我用 Google 的 TensorFlow 构建了一个 MLP图书馆。网络正在工作,但不知何故它拒绝正常学习。无论实际输入是什么,它总是收敛到接近 1.0 的输出。
完整代码可见here .
有什么想法吗?
输入和输出(批量大小为 4)如下:
input_data = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]] # XOR input
output_data = [[0.], [1.], [1.], [0.]] # XOR output
n_input = tf.placeholder(tf.float32, shape=[None, 2], name="n_input")
n_output = tf.placeholder(tf.float32, shape=[None, 1], name="n_output")
隐藏层配置:
# hidden layer's bias neuron
b_hidden = tf.Variable(0.1, name="hidden_bias")
# hidden layer's weight matrix initialized with a uniform distribution
W_hidden = tf.Variable(tf.random_uniform([2, hidden_nodes], -1.0, 1.0), name="hidden_weights")
# calc hidden layer's activation
hidden = tf.sigmoid(tf.matmul(n_input, W_hidden) + b_hidden)
输出层配置:
W_output = tf.Variable(tf.random_uniform([hidden_nodes, 1], -1.0, 1.0), name="output_weights") # output layer's weight matrix
output = tf.sigmoid(tf.matmul(hidden, W_output)) # calc output layer's activation
我的学习方法是这样的:
loss = tf.reduce_mean(cross_entropy) # mean the cross_entropy
optimizer = tf.train.GradientDescentOptimizer(0.01) # take a gradient descent for optimizing
train = optimizer.minimize(loss) # let the optimizer train
我为交叉熵尝试了两种设置:
cross_entropy = -tf.reduce_sum(n_output * tf.log(output))
和
cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(n_output, output)
其中 n_output
是 output_data
中描述的原始输出,output
是我的网络的预测/计算值。
for 循环内的训练(n 个时期)是这样的:
cvalues = sess.run([train, loss, W_hidden, b_hidden, W_output],
feed_dict={n_input: input_data, n_output: output_data})
我正在将结果保存到 cvalues 以调试 loss
, W_hidden
, ...
无论我尝试过什么,当我测试我的网络并尝试验证输出时,它总是会产生如下内容:
(...)
step: 2000
loss: 0.0137040186673
b_hidden: 1.3272010088
W_hidden: [[ 0.23195425 0.53248233 -0.21644847 -0.54775208 0.52298909]
[ 0.73933059 0.51440752 -0.08397482 -0.62724304 -0.53347367]]
W_output: [[ 1.65939867]
[ 0.78912479]
[ 1.4831928 ]
[ 1.28612828]
[ 1.12486529]]
(--- finished with 2000 epochs ---)
(Test input for validation:)
input: [0.0, 0.0] | output: [[ 0.99339396]]
input: [0.0, 1.0] | output: [[ 0.99289012]]
input: [1.0, 0.0] | output: [[ 0.99346077]]
input: [1.0, 1.0] | output: [[ 0.99261558]]
所以它没有正确地学习,但无论输入哪个输入,它总是收敛到接近 1.0。
最佳答案
与此同时,在一位同事的帮助下,我能够修复我的解决方案并希望将其发布以确保完整性。我的解决方案使用交叉熵并且不改变训练数据。此外,它具有所需的输入形状 (1, 2) 和输出是标量。
它利用 AdamOptimizer
比 GradientDescentOptimizer
更快地减少错误。参见 this post有关优化器的更多信息(和问题^^)。
事实上,我的网络仅用 400-800 个学习步骤就产生了相当不错的结果。
经过 2000 个学习步骤后,输出接近“完美”:
step: 2000
loss: 0.00103311243281
input: [0.0, 0.0] | output: [[ 0.00019799]]
input: [0.0, 1.0] | output: [[ 0.99979786]]
input: [1.0, 0.0] | output: [[ 0.99996307]]
input: [1.0, 1.0] | output: [[ 0.00033751]]
import tensorflow as tf
#####################
# preparation stuff #
#####################
# define input and output data
input_data = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]] # XOR input
output_data = [[0.], [1.], [1.], [0.]] # XOR output
# create a placeholder for the input
# None indicates a variable batch size for the input
# one input's dimension is [1, 2] and output's [1, 1]
n_input = tf.placeholder(tf.float32, shape=[None, 2], name="n_input")
n_output = tf.placeholder(tf.float32, shape=[None, 1], name="n_output")
# number of neurons in the hidden layer
hidden_nodes = 5
################
# hidden layer #
################
# hidden layer's bias neuron
b_hidden = tf.Variable(tf.random_normal([hidden_nodes]), name="hidden_bias")
# hidden layer's weight matrix initialized with a uniform distribution
W_hidden = tf.Variable(tf.random_normal([2, hidden_nodes]), name="hidden_weights")
# calc hidden layer's activation
hidden = tf.sigmoid(tf.matmul(n_input, W_hidden) + b_hidden)
################
# output layer #
################
W_output = tf.Variable(tf.random_normal([hidden_nodes, 1]), name="output_weights") # output layer's weight matrix
output = tf.sigmoid(tf.matmul(hidden, W_output)) # calc output layer's activation
############
# learning #
############
cross_entropy = -(n_output * tf.log(output) + (1 - n_output) * tf.log(1 - output))
# cross_entropy = tf.square(n_output - output) # simpler, but also works
loss = tf.reduce_mean(cross_entropy) # mean the cross_entropy
optimizer = tf.train.AdamOptimizer(0.01) # take a gradient descent for optimizing with a "stepsize" of 0.1
train = optimizer.minimize(loss) # let the optimizer train
####################
# initialize graph #
####################
init = tf.initialize_all_variables()
sess = tf.Session() # create the session and therefore the graph
sess.run(init) # initialize all variables
#####################
# train the network #
#####################
for epoch in xrange(0, 2001):
# run the training operation
cvalues = sess.run([train, loss, W_hidden, b_hidden, W_output],
feed_dict={n_input: input_data, n_output: output_data})
# print some debug stuff
if epoch % 200 == 0:
print("")
print("step: {:>3}".format(epoch))
print("loss: {}".format(cvalues[1]))
# print("b_hidden: {}".format(cvalues[3]))
# print("W_hidden: {}".format(cvalues[2]))
# print("W_output: {}".format(cvalues[4]))
print("")
print("input: {} | output: {}".format(input_data[0], sess.run(output, feed_dict={n_input: [input_data[0]]})))
print("input: {} | output: {}".format(input_data[1], sess.run(output, feed_dict={n_input: [input_data[1]]})))
print("input: {} | output: {}".format(input_data[2], sess.run(output, feed_dict={n_input: [input_data[2]]})))
print("input: {} | output: {}".format(input_data[3], sess.run(output, feed_dict={n_input: [input_data[3]]})))
关于python - TensorFlow MLP 不训练 XOR,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/33997823/