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In [1]:



    


import tensorflow as tf
import numpy as np



    











In [2]:



    


with np.load("notMNIST.npz") as data:
    Data, Target = data ["images"], data["labels"]
    np.random.seed(521)
    randIndx = np.arange(len(Data))
    np.random.shuffle(randIndx)
    Data = Data[randIndx]/255.
    Target = Target[randIndx]
    trainData, trainTarget = Data[:15000], Target[:15000]
    validData, validTarget = Data[15000:16000], Target[15000:16000]
    testData, testTarget = Data[16000:], Target[16000:]



    











In [3]:



    


def linear(x, numHidden):
    numInput = int(x.get_shape()[1])
    tmp = (numInput+numHidden)
    weights = tf.Variable(tf.random_normal([numInput, numHidden], 
                          stddev=tf.sqrt(1./tmp)), 
                          name = "w")
    bias = tf.Variable(tf.zeros( [numHidden,]), name="b")
    weights_loss = tf.reduce_sum(weights**2)/2.
    tf.add_to_collection("weights_loss", weights_loss)
    return tf.nn.bias_add(tf.matmul(x, weights), bias)



    











In [4]:



    


tf.reset_default_graph()

x = tf.placeholder(tf.float32, shape=(None, 28,28))
y_target = tf.placeholder(tf.float32, [None,], name='target_y')
y_onehot = tf.to_float(tf.equal(tf.expand_dims(y_target, 1),
                                tf.to_float(tf.constant(
                                    np.arange(10).reshape(1,-1)))))
x_reshape = tf.reshape(x, [-1, 28*28])
h = x_reshape
for i in range(1):
    h = tf.nn.relu(linear(h, 1000))
yhat = (linear(h, 10))
crossEntropyError = tf.reduce_mean(
                tf.nn.softmax_cross_entropy_with_logits(yhat, y_onehot))
weightsError = tf.add_n(tf.get_collection("weights_loss"))
loss = crossEntropyError + 3e-4*weightsError
acc = tf.reduce_mean(tf.to_float(
                    tf.equal(tf.to_float(tf.arg_max(yhat, 1)), 
                             tf.to_float(y_target))))
train_op = tf.train.AdamOptimizer(learning_rate=0.001,).minimize(loss)



    











In [5]:



    


init = tf.initialize_all_variables()
sess = tf.InteractiveSession()



    











In [9]:



    


sess.run(init)
loss.eval(feed_dict={x:validData, y_target:validTarget})
acc.eval(feed_dict={x:validData, y_target:validTarget})



    


















    
Out[9]:








0.079999998

















In [10]:



    


B = 500



    











In [ ]:



    


for step in xrange(0,1000):
        randIdx = np.arange(len(trainData))
        np.random.shuffle(randIdx)
        for i in range(int(len(trainData)/B)):
            feeddict = {x: trainData[i*B:(i+1)*B], y_target: trainTarget[i*B:(i+1)*B], }
            _, err, Acc = sess.run([train_op, crossEntropyError,acc], feed_dict=feeddict)
        Val_err = loss.eval(feed_dict={x:validData, y_target:validTarget})
        Val_Acc = acc.eval(feed_dict={x:validData, y_target:validTarget})
        print("Iter: %3d, CE-train: %4.2f, CE-valid: %4.2f, Acc-train: %4.2f, Acc-valid: %4.4f"%(step, err, Val_err, Acc, Val_Acc))



    


















    






Iter:   0, CE-train: 0.42, CE-valid: 0.49, Acc-train: 0.88, Acc-valid: 0.8930
Iter:   1, CE-train: 0.33, CE-valid: 0.45, Acc-train: 0.91, Acc-valid: 0.9060
Iter:   2, CE-train: 0.28, CE-valid: 0.43, Acc-train: 0.93, Acc-valid: 0.9110
Iter:   3, CE-train: 0.25, CE-valid: 0.40, Acc-train: 0.93, Acc-valid: 0.9100
Iter:   4, CE-train: 0.21, CE-valid: 0.39, Acc-train: 0.94, Acc-valid: 0.9210
Iter:   5, CE-train: 0.17, CE-valid: 0.40, Acc-train: 0.95, Acc-valid: 0.9230
Iter:   6, CE-train: 0.17, CE-valid: 0.39, Acc-train: 0.96, Acc-valid: 0.9270
Iter:   7, CE-train: 0.15, CE-valid: 0.42, Acc-train: 0.97, Acc-valid: 0.9240
Iter:   8, CE-train: 0.12, CE-valid: 0.39, Acc-train: 0.97, Acc-valid: 0.9310
Iter:   9, CE-train: 0.10, CE-valid: 0.40, Acc-train: 0.97, Acc-valid: 0.9300
Iter:  10, CE-train: 0.07, CE-valid: 0.40, Acc-train: 0.98, Acc-valid: 0.9340
Iter:  11, CE-train: 0.06, CE-valid: 0.41, Acc-train: 0.99, Acc-valid: 0.9370
Iter:  12, CE-train: 0.09, CE-valid: 0.42, Acc-train: 0.97, Acc-valid: 0.9280
Iter:  13, CE-train: 0.04, CE-valid: 0.44, Acc-train: 0.99, Acc-valid: 0.9220
Iter:  14, CE-train: 0.08, CE-valid: 0.41, Acc-train: 0.98, Acc-valid: 0.9320
Iter:  15, CE-train: 0.07, CE-valid: 0.45, Acc-train: 0.98, Acc-valid: 0.9240
Iter:  16, CE-train: 0.08, CE-valid: 0.41, Acc-train: 0.98, Acc-valid: 0.9280
Iter:  17, CE-train: 0.06, CE-valid: 0.43, Acc-train: 0.98, Acc-valid: 0.9190
Iter:  18, CE-train: 0.05, CE-valid: 0.39, Acc-train: 0.99, Acc-valid: 0.9330
Iter:  19, CE-train: 0.07, CE-valid: 0.42, Acc-train: 0.99, Acc-valid: 0.9290
Iter:  20, CE-train: 0.07, CE-valid: 0.41, Acc-train: 0.98, Acc-valid: 0.9290
Iter:  21, CE-train: 0.03, CE-valid: 0.39, Acc-train: 0.99, Acc-valid: 0.9260
Iter:  22, CE-train: 0.05, CE-valid: 0.41, Acc-train: 0.99, Acc-valid: 0.9280
Iter:  23, CE-train: 0.03, CE-valid: 0.42, Acc-train: 1.00, Acc-valid: 0.9290
Iter:  24, CE-train: 0.04, CE-valid: 0.41, Acc-train: 0.99, Acc-valid: 0.9310
Iter:  25, CE-train: 0.03, CE-valid: 0.41, Acc-train: 0.99, Acc-valid: 0.9330
Iter:  26, CE-train: 0.03, CE-valid: 0.42, Acc-train: 0.99, Acc-valid: 0.9280
Iter:  27, CE-train: 0.02, CE-valid: 0.40, Acc-train: 1.00, Acc-valid: 0.9390
Iter:  28, CE-train: 0.03, CE-valid: 0.40, Acc-train: 0.99, Acc-valid: 0.9270
Iter:  29, CE-train: 0.03, CE-valid: 0.41, Acc-train: 0.99, Acc-valid: 0.9250
Iter:  30, CE-train: 0.02, CE-valid: 0.39, Acc-train: 1.00, Acc-valid: 0.9300
Iter:  31, CE-train: 0.02, CE-valid: 0.37, Acc-train: 1.00, Acc-valid: 0.9390
Iter:  32, CE-train: 0.02, CE-valid: 0.39, Acc-train: 1.00, Acc-valid: 0.9390
Iter:  33, CE-train: 0.02, CE-valid: 0.42, Acc-train: 0.99, Acc-valid: 0.9340
Iter:  34, CE-train: 0.01, CE-valid: 0.40, Acc-train: 1.00, Acc-valid: 0.9300
Iter:  35, CE-train: 0.02, CE-valid: 0.41, Acc-train: 0.99, Acc-valid: 0.9340
Iter:  36, CE-train: 0.02, CE-valid: 0.44, Acc-train: 1.00, Acc-valid: 0.9260
Iter:  37, CE-train: 0.02, CE-valid: 0.42, Acc-train: 1.00, Acc-valid: 0.9320
Iter:  38, CE-train: 0.02, CE-valid: 0.39, Acc-train: 0.99, Acc-valid: 0.9330
Iter:  39, CE-train: 0.01, CE-valid: 0.41, Acc-train: 1.00, Acc-valid: 0.9300
Iter:  40, CE-train: 0.01, CE-valid: 0.39, Acc-train: 1.00, Acc-valid: 0.9300
Iter:  41, CE-train: 0.01, CE-valid: 0.40, Acc-train: 1.00, Acc-valid: 0.9330
Iter:  42, CE-train: 0.01, CE-valid: 0.40, Acc-train: 1.00, Acc-valid: 0.9270
Iter:  43, CE-train: 0.02, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9330
Iter:  44, CE-train: 0.01, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9400
Iter:  45, CE-train: 0.02, CE-valid: 0.38, Acc-train: 0.99, Acc-valid: 0.9310
Iter:  46, CE-train: 0.01, CE-valid: 0.39, Acc-train: 1.00, Acc-valid: 0.9260
Iter:  47, CE-train: 0.02, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9320
Iter:  48, CE-train: 0.04, CE-valid: 0.39, Acc-train: 0.99, Acc-valid: 0.9280
Iter:  49, CE-train: 0.02, CE-valid: 0.37, Acc-train: 1.00, Acc-valid: 0.9330
Iter:  50, CE-train: 0.02, CE-valid: 0.39, Acc-train: 0.99, Acc-valid: 0.9280
Iter:  51, CE-train: 0.04, CE-valid: 0.40, Acc-train: 0.98, Acc-valid: 0.9230
Iter:  52, CE-train: 0.02, CE-valid: 0.41, Acc-train: 0.99, Acc-valid: 0.9320
Iter:  53, CE-train: 0.03, CE-valid: 0.39, Acc-train: 0.99, Acc-valid: 0.9250
Iter:  54, CE-train: 0.04, CE-valid: 0.39, Acc-train: 0.98, Acc-valid: 0.9290
Iter:  55, CE-train: 0.02, CE-valid: 0.39, Acc-train: 0.99, Acc-valid: 0.9280
Iter:  56, CE-train: 0.01, CE-valid: 0.39, Acc-train: 1.00, Acc-valid: 0.9310
Iter:  57, CE-train: 0.01, CE-valid: 0.42, Acc-train: 1.00, Acc-valid: 0.9250
Iter:  58, CE-train: 0.01, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9340
Iter:  59, CE-train: 0.01, CE-valid: 0.43, Acc-train: 1.00, Acc-valid: 0.9250
Iter:  60, CE-train: 0.01, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9350
Iter:  61, CE-train: 0.01, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9280
Iter:  62, CE-train: 0.01, CE-valid: 0.39, Acc-train: 1.00, Acc-valid: 0.9320
Iter:  63, CE-train: 0.01, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9380
Iter:  64, CE-train: 0.01, CE-valid: 0.39, Acc-train: 1.00, Acc-valid: 0.9350
Iter:  65, CE-train: 0.01, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9360
Iter:  66, CE-train: 0.01, CE-valid: 0.39, Acc-train: 1.00, Acc-valid: 0.9330
Iter:  67, CE-train: 0.01, CE-valid: 0.37, Acc-train: 1.00, Acc-valid: 0.9350
Iter:  68, CE-train: 0.01, CE-valid: 0.36, Acc-train: 1.00, Acc-valid: 0.9360
Iter:  69, CE-train: 0.01, CE-valid: 0.37, Acc-train: 1.00, Acc-valid: 0.9360
Iter:  70, CE-train: 0.01, CE-valid: 0.36, Acc-train: 1.00, Acc-valid: 0.9320
Iter:  71, CE-train: 0.01, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9300
Iter:  72, CE-train: 0.01, CE-valid: 0.36, Acc-train: 1.00, Acc-valid: 0.9330
Iter:  73, CE-train: 0.01, CE-valid: 0.36, Acc-train: 1.00, Acc-valid: 0.9320
Iter:  74, CE-train: 0.01, CE-valid: 0.39, Acc-train: 1.00, Acc-valid: 0.9300
Iter:  75, CE-train: 0.02, CE-valid: 0.37, Acc-train: 0.99, Acc-valid: 0.9330
Iter:  76, CE-train: 0.02, CE-valid: 0.42, Acc-train: 1.00, Acc-valid: 0.9260
Iter:  77, CE-train: 0.01, CE-valid: 0.36, Acc-train: 1.00, Acc-valid: 0.9350
Iter:  78, CE-train: 0.04, CE-valid: 0.39, Acc-train: 0.99, Acc-valid: 0.9310
Iter:  79, CE-train: 0.03, CE-valid: 0.40, Acc-train: 0.99, Acc-valid: 0.9340
Iter:  80, CE-train: 0.02, CE-valid: 0.37, Acc-train: 0.99, Acc-valid: 0.9370
Iter:  81, CE-train: 0.02, CE-valid: 0.47, Acc-train: 1.00, Acc-valid: 0.9220
Iter:  82, CE-train: 0.02, CE-valid: 0.40, Acc-train: 0.99, Acc-valid: 0.9320
Iter:  83, CE-train: 0.01, CE-valid: 0.41, Acc-train: 1.00, Acc-valid: 0.9310
Iter:  84, CE-train: 0.01, CE-valid: 0.37, Acc-train: 1.00, Acc-valid: 0.9340
Iter:  85, CE-train: 0.01, CE-valid: 0.39, Acc-train: 1.00, Acc-valid: 0.9250
Iter:  86, CE-train: 0.03, CE-valid: 0.42, Acc-train: 0.99, Acc-valid: 0.9250
Iter:  87, CE-train: 0.00, CE-valid: 0.39, Acc-train: 1.00, Acc-valid: 0.9310
Iter:  88, CE-train: 0.01, CE-valid: 0.40, Acc-train: 1.00, Acc-valid: 0.9320
Iter:  89, CE-train: 0.01, CE-valid: 0.39, Acc-train: 1.00, Acc-valid: 0.9320
Iter:  90, CE-train: 0.01, CE-valid: 0.41, Acc-train: 1.00, Acc-valid: 0.9260
Iter:  91, CE-train: 0.01, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9360
Iter:  92, CE-train: 0.00, CE-valid: 0.39, Acc-train: 1.00, Acc-valid: 0.9350
Iter:  93, CE-train: 0.00, CE-valid: 0.41, Acc-train: 1.00, Acc-valid: 0.9350
Iter:  94, CE-train: 0.01, CE-valid: 0.40, Acc-train: 1.00, Acc-valid: 0.9310
Iter:  95, CE-train: 0.01, CE-valid: 0.40, Acc-train: 1.00, Acc-valid: 0.9310
Iter:  96, CE-train: 0.01, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9330
Iter:  97, CE-train: 0.00, CE-valid: 0.37, Acc-train: 1.00, Acc-valid: 0.9330
Iter:  98, CE-train: 0.01, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9390
Iter:  99, CE-train: 0.01, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9340
Iter: 100, CE-train: 0.00, CE-valid: 0.36, Acc-train: 1.00, Acc-valid: 0.9360
Iter: 101, CE-train: 0.01, CE-valid: 0.36, Acc-train: 1.00, Acc-valid: 0.9320
Iter: 102, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9390
Iter: 103, CE-train: 0.00, CE-valid: 0.33, Acc-train: 1.00, Acc-valid: 0.9390
Iter: 104, CE-train: 0.00, CE-valid: 0.33, Acc-train: 1.00, Acc-valid: 0.9380
Iter: 105, CE-train: 0.00, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9350
Iter: 106, CE-train: 0.00, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9350
Iter: 107, CE-train: 0.01, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9360
Iter: 108, CE-train: 0.01, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9320
Iter: 109, CE-train: 0.01, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9330
Iter: 110, CE-train: 0.01, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9380
Iter: 111, CE-train: 0.03, CE-valid: 0.38, Acc-train: 0.99, Acc-valid: 0.9270
Iter: 112, CE-train: 0.06, CE-valid: 0.42, Acc-train: 0.98, Acc-valid: 0.9200
Iter: 113, CE-train: 0.03, CE-valid: 0.40, Acc-train: 0.99, Acc-valid: 0.9170
Iter: 114, CE-train: 0.03, CE-valid: 0.44, Acc-train: 0.99, Acc-valid: 0.9160
Iter: 115, CE-train: 0.04, CE-valid: 0.44, Acc-train: 0.98, Acc-valid: 0.9180
Iter: 116, CE-train: 0.05, CE-valid: 0.40, Acc-train: 0.99, Acc-valid: 0.9210
Iter: 117, CE-train: 0.03, CE-valid: 0.39, Acc-train: 0.99, Acc-valid: 0.9280
Iter: 118, CE-train: 0.01, CE-valid: 0.39, Acc-train: 1.00, Acc-valid: 0.9320
Iter: 119, CE-train: 0.01, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9310
Iter: 120, CE-train: 0.00, CE-valid: 0.40, Acc-train: 1.00, Acc-valid: 0.9340
Iter: 121, CE-train: 0.01, CE-valid: 0.41, Acc-train: 1.00, Acc-valid: 0.9280
Iter: 122, CE-train: 0.00, CE-valid: 0.42, Acc-train: 1.00, Acc-valid: 0.9290
Iter: 123, CE-train: 0.00, CE-valid: 0.41, Acc-train: 1.00, Acc-valid: 0.9310
Iter: 124, CE-train: 0.00, CE-valid: 0.40, Acc-train: 1.00, Acc-valid: 0.9290
Iter: 125, CE-train: 0.00, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9340
Iter: 126, CE-train: 0.00, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9340
Iter: 127, CE-train: 0.00, CE-valid: 0.37, Acc-train: 1.00, Acc-valid: 0.9360
Iter: 128, CE-train: 0.00, CE-valid: 0.36, Acc-train: 1.00, Acc-valid: 0.9330
Iter: 129, CE-train: 0.00, CE-valid: 0.36, Acc-train: 1.00, Acc-valid: 0.9310
Iter: 130, CE-train: 0.00, CE-valid: 0.36, Acc-train: 1.00, Acc-valid: 0.9340
Iter: 131, CE-train: 0.01, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9310
Iter: 132, CE-train: 0.00, CE-valid: 0.36, Acc-train: 1.00, Acc-valid: 0.9320
Iter: 133, CE-train: 0.00, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9340
Iter: 134, CE-train: 0.00, CE-valid: 0.36, Acc-train: 1.00, Acc-valid: 0.9320
Iter: 135, CE-train: 0.00, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9290
Iter: 136, CE-train: 0.00, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9310
Iter: 137, CE-train: 0.00, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9300
Iter: 138, CE-train: 0.01, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9300
Iter: 139, CE-train: 0.00, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9300
Iter: 140, CE-train: 0.00, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9290
Iter: 141, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9370
Iter: 142, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9330
Iter: 143, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9320
Iter: 144, CE-train: 0.00, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9270
Iter: 145, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9310
Iter: 146, CE-train: 0.01, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9310
Iter: 147, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9330
Iter: 148, CE-train: 0.00, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9330
Iter: 149, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9320
Iter: 150, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9350
Iter: 151, CE-train: 0.01, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9340
Iter: 152, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9340
Iter: 153, CE-train: 0.01, CE-valid: 0.36, Acc-train: 1.00, Acc-valid: 0.9250
Iter: 154, CE-train: 0.01, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9320
Iter: 155, CE-train: 0.07, CE-valid: 0.40, Acc-train: 0.97, Acc-valid: 0.9220
Iter: 156, CE-train: 0.08, CE-valid: 0.44, Acc-train: 0.97, Acc-valid: 0.9140
Iter: 157, CE-train: 0.07, CE-valid: 0.41, Acc-train: 0.98, Acc-valid: 0.9240
Iter: 158, CE-train: 0.07, CE-valid: 0.44, Acc-train: 0.98, Acc-valid: 0.9250
Iter: 159, CE-train: 0.03, CE-valid: 0.42, Acc-train: 0.99, Acc-valid: 0.9310
Iter: 160, CE-train: 0.02, CE-valid: 0.39, Acc-train: 1.00, Acc-valid: 0.9320
Iter: 161, CE-train: 0.01, CE-valid: 0.40, Acc-train: 1.00, Acc-valid: 0.9290
Iter: 162, CE-train: 0.02, CE-valid: 0.41, Acc-train: 1.00, Acc-valid: 0.9290
Iter: 163, CE-train: 0.02, CE-valid: 0.40, Acc-train: 1.00, Acc-valid: 0.9320
Iter: 164, CE-train: 0.01, CE-valid: 0.39, Acc-train: 1.00, Acc-valid: 0.9280
Iter: 165, CE-train: 0.01, CE-valid: 0.42, Acc-train: 1.00, Acc-valid: 0.9310
Iter: 166, CE-train: 0.00, CE-valid: 0.40, Acc-train: 1.00, Acc-valid: 0.9260
Iter: 167, CE-train: 0.00, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9310
Iter: 168, CE-train: 0.00, CE-valid: 0.38, Acc-train: 1.00, Acc-valid: 0.9360
Iter: 169, CE-train: 0.00, CE-valid: 0.37, Acc-train: 1.00, Acc-valid: 0.9340
Iter: 170, CE-train: 0.00, CE-valid: 0.37, Acc-train: 1.00, Acc-valid: 0.9360
Iter: 171, CE-train: 0.00, CE-valid: 0.36, Acc-train: 1.00, Acc-valid: 0.9360
Iter: 172, CE-train: 0.00, CE-valid: 0.36, Acc-train: 1.00, Acc-valid: 0.9360
Iter: 173, CE-train: 0.00, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9360
Iter: 174, CE-train: 0.00, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9360
Iter: 175, CE-train: 0.00, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9360
Iter: 176, CE-train: 0.00, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9370
Iter: 177, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9350
Iter: 178, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9350
Iter: 179, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9350
Iter: 180, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9360
Iter: 181, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9370
Iter: 182, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9370
Iter: 183, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9370
Iter: 184, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9380
Iter: 185, CE-train: 0.00, CE-valid: 0.33, Acc-train: 1.00, Acc-valid: 0.9370
Iter: 186, CE-train: 0.00, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9380
Iter: 187, CE-train: 0.00, CE-valid: 0.33, Acc-train: 1.00, Acc-valid: 0.9370
Iter: 188, CE-train: 0.00, CE-valid: 0.33, Acc-train: 1.00, Acc-valid: 0.9380
Iter: 189, CE-train: 0.00, CE-valid: 0.33, Acc-train: 1.00, Acc-valid: 0.9390
Iter: 190, CE-train: 0.00, CE-valid: 0.33, Acc-train: 1.00, Acc-valid: 0.9380
Iter: 191, CE-train: 0.00, CE-valid: 0.33, Acc-train: 1.00, Acc-valid: 0.9380
Iter: 192, CE-train: 0.00, CE-valid: 0.33, Acc-train: 1.00, Acc-valid: 0.9390
Iter: 193, CE-train: 0.00, CE-valid: 0.33, Acc-train: 1.00, Acc-valid: 0.9370
Iter: 194, CE-train: 0.00, CE-valid: 0.33, Acc-train: 1.00, Acc-valid: 0.9350
Iter: 195, CE-train: 0.00, CE-valid: 0.33, Acc-train: 1.00, Acc-valid: 0.9370
Iter: 196, CE-train: 0.00, CE-valid: 0.33, Acc-train: 1.00, Acc-valid: 0.9380
Iter: 197, CE-train: 0.01, CE-valid: 0.33, Acc-train: 1.00, Acc-valid: 0.9320
Iter: 198, CE-train: 0.01, CE-valid: 0.34, Acc-train: 1.00, Acc-valid: 0.9330
Iter: 199, CE-train: 0.02, CE-valid: 0.35, Acc-train: 1.00, Acc-valid: 0.9330
Iter: 200, CE-train: 0.01, CE-valid: 0.43, Acc-train: 1.00, Acc-valid: 0.9210

















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Content source: tianrui/521dev

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