尝试运行此代码时出现上述意外错误：
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 24 10:38:04 2016
@author: andrea
"""
# pylint: disable=missing-docstring
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
from pylab import *
import argparse
import 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.flags
FLAGS = flags.FLAGS
flags.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_placeholder
def 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_dict
def 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_pl
def 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：
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import tensorflow as tf
NUM_OUTPUT = 1
NUM_INPUT = 625
NUM_HIDDEN = 5
def 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 output
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')
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
这里的错误：
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。在我看来，在使用它们之前，我正在为所有占位符提供食物。我还删除了“merge_all_summaries”，因为此问题类似于其他（this和this），但它没有帮助
编辑：培训数据：100000个样本x 625个功能 测试数据：1000个样本x 625个功能 NUM。输出：1
答案 0 :(得分：5)
我认为问题在于此代码：
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')
您将收集“损失”中的所有损失加起来，包括您的培训和测试损失。特别是在这段代码中：
loss = mlp.loss(logits, labels_placeholder)
test_loss = mlp.loss(test_pred, test_labels_pl)
对mlp.loss的第一次调用将为'损失'收集增加训练损失。对mlp.loss的第二次调用将在结果中包含这些值。因此，当您尝试计算test_loss时，Tensorflow会抱怨您没有提供所有输入（培训占位符）。
也许你的意思是这样的？
def loss(outputs, labels):
rmse = tf.sqrt(tf.reduce_mean(tf.square(tf.sub(labels, outputs))), name="rmse")
return rmse
我希望有所帮助！