我想将立体图像放入优化器中。这是我的代码:
tf.reset_default_graph()# configlearning_rate = 0.5training_epochs = 5# initinit = tf.global_variables_initializer()def conv2d(input_layer): conv1 = tf.layers.conv2d( inputs=input_layer, filters=32, kernel_size=[3, 3], padding='same', activation=tf.tanh, use_bias=False ) conv2 = tf.layers.conv2d( inputs=conv1, filters=32, kernel_size=[3, 3], padding='same', activation=tf.tanh, use_bias=False ) conv3 = tf.layers.conv2d( inputs=conv2, filters=32, kernel_size=[3, 3], padding='same', activation=tf.tanh, use_bias=False ) conv4 = tf.layers.conv2d( inputs=conv3, filters=32, kernel_size=[3, 3], padding='same', activation=tf.tanh, use_bias=False ) logits = tf.layers.conv2d( inputs=conv4, filters=32, kernel_size=[3, 3], padding='same', activation=tf.sigmoid, use_bias=False ) return logitsif __name__ == '__main__': # read images # preprocessing: rgb converted to float, zero_mean, uni_variance images = reading_images() mask_tensor = images["mask"][1] # reshape images img0 = images["img0"][1] img1 = images["img1"][1] img0_rs = tf.reshape(img0, [1, int(1988 / 2), int(2880 / 2), 3]) img1_rs = tf.reshape(img1, [1, int(1988 / 2), int(2880 / 2), 3]) # define symbolic placeholders t_im0 = tf.placeholder(tf.float32, [1, None, None, 3]) t_im1 = tf.placeholder(tf.float32, [1, None, None, 3]) t_img = tf.concat([t_im0, t_im1], axis=3) input_layer = tf.reshape(t_img, [1, int(1988 / 2), int(2880 / 2), 6]) logits = conv2d(input_layer) with tf.name_scope("cost_function") as scope: mask_tensor = tf.tile(mask_tensor, [1, 1, 3]) cost_function = -tf.reduce_mean(mask_tensor * tf.log(logits) + (1. - mask_tensor) * tf.log(1. - logits)) tf.summary.scalar("cost_function", cost_function) with tf.name_scope("train") as scope: optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function) merged_summary_op = tf.summary.merge_all() with tf.Session() as sess: coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) sess.run(init) # summary_writer = tf.summary.FileWriter('/tmp/tensorflow_logs', graph=sess.graph) for epoch in range(training_epochs): print("epoch ", epoch) avg_cost = 0.0 mask = sess.run(mask_tensor) np_img0_rs = sess.run(img0_rs) np_img1_rs = sess.run(img1_rs) # res = t_img.eval(feed_dict={t_im0: img0_rs_, t_im1: img1_rs_}) sess.run([optimizer], feed_dict={t_im0: np_img0_rs, t_im1: np_img1_rs}) coord.request_stop() coord.join(threads)但我总是得到这个错误。我不知道是什么原因导致的，需要更改什么。我可以尝试什么方法来调试它？我真的尝试了很多方法来修复这个错误。
epoch 02017-07-17 10:26:03.719539: W tensorflow/core/kernels/queue_base.cc:294] _4_input_producer: Skipping cancelled enqueue attempt with queue not closed2017-07-17 10:26:03.719610: W tensorflow/core/kernels/queue_base.cc:294] _5_input_producer_1: Skipping cancelled enqueue attempt with queue not closedTraceback (most recent call last): File "/home/test/Dropbox/occlusion_thesis/occ_small/main.py", line 111, in <module> sess.run([optimizer], feed_dict={t_im0: np_img0_rs, t_im1: np_img1_rs}) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 789, in run run_metadata_ptr) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 997, in _run feed_dict_string, options, run_metadata) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1132, in _do_run target_list, options, run_metadata) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1152, in _do_call raise type(e)(node_def, op, message)tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value conv2d_4/kernel [[Node: conv2d_4/kernel/read = Identity[T=DT_FLOAT, _class=["loc:@conv2d_4/kernel"], _device="/job:localhost/replica:0/task:0/cpu:0"](conv2d_4/kernel)]]Caused by op u'conv2d_4/kernel/read', defined at: File "/home/test/Dropbox/occlusion_thesis/occ_small/main.py", line 84, in <module> logits = conv2d(input_layer) File "/home/test/Dropbox/occlusion_thesis/occ_small/main.py", line 60, in conv2d use_bias=False File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/layers/convolutional.py", line 551, in conv2d return layer.apply(inputs) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 492, in apply return self.__call__(inputs, *args, **kwargs) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 434, in __call__ self.build(input_shapes[0]) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/layers/convolutional.py", line 137, in build dtype=self.dtype) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 374, in add_variable trainable=trainable and self.trainable) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 1065, in get_variable use_resource=use_resource, custom_getter=custom_getter) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 962, in get_variable use_resource=use_resource, custom_getter=custom_getter) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 367, in get_variable validate_shape=validate_shape, use_resource=use_resource) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 352, in _true_getter use_resource=use_resource) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 725, in _get_single_variable validate_shape=validate_shape) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 200, in __init__ expected_shape=expected_shape) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 319, in _init_from_args self._snapshot = array_ops.identity(self._variable, name="read") File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 1303, in identity result = _op_def_lib.apply_op("Identity", input=input, name=name) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op op_def=op_def) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2506, in create_op original_op=self._default_original_op, op_def=op_def) File "/home/test/Programs/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1269, in __init__ self._traceback = _extract_stack()FailedPreconditionError (see above for traceback): Attempting to use uninitialized value conv2d_4/kernel [[Node: conv2d_4/kernel/read = Identity[T=DT_FLOAT, _class=["loc:@conv2d_4/kernel"], _device="/job:localhost/replica:0/task:0/cpu:0"](conv2d_4/kernel)]]回答：
我不确定你的代码是否完整，但错误信息对我来说似乎相当清楚:
FailedPreconditionError: 尝试使用未初始化的值 conv2d_4/kernel
查看你的代码，我看到你有 sess.run(init)，但我找不到任何地方定义了 init。尝试在 with tf.Session() as sess: 之前添加 init = tf.global_variables_initializer()，这应该可以修复“未初始化的值”错误。
编辑: 有了完整的代码，我看到问题出自:
# initinit = tf.global_variables_initializer() # <<<<<<<<< 1def conv2d(input_layer): ## 一大堆定义层的代码 return logitsif __name__ == '__main__': ## 一大堆其他代码 logits = conv2d(input_layer) # <<<<<<<<< 2我标记为 1 的地方是你定义初始化函数的地方，对于到那时为止定义的所有变量，而标记为 2 的地方是你实际定义网络的地方（以及其中的所有变量）。init 的定义必须在所有变量定义完成之后，否则你将会有未初始化的变量。
更新:
我在这里复制了我对答案的评论，因为这里可能是放置它的更好位置。tf.global_variables_initializer() 必须在你的图定义完成之后调用。如果你在开始时定义它，然后再向网络中添加层，添加的层的权重将不会被初始化，因为在你创建初始化操作时它们尚未定义。始终将 init 定义为在 with tf.Session() ... 之前的最后一个操作，以确保初始化时不会遗漏任何东西。
