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我有一个系统，可能在一瞬间有65个不同的属性。我想用dnn来预测它们。我的输入是系统的属性（79个二进制输入），输出是65个不连续的状态，可以是0,1,2。 因此，对于每个输入，我可能有一个输出向量，如[0,0,1,2,...,2,1,0,1,1]。为了使用dnn算法，我想要有65个softmax输出，每个输出有三个输出。所以，dnn的输出是大小为[,65*3]的矢量y。张量流中多输出的精度问题
我在tensorflow中用完全连接的网络实现了此问题。 但是，我在获得每个解决方案的准确性方面存在问题。
correct_predct = tf.reduce_sum(tf.cast([tf.equal(tf.argmax(y_[:,i*3:(i+1)*3],0) , tf.argmax(y[:,i*3:(i+1)*3],0)) for i in range(65)],tf.float32))
accuracy = tf.reduce_mean(tf.scalar_mul(1/65.0,correct_predct))
但是，它并没有因为y_和y的定义方式工作：对于每个样品和给定y_，可以得到精确度。
这里是我的代码：
import tensorflow as tf
import numpy as np
import scipy.io as sio
from tensorflow.python.training import queue_runner
tf.logging.set_verbosity(tf.logging.FATAL)
sess = tf.InteractiveSession()
maxiter = 50000
display = 100
decay_rate = 0.9
starter_learning_rate = 0.001
power = 0.75
l2lambda = .01
init_momentum = 0.9
decay_step = 5000
nnodes1 = 350
nnodes2 = 100
batch_size = 50
var = 2.0/(67+195)
print decay_rate,starter_learning_rate,power,l2lambda,init_momentum,decay_step,nnodes1,nnodes2,batch_size
result_mat = sio.loadmat('binarySysFuncProf.mat')
feature_mat = sio.loadmat('binaryDurSamples.mat')
result = result_mat['binarySysFuncProf']
feature = feature_mat['binaryDurSamples']
train_size=750000
test_size=250000
train_feature = feature[0:train_size,:]
train_output = result[0:train_size,:]
test_feature = feature[train_size + 1 : train_size + test_size , :]
test_output = result[train_size + 1 : train_size + test_size , :]
# import the data
#from tensorflow.examples.tutorials.mnist import input_data
# placeholders, which are the training data
x = tf.placeholder(tf.float64, shape=[None,79])
y_ = tf.placeholder(tf.float64, shape=[None,195])
learning_rate = tf.placeholder(tf.float64, shape=[])
# define the variables
W1 = tf.Variable(np.random.normal(0,var,(79,nnodes1)))
b1 = tf.Variable(np.random.normal(0,var,nnodes1))
W2 = tf.Variable(np.random.normal(0,var,(nnodes1,nnodes2)))
b2 = tf.Variable(np.random.normal(0,var,nnodes2))
W3 = tf.Variable(np.random.normal(0,var,(nnodes2,1)))
b3 = tf.Variable(np.random.normal(0,var,1))
# Passing global_step to minimize() will increment it at each step.
global_step = tf.Variable(0, trainable=False)
momentum = tf.Variable(init_momentum, trainable=False)
# prediction function (just one layer)
layer1 = tf.nn.sigmoid(tf.matmul(x,W1) + b1)
layer2 = tf.nn.sigmoid(tf.matmul(layer1,W2) + b2)
y = tf.nn.softmax(tf.matmul(layer2,W3) + b3)
cost_function =tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y)))
#cost_function = tf.sum([tf.reduce_mean((-tf.reduce_sum(y_[:,i*3:(i+1)*3])*tf.log(y[:,i*3:(i+1)*3]))) for i in range(65)])
correct_predct = tf.reduce_sum(tf.cast([tf.equal(tf.argmax(y_[:,i*3:(i+1)*3],0) , tf.argmax(y[:,i*3:(i+1)*3],0)) for i in range(65)],tf.float32))
accuracy = tf.reduce_mean(tf.scalar_mul(1/65.0,correct_predct))
l2regularization = tf.reduce_sum(tf.square(W1)) + tf.reduce_sum(tf.square(b1)) + tf.reduce_sum(tf.square(W2)) + tf.reduce_sum(tf.square(b2)) + tf.reduce_sum(tf.square(W3)) + tf.reduce_sum(tf.square(b3))
loss = (cost_function) + l2lambda*l2regularization
# define the learning_rate and its decaying procedure.
learning_rate = tf.train.exponential_decay(starter_learning_rate, global_step,decay_step, decay_rate, staircase=True)
# define the training paramters and model, gradient model and feeding the function
#train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
train_step = tf.train.MomentumOptimizer(learning_rate,0.9).minimize(loss, global_step=global_step)
# initilize the variables
sess.run(tf.initialize_all_variables())
# Train the Model for 1000 times. by defining the batch number we determine that it is sgd
for i in range(maxiter):
batch = np.random.randint(0,train_size,size=batch_size)
train_step.run(feed_dict={x:train_feature[batch,:], y_:train_output[batch,:]})
if np.mod(i,display) == 0:
train_loss = cost_function.eval(feed_dict={x: train_feature[0:train_size,:], y_: train_output[0:train_size,:]})
test_loss = cost_function.eval(feed_dict={x: test_feature, y_: test_output})
train_acc = 0#accuracy.eval(feed_dict={x: train_feature[0:train_size,:], y_: train_output[0:train_size,:]})
test_acc = 0#accuracy.eval(feed_dict={x: test_feature, y_: test_output})
print "Iter" , i, "lr" , learning_rate.eval() , "| Train loss" , train_loss , "| Test loss", test_loss , "| Train Accu", train_acc , "| Test Accu", test_acc ,"||W||",l2regularization.eval() , "lmbd*||W||", l2lambda*l2regularization.eval()
因为我有y_和y与列大小195矩阵，我收到以下错误：
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 555, in eval
return _eval_using_default_session(self, feed_dict, self.graph, session)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 3498, in _eval_using_default_session
return session.run(tensors, feed_dict)
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 625, in _run
% (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (750000, 195) for Tensor u'Placeholder_7:0', which has shape '(?, 3)'
我明白任何意见或帮助以获得准确性。
阿夫欣