, train_label = input_pipeline(train_queue, trainnames, batch_size, epochs, labels)
prediction = AlexNet(x)
#Training
with tf.name_scope("cost_function") as scope:
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=train_label, logits=prediction(train_batch)))
tf.summary.scalar("cost_function", cost)
train_step = tf.train.MomentumOptimizer(learning_rate, 0.9).minimize(cost)
#Accuracy
with tf.name_scope("accuracy") as scope:
correct_prediction = tf.equal(tf.argmax(prediction,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar("accuracy", accuracy)
merged = tf.summary.merge_all()
#Session
with tf.Session() as sess:
print('started')
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord, start=True)
sess.run(threads)
try:
for step in range(steps_per_epch * epochs):
print('step: %d' %step)
sess.run(train_step)
except tf.errors.OutOfRangeError as ex:
pass
coord.request_stop()
coord.join(threads)
答案 0 :(得分:0)
由于未定义get_label
方法,您的代码并非完全自包含。
但很可能你所遇到的问题来自read_image_file
方法中的这些行:
with tf.Session() as sess:
label = getLabel(labels, key.eval())
key.eval
部分尝试将尚未启动的队列的元素出列。
在定义输入管道之前不应创建任何会话(也不要尝试评估key
(可能还有labels
))。 get_label
方法只应对labels
和key
执行张量操作并返回label
张量..
例如,您可以使用这些tensor
string operations,这样它们就会成为图表的一部分。