当我尝试运行这段代码时，出现了上述意外的错误：
# -*- coding: utf-8 -*-"""Created on Fri Jun 24 10:38:04 2016@author: andrea"""# pylint: disable=missing-docstringfrom __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport timefrom six.moves import xrange # pylint: disable=redefined-builtinimport tensorflow as tffrom pylab import *import argparseimport mlp# Basic model parameters as external flags.tf.app.flags.FLAGS = tf.python.platform.flags._FlagValues()tf.app.flags._global_parser = argparse.ArgumentParser()flags = tf.app.flagsFLAGS = flags.FLAGSflags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')flags.DEFINE_integer('max_steps', 20, 'Number of steps to run trainer.')flags.DEFINE_integer('batch_size', 1000, 'Batch size. Must divide evenly into the dataset sizes.')flags.DEFINE_integer('num_samples', 100000, 'Total number of samples. Needed by the reader')flags.DEFINE_string('training_set_file', 'godzilla_dataset_size625', 'Training set file')flags.DEFINE_string('test_set_file', 'godzilla_testset_size625', 'Test set file')flags.DEFINE_string('test_size', 1000, 'Test set size')def placeholder_inputs(batch_size): images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, mlp.NUM_INPUT)) labels_placeholder = tf.placeholder(tf.float32, shape=(batch_size, mlp.NUM_OUTPUT)) return images_placeholder, labels_placeholderdef fill_feed_dict(data_set_file, images_pl, labels_pl): for l in range(int(FLAGS.num_samples/FLAGS.batch_size)): data_set = genfromtxt("../dataset/" + data_set_file, skip_header=l*FLAGS.batch_size, max_rows=FLAGS.batch_size) data_set = reshape(data_set, [FLAGS.batch_size, mlp.NUM_INPUT + mlp.NUM_OUTPUT]) images = data_set[:, :mlp.NUM_INPUT] labels_feed = reshape(data_set[:, mlp.NUM_INPUT:], [FLAGS.batch_size, mlp.NUM_OUTPUT]) images_feed = reshape(images, [FLAGS.batch_size, mlp.NUM_INPUT]) feed_dict = { images_pl: images_feed, labels_pl: labels_feed, } yield feed_dictdef reader(data_set_file, images_pl, labels_pl): data_set = loadtxt("../dataset/" + data_set_file) images = data_set[:, :mlp.NUM_INPUT] labels_feed = reshape(data_set[:, mlp.NUM_INPUT:], [data_set.shape[0], mlp.NUM_OUTPUT]) images_feed = reshape(images, [data_set.shape[0], mlp.NUM_INPUT]) feed_dict = { images_pl: images_feed, labels_pl: labels_feed, } return feed_dict, labels_pldef run_training(): tot_training_loss = [] tot_test_loss = [] tf.reset_default_graph() with tf.Graph().as_default() as g: images_placeholder, labels_placeholder = placeholder_inputs(FLAGS.batch_size) test_images_pl, test_labels_pl = placeholder_inputs(FLAGS.test_size) logits = mlp.inference(images_placeholder) test_pred = mlp.inference(test_images_pl, reuse=True) loss = mlp.loss(logits, labels_placeholder) test_loss = mlp.loss(test_pred, test_labels_pl) train_op = mlp.training(loss, FLAGS.learning_rate) #summary_op = tf.merge_all_summaries() init = tf.initialize_all_variables() saver = tf.train.Saver() sess = tf.Session() #summary_writer = tf.train.SummaryWriter("./", sess.graph) sess.run(init) test_feed, test_labels_placeholder = reader(FLAGS.test_set_file, test_images_pl, test_labels_pl) # Start the training loop. for step in xrange(FLAGS.max_steps): start_time = time.time() feed_gen = fill_feed_dict(FLAGS.training_set_file, images_placeholder, labels_placeholder) i=1 for feed_dict in feed_gen: _, loss_value = sess.run([train_op, loss], feed_dict=feed_dict) _, test_loss_val = sess.run([test_pred, test_loss], feed_dict=test_feed) tot_training_loss.append(loss_value) tot_test_loss.append(test_loss_val) #if i % 10 == 0: #print('%d minibatches analyzed...'%i) i+=1 if step % 1 == 0: duration = time.time() - start_time print('Epoch %d (%.3f sec):\n training loss = %f \n test loss = %f ' % (step, duration, loss_value, test_loss_val)) predictions = sess.run(test_pred, feed_dict=test_feed) savetxt("predictions", predictions) savetxt("training_loss", tot_training_loss) savetxt("test_loss", tot_test_loss) plot(tot_training_loss) plot(tot_test_loss) figure() scatter(test_feed[test_labels_placeholder], predictions) #plot([.4, .6], [.4, .6])run_training()#if __name__ == '__main__':# tf.app.run()这是mlp模块：
from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport mathimport tensorflow as tfNUM_OUTPUT = 1NUM_INPUT = 625NUM_HIDDEN = 5def inference(images, reuse=None): with tf.variable_scope('hidden1', reuse=reuse): weights = tf.get_variable(name='weights', shape=[NUM_INPUT, NUM_HIDDEN], initializer=tf.contrib.layers.xavier_initializer()) weight_decay = tf.mul(tf.nn.l2_loss(weights), 0.00001, name='weight_loss') tf.add_to_collection('losses', weight_decay) biases = tf.Variable(tf.constant(0.0, name='biases', shape=[NUM_HIDDEN])) hidden1_output = tf.nn.relu(tf.matmul(images, weights)+biases, name='hidden1') with tf.variable_scope('output', reuse=reuse): weights = tf.get_variable(name='weights', shape=[NUM_HIDDEN, NUM_OUTPUT], initializer=tf.contrib.layers.xavier_initializer()) weight_decay = tf.mul(tf.nn.l2_loss(weights), 0.00001, name='weight_loss') tf.add_to_collection('losses', weight_decay) biases = tf.Variable(tf.constant(0.0, name='biases', shape=[NUM_OUTPUT])) output = tf.nn.relu(tf.matmul(hidden1_output, weights)+biases, name='output') return outputdef loss(outputs, labels): rmse = tf.sqrt(tf.reduce_mean(tf.square(tf.sub(labels, outputs))), name="rmse") tf.add_to_collection('losses', rmse) return tf.add_n(tf.get_collection('losses'), name='total_loss')def training(loss, learning_rate): tf.scalar_summary(loss.op.name, loss) optimizer = tf.train.GradientDescentOptimizer(learning_rate) global_step = tf.Variable(0, name='global_step', trainable=False) train_op = optimizer.minimize(loss, global_step=global_step) return train_op这里是错误信息：
Traceback (most recent call last): File "<ipython-input-1-f16dfed3b99b>", line 1, in <module> runfile('/home/andrea/test/python/main_mlp_yield.py', wdir='/home/andrea/test/python') File "/usr/local/lib/python2.7/dist-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 714, in runfile execfile(filename, namespace) File "/usr/local/lib/python2.7/dist-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 81, in execfile builtins.execfile(filename, *where) File "/home/andrea/test/python/main_mlp_yield.py", line 127, in <module> run_training() File "/home/andrea/test/python/main_mlp_yield.py", line 105, in run_training _, test_loss_val = sess.run([test_pred, test_loss], feed_dict=test_feed) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 372, in run run_metadata_ptr) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 636, in _run feed_dict_string, options, run_metadata) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 708, in _do_run target_list, options, run_metadata) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 728, in _do_call raise type(e)(node_def, op, message)InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [1000,625] [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[1000,625], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]Caused by op u'Placeholder', defined at: File "/usr/local/lib/python2.7/dist-packages/spyderlib/widgets/externalshell/start_ipython_kernel.py", line 205, in <module> __ipythonkernel__.start() File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelapp.py", line 442, in start ioloop.IOLoop.instance().start() File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/ioloop.py", line 162, in start super(ZMQIOLoop, self).start() File "/usr/local/lib/python2.7/dist-packages/tornado/ioloop.py", line 883, in start handler_func(fd_obj, events) File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 275, in null_wrapper return fn(*args, **kwargs) File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events self._handle_recv() File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv self._run_callback(callback, msg) File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback callback(*args, **kwargs) File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 275, in null_wrapper return fn(*args, **kwargs) File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 276, in dispatcher return self.dispatch_shell(stream, msg) File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 228, in dispatch_shell handler(stream, idents, msg) File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 391, in execute_request user_expressions, allow_stdin) File "/usr/local/lib/python2.7/dist-packages/ipykernel/ipkernel.py", line 199, in do_execute shell.run_cell(code, store_history=store_history, silent=silent) File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2723, in run_cell interactivity=interactivity, compiler=compiler, result=result) File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2831, in run_ast_nodes if self.run_code(code, result): File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2885, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-1-f16dfed3b99b>", line 1, in <module> runfile('/home/andrea/test/python/main_mlp_yield.py', wdir='/home/andrea/test/python') File "/usr/local/lib/python2.7/dist-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 714, in runfile execfile(filename, namespace) File "/usr/local/lib/python2.7/dist-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 81, in execfile builtins.execfile(filename, *where) File "/home/andrea/test/python/main_mlp_yield.py", line 127, in <module> run_training() File "/home/andrea/test/python/main_mlp_yield.py", line 79, in run_training images_placeholder, labels_placeholder = placeholder_inputs(FLAGS.batch_size) File "/home/andrea/test/python/main_mlp_yield.py", line 37, in placeholder_inputs images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, mlp.NUM_INPUT)) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/array_ops.py", line 895, in placeholder name=name) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 1238, in _placeholder name=name) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 704, in apply_op op_def=op_def) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2260, in create_op original_op=self._default_original_op, op_def=op_def) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1230, in __init__ self._traceback = _extract_stack()我真的不明白为什么会这样。看起来我已经在使用它们之前为所有占位符提供了值。我还删除了“merge_all_summaries”，因为这个问题与其他问题类似（这个和这个），但这并没有帮助
编辑：训练数据：100000个样本 x 625个特征测试数据：1000个样本 x 625个特征输出数量：1
回答：
我认为问题出在这段代码中：
def loss(outputs, labels): rmse = tf.sqrt(tf.reduce_mean(tf.square(tf.sub(labels, outputs))), name="rmse") tf.add_to_collection('losses', rmse) return tf.add_n(tf.get_collection('losses'), name='total_loss')您正在将集合’losses’中的所有损失加起来，包括训练和测试损失。特别是在这段代码中：
loss = mlp.loss(logits, labels_placeholder)test_loss = mlp.loss(test_pred, test_labels_pl)第一次调用mlp.loss会将训练损失添加到’losses’集合中。第二次调用mlp.loss会将其结果包含这些值。因此，当您尝试计算test_loss时，Tensorflow会抱怨您没有提供所有输入（训练占位符）。
也许您想要的是这样的代码？
def loss(outputs, labels): rmse = tf.sqrt(tf.reduce_mean(tf.square(tf.sub(labels, outputs))), name="rmse") return rmse希望这对您有帮助！
