CODEWITHSUNDEEP
pythontensorflowdeep-learningsoftmax
Ali
Ali

Reputation: 974

sparse_softmax_cross_entropy_with_logits results is worse than softmax_cross_entropy_with_logits

I implement classic image classification problem with tensorflow, I have 9 classes, first I use softmax_cross_entropy_with_logits as classifier and train network, after some steps it gives to about 99% train accuracy,

Then test the same problem with sparse_softmax_cross_entropy_with_logits this time it doesn't converge at all,(train accuracy is around 0.10 and 0.20)

Only for your information, for softmax_cross_entropy_with_logits, I use [batch_size, num_classes] with dtype float32 for labels, and for sparse_softmax_cross_entropy_with_logits I use [batch_size] with dtype int32 for labels.

Does anybody have any idea?

Update:

this is code:

def costFun(self):  
    self.y_ = tf.reshape(self.y_, [-1]) 
    return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(self.score_, self.y_))

def updateFun(self):
    return tf.train.AdamOptimizer(learning_rate = self.lr_).minimize(self.cost_)

def perfFun(self):
    correct_pred = tf.equal(tf.argmax(self.score_,1), tf.argmax(y,1))
    return(tf.reduce_mean(tf.cast(correct_pred, tf.float32)))

def __init__(self,x,y,lr,lyr1FilterNo,lyr2FilterNo,lyr3FilterNo,fcHidLyrSize,inLyrSize,outLyrSize, keepProb):

    self.x_            = x
    self.y_            = y
    self.lr_           = lr
    self.inLyrSize     = inLyrSize
    self.outLyrSize_   = outLyrSize
    self.lyr1FilterNo_ = lyr1FilterNo
    self.lyr2FilterNo_ = lyr2FilterNo
    self.lyr3FilterNo_ = lyr3FilterNo
    self.fcHidLyrSize_ = fcHidLyrSize
    self.keepProb_     = keepProb

    [self.params_w_, self.params_b_] = ConvNet.paramsFun(self) 
    self.score_, self.PackShow_      = ConvNet.scoreFun (self) 
    self.cost_                       = ConvNet.costFun  (self) 
    self.update_                     = ConvNet.updateFun(self) 
    self.perf_                       = ConvNet.perfFun  (self)

main:

lyr1FilterNo = 32 
lyr2FilterNo = 64 
lyr3FilterNo = 128 

fcHidLyrSize = 1024
inLyrSize    = 32 * 32 

outLyrSize   = 9
lr           = 0.001
batch_size   = 300

dropout      = 0.5
x            = tf.placeholder(tf.float32, [None, inLyrSize ])
y            = tf.placeholder(tf.int32,    None             ) 

ConvNet_class = ConvNet(x,y,lr,lyr1FilterNo,lyr2FilterNo,lyr3FilterNo,fcHidLyrSize,inLyrSize,outLyrSize, keepProb)
initVar = tf.global_variables_initializer()


with tf.Session() as sess:
    sess.run(initVar)   

    for step in range(10000): 

        trData_i  = np.reshape( trData_i , ( -1, 32 * 32 ) ) 
        trLabel_i = np.reshape( trLabel_i, ( -1, 1       ) )  

        update_i, PackShow, wLyr1_i, wLyr2_i, wLyr3_i = sess.run([ConvNet_class.update_, ConvNet_class.PackShow_,
                            ConvNet_class.params_w_['wLyr1'], ConvNet_class.params_w_['wLyr2'], ConvNet_class.params_w_['wLyr3']], 
                            feed_dict = { x:trData_i, y:trLabel_i, keepProb:dropout} )

Upvotes: 1

Views: 946

Answers (1)

Ali
Ali

Reputation: 974

I found the problem, thanks to @mrry for helpful comment, actually I mistake about calculation of accuracy, in fact, "sparse_softmax" and "softmax" has the same loss(or cost) for input logits,

for computation accuracy, I change

correct_pred = tf.equal(tf.argmax(self.score_,1), tf.argmax(y,1))

to

correct_pred = tf.equal(tf.argmax(self.score_,1), y ))

since in "sparse_softmax" the ground truth labels are not in one-hot vector format, but real int32 or int64 numbers.

Upvotes: 3

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