我试图在tensorflow中创建一个简单的神经网络，它学习输入和输出之间的一些简单关系（例如，y = -x），其中输入和输出是浮点值（意思是，输出上没有使用softmax）。
我觉得这应该很容易，但我必须搞砸到某个地方。想知道是否有任何类似的教程或示例。我查看了现有的tensorflow教程并没有看到这样的内容，并查看了我通过谷歌搜索找到的其他几个张量流示例来源，但没有看到我在寻找什么。
这是我一直在尝试的精简版。在这个特定的版本中，我注意到我的重量和偏差似乎总是停留在零。也许这是由于我的单输入和单输出？
我已经好好改变雾的例子用于各种恶意目的，但是我已经成功工作的所有东西都在输出上使用softmax进行分类。如果我能弄清楚如何从我的神经网络生成原始浮点输出，我想用它做几个有趣的项目。
有人看到我错过了吗？提前致谢！ - J.
# Trying to define the simplest possible neural net where the output layer of the neural net is a single
# neuron with a "continuous" (a.k.a floating point) output. I want the neural net to output a continuous
# value based off one or more continuous inputs. My real problem is more complex, but this is the simplest
# representation of it for explaining my issue. Even though I've oversimplified this to look like a simple
# linear regression problem (y=m*x), I want to apply this to more complex neural nets. But if I can't get
# it working with this simple problem, then I won't get it working for anything more complex.
import tensorflow as tf
import random
import numpy as np
INPUT_DIMENSION = 1
OUTPUT_DIMENSION = 1
TRAINING_RUNS = 100
BATCH_SIZE = 10000
VERF_SIZE = 1
# Generate two arrays, the first array being the inputs that need trained on, and the second array containing outputs.
def generate_test_point():
x = random.uniform(-8, 8)
# To keep it simple, output is just -x.
out = -x
return ( np.array([ x ]), np.array([ out ]) )
# Generate a bunch of data points and then package them up in the array format needed by
# tensorflow
def generate_batch_data( num ):
xs = []
ys = []
for i in range(num):
x, y = generate_test_point()
xs.append( x )
ys.append( y )
return (np.array(xs), np.array(ys) )
# Define a single-layer neural net. Originally based off the tensorflow mnist for beginners tutorial
# Create a placeholder for our input variable
x = tf.placeholder(tf.float32, [None, INPUT_DIMENSION])
# Create variables for our neural net weights and bias
W = tf.Variable(tf.zeros([INPUT_DIMENSION, OUTPUT_DIMENSION]))
b = tf.Variable(tf.zeros([OUTPUT_DIMENSION]))
# Define the neural net. Note that since I'm not trying to classify digits as in the tensorflow mnist
# tutorial, I have removed the softmax op. My expectation is that 'net' will return a floating point
# value.
net = tf.matmul(x, W) + b
# Create a placeholder for the expected result during training
expected = tf.placeholder(tf.float32, [None, OUTPUT_DIMENSION])
# Same training as used in mnist example
cross_entropy = -tf.reduce_sum(expected*tf.log(tf.clip_by_value(net,1e-10,1.0)))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)
# Perform our training runs
for i in range( TRAINING_RUNS ):
print "trainin run: ", i,
batch_inputs, batch_outputs = generate_batch_data( BATCH_SIZE )
# I've found that my weights and bias values are always zero after training, and I'm not sure why.
sess.run( train_step, feed_dict={x: batch_inputs, expected: batch_outputs})
# Test our accuracy as we train... I am defining my accuracy as the error between what I
# expected and the actual output of the neural net.
#accuracy = tf.reduce_mean(tf.sub( expected, net))
accuracy = tf.sub( expected, net) # using just subtract since I made my verification size 1 for debug
# Uncomment this to debug
#import pdb; pdb.set_trace()
batch_inputs, batch_outputs = generate_batch_data( VERF_SIZE )
result = sess.run(accuracy, feed_dict={x: batch_inputs, expected: batch_outputs})
print " progress: "
print " inputs: ", batch_inputs
print " outputs:", batch_outputs
print " actual: ", result
您的损失应该是输出和真实值的平方差异：
loss = tf.reduce_mean(tf.square(expected - net))
通过这种方式，网络学会优化这种损失并使输出更接近实际结果。交叉熵只应用于0到1之间的输出值，即用于分类。
如果有人有兴趣，我得到了这个例子。这是代码：
# Trying to define the simplest possible neural net where the output layer of the neural net is a single
# neuron with a "continuous" (a.k.a floating point) output. I want the neural net to output a continuous
# value based off one or more continuous inputs. My real problem is more complex, but this is the simplest
# representation of it for explaining my issue. Even though I've oversimplified this to look like a simple
# linear regression problem (y=m*x), I want to apply this to more complex neural nets. But if I can't get
# it working with this simple problem, then I won't get it working for anything more complex.
import tensorflow as tf
import random
import numpy as np
INPUT_DIMENSION = 1
OUTPUT_DIMENSION = 1
TRAINING_RUNS = 100
BATCH_SIZE = 10000
VERF_SIZE = 1
# Generate two arrays, the first array being the inputs that need trained on, and the second array containing outputs.
def generate_test_point():
x = random.uniform(-8, 8)
# To keep it simple, output is just -x.
out = -x
return (np.array([x]), np.array([out]))
# Generate a bunch of data points and then package them up in the array format needed by
# tensorflow
def generate_batch_data(num):
xs = []
ys = []
for i in range(num):
x, y = generate_test_point()
xs.append(x)
ys.append(y)
return (np.array(xs), np.array(ys))
# Define a single-layer neural net. Originally based off the tensorflow mnist for beginners tutorial
# Create a placeholder for our input variable
x = tf.placeholder(tf.float32, [None, INPUT_DIMENSION])
# Create variables for our neural net weights and bias
W = tf.Variable(tf.zeros([INPUT_DIMENSION, OUTPUT_DIMENSION]))
b = tf.Variable(tf.zeros([OUTPUT_DIMENSION]))
# Define the neural net. Note that since I'm not trying to classify digits as in the tensorflow mnist
# tutorial, I have removed the softmax op. My expectation is that 'net' will return a floating point
# value.
net = tf.matmul(x, W) + b
# Create a placeholder for the expected result during training
expected = tf.placeholder(tf.float32, [None, OUTPUT_DIMENSION])
# Same training as used in mnist example
loss = tf.reduce_mean(tf.square(expected - net))
# cross_entropy = -tf.reduce_sum(expected*tf.log(tf.clip_by_value(net,1e-10,1.0)))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)
# Perform our training runs
for i in range(TRAINING_RUNS):
print("trainin run: ", i, )
batch_inputs, batch_outputs = generate_batch_data(BATCH_SIZE)
# I've found that my weights and bias values are always zero after training, and I'm not sure why.
sess.run(train_step, feed_dict={x: batch_inputs, expected: batch_outputs})
# Test our accuracy as we train... I am defining my accuracy as the error between what I
# expected and the actual output of the neural net.
# accuracy = tf.reduce_mean(tf.sub( expected, net))
accuracy = tf.subtract(expected, net) # using just subtract since I made my verification size 1 for debug
# tf.subtract()
# Uncomment this to debug
# import pdb; pdb.set_trace()
print("W=%f, b=%f" % (sess.run(W), sess.run(b)))
batch_inputs, batch_outputs = generate_batch_data(VERF_SIZE)
result = sess.run(accuracy, feed_dict={x: batch_inputs, expected: batch_outputs})
print(" progress: ")
print(" inputs: ", batch_inputs)
print(" outputs:", batch_outputs)
print(" actual: ", result)