Question

我正在努力解决这个问题，我相信这是由于我的数据。我认为这是几个到多个回归问题，但在张量流中可能有更好的方法。

培训数据

我有一些视频序列生成的数据。对于每帧视频，我有每个群集的x，y位置分布。有157,110个框架和200,000个集群。帧和簇是输入，它们是整数，我认为可以被认为是标签（我将在稍后使用另一个网络来学习簇的序列）。由于每个直方图都与帧和clusterID相关，因此输入不是一个热点＆＃34;。直方图（输出）有19 + 8（x + y）个区间，其中每个计数很少超过10，并且可以归一化。

训练数据的子集可用here：前两列是帧和clusterID（输入），其余19 + 8列是直方图（输出）。

学习为给定的frame / clusterID对生成适当的直方图的最佳网络是什么？

以下代码是我目前使用MLP的尝试。它没有收敛;事实上，成本并没有下降。我的实现，选择MLP或输入数据缺少扩展是否有问题？

#!/usr/bin/python
# This program uses tensorflow to learn cluster probabilities and associate them with frame and cluster IDs

# Arguments
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("clusterProbabilityfile", help="CSV file containing cluster probabilities")
parser.add_argument("trainingIterations", type=int,  help="CSV file containing cluster probabilities")
args = parser.parse_args()

# Imports for ML
import tensorflow as tf
import numpy as np
from tensorflow.python.framework import dtypes

# Imports for loading CSV file
from tensorflow.python.platform import gfile 
import csv

# Global vars
numInputUnits = 2;
numOutputUnits = 19+8
numHiddenUnits = (numOutputUnits-numInputUnits)/2
workingDirectory = args.clusterProbabilityfile.split('/')[0]+"/"
columnSplit = 2 # Column number that splits 

# Shuffle training set
def shuffleTrainingSet(trainingSet):
    trainingIndecies = np.arange(len(trainingSet.data)) # assumes len(data) == len(target)
    np.random.shuffle(trainingIndecies) # shuffle indecies
    data = trainingSet.data[trainingIndecies]
    target = trainingSet.target[trainingIndecies]
    training_set = tf.contrib.learn.datasets.base.Dataset(data=data, target=target)
    return training_set

# Load training data from CSV file, convert to numpy arrays and construct Dataset
# Modified from tf.contrib.learn.datasets.base.load_csv_without_header
# Should these be randomized???
with gfile.Open(args.clusterProbabilityfile) as csv_file:
    data_file = csv.reader(csv_file)
    data, target = [], []
    for row in data_file:
        target.append(row[columnSplit+1:]) # All elements past the split column.
        data.append(row[:columnSplit]) # All elements before and including the split column.

target = np.array(target, dtype=int)
data = np.array(data, dtype=int)
training_set = tf.contrib.learn.datasets.base.Dataset(data=data, target=target)
training_set = shuffleTrainingSet(training_set)

# Construct computation graph
# MLP approach (from https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/multilayer_perceptron.py)
# Single hidden layer!
inputVec = tf.placeholder(tf.float32, [None, numInputUnits]) 
outputVec = tf.placeholder(tf.float32, [None, numOutputUnits])

# Weights
hiddenWeights = tf.Variable(tf.random_normal([numInputUnits, numHiddenUnits])) # inputUnits -> hiddenUnits
outputWeights = tf.Variable(tf.random_normal([numHiddenUnits, numOutputUnits])) # hiddenUnits -> outputUnits

# Biases
hiddenBiases = tf.Variable(tf.random_normal([numHiddenUnits]))
outputBiases = tf.Variable(tf.random_normal([numOutputUnits]))

# Contruct MLP from layers
hiddenLayer = tf.add(tf.matmul(inputVec, hiddenWeights), hiddenBiases) # input * weight + bias = hidden
hiddenLayer = tf.nn.relu(hiddenLayer) # RELU Activation function for hidden layer.
outputLayer = tf.add(tf.matmul(hiddenLayer, outputWeights), outputBiases) # hidden * weight + bias = output

# loss and optimizer
#cross_entropy = -(outputVec * tf.log(outputLayer) + (1 - outputVec) * tf.log(1 - outputLayer))
#cost = tf.reduce_mean(cross_entropy)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(outputLayer, outputVec))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)

# Compute graph
sess = tf.Session()
sess.run(tf.initialize_all_variables())
for epoch in range(args.trainingIterations):
    training_set = shuffleTrainingSet(training_set) # Reshuffle for each epoch.
    epochCost = sess.run(cost, feed_dict={inputVec: training_set.data, outputVec: training_set.target})
    print("{:d}\t{:f}".format(epoch, epochCost))

# Evaluate model
correct_prediction = tf.equal(tf.argmax(outputLayer,1), tf.argmax(outputVec,1)) # compare output layer with target output vector.
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print("Cost:", sess.run(cost,feed_dict={inputVec: training_set.data, outputVec: training_set.target}))
print("Accuracy:", sess.run(accuracy,feed_dict={inputVec: training_set.data, outputVec: training_set.target}))

使用MLP使用张量流将整数（输入）与直方图（输出）相关联

0 个答案: