In [1]:

    

from __future__ import division
import tensorflow as tf
import numpy as np
import tarfile
import os
import matplotlib.pyplot as plt
import time
%matplotlib inline


    

In [2]:

    

def csv_to_numpy_array(filePath, delimiter):
    return np.genfromtxt(filePath, delimiter=delimiter, dtype=None)

def import_data():
    if "data" not in os.listdir(os.getcwd()):
        # Untar directory of data if we haven't already
        tarObject = tarfile.open("data.tar.gz")
        tarObject.extractall()
        tarObject.close()
        print("Extracted tar to current directory")
    else:
        # we've already extracted the files
        pass

    print("loading training data")
    trainX = csv_to_numpy_array("data/trainX.csv", delimiter="\t")
    trainY = csv_to_numpy_array("data/trainY.csv", delimiter="\t")
    print("loading test data")
    testX = csv_to_numpy_array("data/testX.csv", delimiter="\t")
    testY = csv_to_numpy_array("data/testY.csv", delimiter="\t")
    return trainX,trainY,testX,testY

trainX,trainY,testX,testY = import_data()


    

    




---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-2-9f9cd1372658> in <module>()
     21     return trainX,trainY,testX,testY
     22 
---> 23 trainX,trainY,testX,testY = import_data()

<ipython-input-2-9f9cd1372658> in import_data()
      5     if "data" not in os.listdir(os.getcwd()):
      6         # Untar directory of data if we haven't already
----> 7         tarObject = tarfile.open("data.tar.gz")
      8         tarObject.extractall()
      9         tarObject.close()

/usr/lib/python3.4/tarfile.py in open(cls, name, mode, fileobj, bufsize, **kwargs)
   1546                     saved_pos = fileobj.tell()
   1547                 try:
-> 1548                     return func(name, "r", fileobj, **kwargs)
   1549                 except (ReadError, CompressionError) as e:
   1550                     if fileobj is not None:

/usr/lib/python3.4/tarfile.py in xzopen(cls, name, mode, fileobj, preset, **kwargs)
   1672             raise CompressionError("lzma module is not available")
   1673 
-> 1674         fileobj = lzma.LZMAFile(fileobj or name, mode, preset=preset)
   1675 
   1676         try:

/usr/lib/python3.4/lzma.py in __init__(self, filename, mode, format, check, preset, filters)
    125             if "b" not in mode:
    126                 mode += "b"
--> 127             self._fp = builtins.open(filename, mode)
    128             self._closefp = True
    129             self._mode = mode_code

FileNotFoundError: [Errno 2] No such file or directory: 'data.tar.gz'

In [ ]:

    

# DATA SET PARAMETERS
# Get our dimensions for our different variables and placeholders:
# numFeatures = the number of words extracted from each email
numFeatures = trainX.shape[1]
# numLabels = number of classes we are predicting (here just 2: Ham or Spam)
numLabels = trainY.shape[1]
numHidden = 4

# TRAINING SESSION PARAMETERS
# number of times we iterate through training data
# tensorboard shows that accuracy plateaus at ~25k epochs
numEpochs = 27000
# a smarter learning rate for gradientOptimizer
learningRate = tf.train.exponential_decay(learning_rate=0.0008,
                                          global_step= 1,
                                          decay_steps=trainX.shape[0],
                                          decay_rate= 0.95,
                                          staircase=True)


    

In [3]:

    

# X = X-matrix / feature-matrix / data-matrix... It's a tensor to hold our email
# data. 'None' here means that we can hold any number of emails
X = tf.placeholder(tf.float32, [None, numFeatures])
# yGold = Y-matrix / label-matrix / labels... This will be our correct answers
# matrix. Every row has either [1,0] for SPAM or [0,1] for HAM. 'None' here 
# means that we can hold any number of emails
yGold = tf.placeholder(tf.float32, [None, numLabels])


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-3-adfafedece5c> in <module>()
      1 # X = X-matrix / feature-matrix / data-matrix... It's a tensor to hold our email
      2 # data. 'None' here means that we can hold any number of emails
----> 3 X = tf.placeholder(tf.float32, [None, numFeatures])
      4 # yGold = Y-matrix / label-matrix / labels... This will be our correct answers
      5 # matrix. Every row has either [1,0] for SPAM or [0,1] for HAM. 'None' here

NameError: name 'numFeatures' is not defined

In [4]:

    

# Values are randomly sampled from a Gaussian with a standard deviation of:
#     sqrt(6 / (numInputNodes + numOutputNodes + 1))

weights_1_2 = tf.Variable(tf.random_normal([numFeatures,numHidden],
                                       mean=0,
                                       stddev=(np.sqrt(6/numFeatures+
                                                         numHidden+1)),
                                       name="weights_1_2"))

bias_hidden = tf.Variable(tf.random_normal([1,numHidden],
                                    mean=0,
                                    stddev=(np.sqrt(6/numFeatures+numLabels+1)),
                                    name="bias_hidden"))

weights_2_3 = tf.Variable(tf.random_normal([numHidden,numLabels],
                                       mean=0,
                                       stddev=(np.sqrt(6/numHidden+
                                                         numLabels+1)),
                                       name="weights_2_3"))

bias_out = tf.Variable(tf.random_normal([1,numLabels],
                                    mean=0,
                                    stddev=(np.sqrt(6/numFeatures+numLabels+1)),
                                    name="bias_out"))


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-4-ca2a142fcb45> in <module>()
      2 #     sqrt(6 / (numInputNodes + numOutputNodes + 1))
      3 
----> 4 weights_1_2 = tf.Variable(tf.random_normal([numFeatures,numHidden],
      5                                        mean=0,
      6                                        stddev=(np.sqrt(6/numFeatures+

NameError: name 'numFeatures' is not defined

In [5]:

    

# INITIALIZE our weights and biases
init_OP = tf.initialize_all_variables()

# Neural Network

apply_weights_1 = tf.matmul(X, weights_1_2, name="apply_weights")
add_bias_1 = tf.add(apply_weights_1, bias_hidden, name="add_bias") 
activation_1 = tf.nn.sigmoid(add_bias_1, name="activation")

apply_weights_OP = tf.matmul(activation_1, weights_2_3, name="apply_weights2")
add_bias_OP = tf.add(apply_weights_OP, bias_out, name="add_bias2") 
activation_OP = tf.nn.sigmoid(add_bias_OP, name="activation2")


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-5-03916c263eb2> in <module>()
      4 # Neural Network
      5 
----> 6 apply_weights_1 = tf.matmul(X, weights_1_2, name="apply_weights")
      7 add_bias_1 = tf.add(apply_weights_1, bias_hidden, name="add_bias")
      8 activation_1 = tf.nn.sigmoid(add_bias_1, name="activation")

NameError: name 'X' is not defined

In [6]:

    

cost_OP = tf.nn.l2_loss(activation_OP-yGold, name="squared_error_cost")


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-6-dd1ff71c28f8> in <module>()
----> 1 cost_OP = tf.nn.l2_loss(activation_OP-yGold, name="squared_error_cost")

NameError: name 'activation_OP' is not defined

In [36]:

    

# OPTIMIZATION ALGORITHM i.e. GRADIENT DESCENT
training_OP = tf.train.GradientDescentOptimizer(learningRate).minimize(cost_OP)


    

In [37]:

    

epoch_values=[]
accuracy_values=[]
cost_values=[]


    

In [38]:

    

# Create a tensorflow session
sess = tf.Session()

# Initialize all tensorflow variables
sess.run(init_OP)

## Ops for vizualization
# argmax(activation_OP, 1) gives the label our model thought was most likely
# argmax(yGold, 1) is the correct label
correct_predictions_OP = tf.equal(tf.argmax(activation_OP,1),tf.argmax(yGold,1))
# False is 0 and True is 1, what was our average?
accuracy_OP = tf.reduce_mean(tf.cast(correct_predictions_OP, "float"))


# Initialize reporting variables
cost = 0
diff = 1

# Training epochs
for i in range(numEpochs):
    if i > 1 and diff < .0001:
        print("change in cost %g; convergence."%diff)
        break
    else:
        # Run training step
        step = sess.run(training_OP, feed_dict={X: trainX, yGold: trainY})
        # Report occasional stats
        if i % 10 == 0:
            # Add epoch to epoch_values
            epoch_values.append(i)
            # Generate accuracy stats on test data
            train_accuracy, newCost = sess.run(
                [accuracy_OP, cost_OP], 
                feed_dict={X: trainX, yGold: trainY}
            )
            # Add accuracy to live graphing variable
            accuracy_values.append(train_accuracy)
            # Add cost to live graphing variable
            cost_values.append(newCost)
            # Write summary stats to writer
            #writer.add_summary(summary_results, i)
            # Re-assign values for variables
            diff = abs(newCost - cost)
            cost = newCost

            #generate print statements
            print("step %d, training accuracy %g"%(i, train_accuracy))
            print("step %d, cost %g"%(i, newCost))
            print("step %d, change in cost %g"%(i, diff))

           
            time.sleep(1)


# How well do we perform on held-out test data?
print("final accuracy on test set: %s" %str(sess.run(accuracy_OP, 
                                                     feed_dict={X: testX, 
                                                                yGold: testY})))


    

    




step 0, training accuracy 0.465897
step 0, cost 366.049
step 0, change in cost 366.049
step 10, training accuracy 0.464848
step 10, cost 291.254
step 10, change in cost 74.7946
step 20, training accuracy 0.624344
step 20, cost 242.376
step 20, change in cost 48.8781
step 30, training accuracy 0.565582
step 30, cost 235.061
step 30, change in cost 7.315
step 40, training accuracy 0.539349
step 40, cost 234.449
step 40, change in cost 0.611954
step 50, training accuracy 0.539349
step 50, cost 234.346
step 50, change in cost 0.1035
step 60, training accuracy 0.539349
step 60, cost 234.278
step 60, change in cost 0.0680084
step 70, training accuracy 0.539349
step 70, cost 234.211
step 70, change in cost 0.0665436
step 80, training accuracy 0.539349
step 80, cost 234.143
step 80, change in cost 0.067749
step 90, training accuracy 0.539349
step 90, cost 234.074
step 90, change in cost 0.0691528
step 100, training accuracy 0.539349
step 100, cost 234.004
step 100, change in cost 0.0704803
step 110, training accuracy 0.539349
step 110, cost 233.932
step 110, change in cost 0.0719452
step 120, training accuracy 0.541448
step 120, cost 233.858
step 120, change in cost 0.0734253
step 130, training accuracy 0.543547
step 130, cost 233.784
step 130, change in cost 0.0747528
step 140, training accuracy 0.544596
step 140, cost 233.707
step 140, change in cost 0.0762329
step 150, training accuracy 0.545645
step 150, cost 233.63
step 150, change in cost 0.077713
step 160, training accuracy 0.547744
step 160, cost 233.551
step 160, change in cost 0.079071
step 170, training accuracy 0.548793
step 170, cost 233.47
step 170, change in cost 0.0805359
step 180, training accuracy 0.550892
step 180, cost 233.388
step 180, change in cost 0.0820465
step 190, training accuracy 0.551941
step 190, cost 233.305
step 190, change in cost 0.0834961
step 200, training accuracy 0.552991
step 200, cost 233.219
step 200, change in cost 0.0850525
step 210, training accuracy 0.556139
step 210, cost 233.133
step 210, change in cost 0.08638
step 220, training accuracy 0.560336
step 220, cost 233.045
step 220, change in cost 0.0878143
step 230, training accuracy 0.562434
step 230, cost 232.956
step 230, change in cost 0.0892334
step 240, training accuracy 0.562434
step 240, cost 232.865
step 240, change in cost 0.0907593
step 250, training accuracy 0.564533
step 250, cost 232.773
step 250, change in cost 0.0921478
step 260, training accuracy 0.563484
step 260, cost 232.68
step 260, change in cost 0.0935059
step 270, training accuracy 0.566632
step 270, cost 232.585
step 270, change in cost 0.0949097
step 280, training accuracy 0.570829
step 280, cost 232.489
step 280, change in cost 0.0961456
step 290, training accuracy 0.572928
step 290, cost 232.391
step 290, change in cost 0.0975952
step 300, training accuracy 0.573977
step 300, cost 232.292
step 300, change in cost 0.0989075
step 310, training accuracy 0.576076
step 310, cost 232.192
step 310, change in cost 0.100159
step 320, training accuracy 0.577125
step 320, cost 232.09
step 320, change in cost 0.10144
step 330, training accuracy 0.576076
step 330, cost 231.988
step 330, change in cost 0.102692
step 340, training accuracy 0.580273
step 340, cost 231.884
step 340, change in cost 0.103851
step 350, training accuracy 0.58447
step 350, cost 231.779
step 350, change in cost 0.105026
step 360, training accuracy 0.586569
step 360, cost 231.673
step 360, change in cost 0.106125
step 370, training accuracy 0.587618
step 370, cost 231.565
step 370, change in cost 0.107269
step 380, training accuracy 0.589717
step 380, cost 231.457
step 380, change in cost 0.108429
step 390, training accuracy 0.587618
step 390, cost 231.348
step 390, change in cost 0.109314
step 400, training accuracy 0.587618
step 400, cost 231.237
step 400, change in cost 0.110321
step 410, training accuracy 0.585519
step 410, cost 231.126
step 410, change in cost 0.111374
step 420, training accuracy 0.585519
step 420, cost 231.014
step 420, change in cost 0.112183
step 430, training accuracy 0.587618
step 430, cost 230.901
step 430, change in cost 0.11319
step 440, training accuracy 0.589717
step 440, cost 230.787
step 440, change in cost 0.114029
step 450, training accuracy 0.589717
step 450, cost 230.672
step 450, change in cost 0.114761
step 460, training accuracy 0.588667
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step 460, change in cost 0.115662
step 470, training accuracy 0.591815
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step 470, change in cost 0.116333
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step 480, change in cost 0.116989
step 490, training accuracy 0.598111
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step 490, change in cost 0.117798
step 500, training accuracy 0.598111
step 500, cost 230.087
step 500, change in cost 0.118347
step 510, training accuracy 0.599161
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step 510, change in cost 0.11908
step 520, training accuracy 0.599161
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step 520, change in cost 0.119644
step 530, training accuracy 0.60021
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step 530, change in cost 0.120193
step 540, training accuracy 0.602309
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step 540, change in cost 0.120743
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step 550, change in cost 0.121368
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step 560, change in cost 0.121811
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step 570, change in cost 0.122345
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step 580, change in cost 0.122818
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step 590, change in cost 0.12326
step 600, training accuracy 0.607555
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step 600, change in cost 0.123672
step 610, training accuracy 0.610703
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step 610, change in cost 0.124207
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step 620, change in cost 0.12468
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step 630, change in cost 0.125092
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step 640, change in cost 0.125504
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step 750, change in cost 0.1306
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step 760, change in cost 0.131149
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step 780, change in cost 0.132339
step 790, training accuracy 0.624344
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step 790, change in cost 0.132797
step 800, training accuracy 0.624344
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step 800, change in cost 0.133347
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step 810, change in cost 0.134125
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step 820, change in cost 0.134735
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step 830, change in cost 0.135376
step 840, training accuracy 0.63064
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step 840, change in cost 0.136017
step 850, training accuracy 0.63064
step 850, cost 225.624
step 850, change in cost 0.136688
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step 860, change in cost 0.137451
step 870, training accuracy 0.632739
step 870, cost 225.348
step 870, change in cost 0.13829
step 880, training accuracy 0.631689
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step 880, change in cost 0.138885
step 890, training accuracy 0.633788
step 890, cost 225.069
step 890, change in cost 0.139801
step 900, training accuracy 0.636936
step 900, cost 224.929
step 900, change in cost 0.140472
step 910, training accuracy 0.635887
step 910, cost 224.787
step 910, change in cost 0.141479
step 920, training accuracy 0.636936
step 920, cost 224.645
step 920, change in cost 0.142319
step 930, training accuracy 0.637985
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step 930, change in cost 0.143021
step 940, training accuracy 0.637985
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step 940, change in cost 0.144135
step 950, training accuracy 0.636936
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step 950, change in cost 0.144836
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step 960, change in cost 0.145889
step 970, training accuracy 0.641133
step 970, cost 223.92
step 970, change in cost 0.146896
step 980, training accuracy 0.640084
step 980, cost 223.772
step 980, change in cost 0.147751
step 990, training accuracy 0.642183
step 990, cost 223.624
step 990, change in cost 0.148911
step 1000, training accuracy 0.641133
step 1000, cost 223.474
step 1000, change in cost 0.149841
step 1010, training accuracy 0.640084
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step 1010, change in cost 0.151031
step 1020, training accuracy 0.641133
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step 1020, change in cost 0.151855
step 1030, training accuracy 0.643232
step 1030, cost 223.018
step 1030, change in cost 0.153091
step 1040, training accuracy 0.642183
step 1040, cost 222.864
step 1040, change in cost 0.154236
step 1050, training accuracy 0.64638
step 1050, cost 222.708
step 1050, change in cost 0.155365
step 1060, training accuracy 0.649528
step 1060, cost 222.552
step 1060, change in cost 0.156433
step 1070, training accuracy 0.649528
step 1070, cost 222.394
step 1070, change in cost 0.157593
step 1080, training accuracy 0.650577
step 1080, cost 222.235
step 1080, change in cost 0.158813
step 1090, training accuracy 0.653725
step 1090, cost 222.075
step 1090, change in cost 0.160004
step 1100, training accuracy 0.655824
step 1100, cost 221.914
step 1100, change in cost 0.161301
step 1110, training accuracy 0.657922
step 1110, cost 221.751
step 1110, change in cost 0.162521
step 1120, training accuracy 0.657922
step 1120, cost 221.588
step 1120, change in cost 0.163742
step 1130, training accuracy 0.657922
step 1130, cost 221.423
step 1130, change in cost 0.165054
step 1140, training accuracy 0.658972
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step 1140, change in cost 0.166428
step 1150, training accuracy 0.660021
step 1150, cost 221.089
step 1150, change in cost 0.167572
step 1160, training accuracy 0.66107
step 1160, cost 220.92
step 1160, change in cost 0.168976
step 1170, training accuracy 0.66107
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step 1170, change in cost 0.170288
step 1180, training accuracy 0.66107
step 1180, cost 220.578
step 1180, change in cost 0.171661
step 1190, training accuracy 0.66212
step 1190, cost 220.405
step 1190, change in cost 0.173218
step 1200, training accuracy 0.66212
step 1200, cost 220.23
step 1200, change in cost 0.174332
step 1210, training accuracy 0.66212
step 1210, cost 220.054
step 1210, change in cost 0.175964
step 1220, training accuracy 0.66212
step 1220, cost 219.877
step 1220, change in cost 0.17717
step 1230, training accuracy 0.665268
step 1230, cost 219.698
step 1230, change in cost 0.178787
step 1240, training accuracy 0.667366
step 1240, cost 219.518
step 1240, change in cost 0.18013
step 1250, training accuracy 0.669465
step 1250, cost 219.336
step 1250, change in cost 0.181763
step 1260, training accuracy 0.672613
step 1260, cost 219.153
step 1260, change in cost 0.183121
step 1270, training accuracy 0.675761
step 1270, cost 218.969
step 1270, change in cost 0.184586
step 1280, training accuracy 0.677859
step 1280, cost 218.783
step 1280, change in cost 0.186111
step 1290, training accuracy 0.679958
step 1290, cost 218.595
step 1290, change in cost 0.187698
step 1300, training accuracy 0.681007
step 1300, cost 218.406
step 1300, change in cost 0.189102
step 1310, training accuracy 0.681007
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step 1310, change in cost 0.19072
step 1320, training accuracy 0.681007
step 1320, cost 218.023
step 1320, change in cost 0.192261
step 1330, training accuracy 0.679958
step 1330, cost 217.829
step 1330, change in cost 0.193832
step 1340, training accuracy 0.681007
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step 1340, change in cost 0.195496
step 1350, training accuracy 0.679958
step 1350, cost 217.436
step 1350, change in cost 0.196991
step 1360, training accuracy 0.679958
step 1360, cost 217.238
step 1360, change in cost 0.198593
step 1370, training accuracy 0.679958
step 1370, cost 217.038
step 1370, change in cost 0.200211
step 1380, training accuracy 0.681007
step 1380, cost 216.836
step 1380, change in cost 0.201874
step 1390, training accuracy 0.683106
step 1390, cost 216.632
step 1390, change in cost 0.203522
step 1400, training accuracy 0.683106
step 1400, cost 216.427
step 1400, change in cost 0.205154
step 1410, training accuracy 0.683106
step 1410, cost 216.22
step 1410, change in cost 0.206741
step 1420, training accuracy 0.684155
step 1420, cost 216.012
step 1420, change in cost 0.208511
step 1430, training accuracy 0.684155
step 1430, cost 215.802
step 1430, change in cost 0.210129
step 1440, training accuracy 0.685205
step 1440, cost 215.59
step 1440, change in cost 0.211761
step 1450, training accuracy 0.685205
step 1450, cost 215.376
step 1450, change in cost 0.213593
step 1460, training accuracy 0.686254
step 1460, cost 215.161
step 1460, change in cost 0.215286
step 1470, training accuracy 0.689402
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step 1470, change in cost 0.216843
step 1480, training accuracy 0.690451
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step 1480, change in cost 0.218689
step 1490, training accuracy 0.690451
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step 1490, change in cost 0.220337
step 1500, training accuracy 0.691501
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step 1500, change in cost 0.222137
step 1510, training accuracy 0.691501
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step 1510, change in cost 0.223816
step 1520, training accuracy 0.691501
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step 1520, change in cost 0.225708
step 1530, training accuracy 0.691501
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step 1530, change in cost 0.227386
step 1540, training accuracy 0.690451
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step 1540, change in cost 0.229095
step 1550, training accuracy 0.690451
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step 1550, change in cost 0.230988
step 1560, training accuracy 0.691501
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step 1560, change in cost 0.232666
step 1570, training accuracy 0.691501
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step 1570, change in cost 0.234589
step 1580, training accuracy 0.693599
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step 1580, change in cost 0.236298
step 1590, training accuracy 0.693599
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step 1590, change in cost 0.238098
step 1600, training accuracy 0.694649
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step 1600, change in cost 0.239929
step 1610, training accuracy 0.697796
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step 1610, change in cost 0.241699
step 1620, training accuracy 0.698846
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step 1620, change in cost 0.243561
step 1630, training accuracy 0.700944
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step 1630, change in cost 0.245483
step 1640, training accuracy 0.701994
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step 1640, change in cost 0.247238
step 1650, training accuracy 0.704092
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step 1650, change in cost 0.248978
step 1660, training accuracy 0.705142
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step 1660, change in cost 0.250946
step 1670, training accuracy 0.706191
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step 1670, change in cost 0.252808
step 1680, training accuracy 0.705142
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step 1680, change in cost 0.254639
step 1690, training accuracy 0.705142
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step 1690, change in cost 0.25647
step 1700, training accuracy 0.70724
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step 1700, change in cost 0.258347
step 1710, training accuracy 0.70829
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step 1710, change in cost 0.260239
step 1720, training accuracy 0.70829
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step 1720, change in cost 0.261993
step 1730, training accuracy 0.709339
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step 1730, change in cost 0.264023
step 1740, training accuracy 0.711438
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step 1740, change in cost 0.2659
step 1750, training accuracy 0.711438
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step 1750, change in cost 0.2677
step 1760, training accuracy 0.712487
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step 1760, change in cost 0.269653
step 1770, training accuracy 0.712487
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step 1770, change in cost 0.271484
step 1780, training accuracy 0.713536
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step 1780, change in cost 0.273453
step 1790, training accuracy 0.712487
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step 1790, change in cost 0.275345
step 1800, training accuracy 0.713536
step 1800, cost 206.777
step 1800, change in cost 0.27713
step 1810, training accuracy 0.714586
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step 1810, change in cost 0.279175
step 1820, training accuracy 0.715635
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step 1820, change in cost 0.280869
step 1830, training accuracy 0.717734
step 1830, cost 205.934
step 1830, change in cost 0.282959
step 1840, training accuracy 0.717734
step 1840, cost 205.65
step 1840, change in cost 0.284729
step 1850, training accuracy 0.717734
step 1850, cost 205.363
step 1850, change in cost 0.286743
step 1860, training accuracy 0.717734
step 1860, cost 205.074
step 1860, change in cost 0.288681
step 1870, training accuracy 0.718783
step 1870, cost 204.784
step 1870, change in cost 0.290405
step 1880, training accuracy 0.718783
step 1880, cost 204.491
step 1880, change in cost 0.29248
step 1890, training accuracy 0.719832
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step 1890, change in cost 0.29425
step 1900, training accuracy 0.720881
step 1900, cost 203.901
step 1900, change in cost 0.296295
step 1910, training accuracy 0.724029
step 1910, cost 203.603
step 1910, change in cost 0.298126
step 1920, training accuracy 0.725079
step 1920, cost 203.303
step 1920, change in cost 0.300049
step 1930, training accuracy 0.728227
step 1930, cost 203.001
step 1930, change in cost 0.301941
step 1940, training accuracy 0.729276
step 1940, cost 202.697
step 1940, change in cost 0.303909
step 1950, training accuracy 0.730325
step 1950, cost 202.391
step 1950, change in cost 0.305679
step 1960, training accuracy 0.730325
step 1960, cost 202.084
step 1960, change in cost 0.307602
step 1970, training accuracy 0.731375
step 1970, cost 201.774
step 1970, change in cost 0.309555
step 1980, training accuracy 0.733473
step 1980, cost 201.463
step 1980, change in cost 0.311447
step 1990, training accuracy 0.736621
step 1990, cost 201.149
step 1990, change in cost 0.313324
step 2000, training accuracy 0.736621
step 2000, cost 200.834
step 2000, change in cost 0.315201
step 2010, training accuracy 0.737671
step 2010, cost 200.517
step 2010, change in cost 0.317078
step 2020, training accuracy 0.739769
step 2020, cost 200.198
step 2020, change in cost 0.318939
step 2030, training accuracy 0.739769
step 2030, cost 199.877
step 2030, change in cost 0.320847
step 2040, training accuracy 0.73872
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step 2040, change in cost 0.322662
step 2050, training accuracy 0.73872
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step 2050, change in cost 0.3246
step 2060, training accuracy 0.741868
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step 2060, change in cost 0.326431
step 2070, training accuracy 0.743966
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step 2070, change in cost 0.328323
step 2080, training accuracy 0.743966
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step 2080, change in cost 0.330139
step 2090, training accuracy 0.743966
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step 2090, change in cost 0.332123
step 2100, training accuracy 0.746065
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step 2100, change in cost 0.33371
step 2110, training accuracy 0.747114
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step 2110, change in cost 0.335632
step 2120, training accuracy 0.751312
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step 2120, change in cost 0.33757
step 2130, training accuracy 0.750262
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step 2130, change in cost 0.33934
step 2140, training accuracy 0.750262
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step 2140, change in cost 0.341171
step 2150, training accuracy 0.749213
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step 2150, change in cost 0.342896
step 2160, training accuracy 0.750262
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step 2160, change in cost 0.344681
step 2170, training accuracy 0.751312
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step 2170, change in cost 0.346588
step 2180, training accuracy 0.752361
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step 2180, change in cost 0.348267
step 2190, training accuracy 0.75446
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step 2190, change in cost 0.350082
step 2200, training accuracy 0.755509
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step 2200, change in cost 0.351913
step 2210, training accuracy 0.758657
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step 2210, change in cost 0.353546
step 2220, training accuracy 0.758657
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step 2220, change in cost 0.355362
step 2230, training accuracy 0.760755
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step 2230, change in cost 0.357101
step 2240, training accuracy 0.761805
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step 2240, change in cost 0.358795
step 2250, training accuracy 0.762854
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step 2250, change in cost 0.360565
step 2260, training accuracy 0.761805
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step 2260, change in cost 0.362213
step 2270, training accuracy 0.761805
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step 2270, change in cost 0.363861
step 2280, training accuracy 0.766002
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step 2280, change in cost 0.365677
step 2290, training accuracy 0.766002
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step 2290, change in cost 0.367264
step 2300, training accuracy 0.767051
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step 2300, change in cost 0.368988
step 2310, training accuracy 0.768101
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step 2310, change in cost 0.370514
step 2320, training accuracy 0.76915
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step 2320, change in cost 0.372238
step 2330, training accuracy 0.774397
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step 2330, change in cost 0.373795
step 2340, training accuracy 0.774397
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step 2340, change in cost 0.375534
step 2350, training accuracy 0.774397
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step 2350, change in cost 0.376923
step 2360, training accuracy 0.775446
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step 2360, change in cost 0.378677
step 2370, training accuracy 0.776495
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step 2370, change in cost 0.380142
step 2380, training accuracy 0.779643
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step 2380, change in cost 0.381744
step 2390, training accuracy 0.780693
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step 2390, change in cost 0.383316
step 2400, training accuracy 0.780693
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step 2400, change in cost 0.384811
step 2410, training accuracy 0.782791
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step 2410, change in cost 0.386322
step 2420, training accuracy 0.785939
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step 2420, change in cost 0.387924
step 2430, training accuracy 0.785939
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step 2430, change in cost 0.389145
step 2440, training accuracy 0.785939
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step 2440, change in cost 0.390762
step 2450, training accuracy 0.789087
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step 2450, change in cost 0.392212
step 2460, training accuracy 0.790136
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step 2460, change in cost 0.393585
step 2470, training accuracy 0.790136
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step 2470, change in cost 0.395081
step 2480, training accuracy 0.791186
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step 2480, change in cost 0.396469
step 2490, training accuracy 0.791186
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step 2490, change in cost 0.397766
step 2500, training accuracy 0.791186
step 2500, cost 182.821
step 2500, change in cost 0.3992
step 2510, training accuracy 0.792235
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step 2510, change in cost 0.400528
step 2520, training accuracy 0.793284
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step 2520, change in cost 0.401855
step 2530, training accuracy 0.796432
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step 2530, change in cost 0.403107
step 2540, training accuracy 0.798531
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step 2540, change in cost 0.404449
step 2550, training accuracy 0.79958
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step 2550, change in cost 0.405731
step 2560, training accuracy 0.79958
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step 2560, change in cost 0.407043
step 2570, training accuracy 0.802728
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step 2570, change in cost 0.408142
step 2580, training accuracy 0.803778
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step 2580, change in cost 0.40947
step 2590, training accuracy 0.803778
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step 2590, change in cost 0.410614
step 2600, training accuracy 0.803778
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step 2600, change in cost 0.411804
step 2610, training accuracy 0.805876
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step 2610, change in cost 0.412949
step 2620, training accuracy 0.806925
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step 2620, change in cost 0.414169
step 2630, training accuracy 0.807975
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step 2630, change in cost 0.415192
step 2640, training accuracy 0.807975
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step 2640, change in cost 0.41629
step 2650, training accuracy 0.809024
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step 2650, change in cost 0.417419
step 2660, training accuracy 0.809024
step 2660, cost 176.264
step 2660, change in cost 0.418442
step 2670, training accuracy 0.809024
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step 2670, change in cost 0.419495
step 2680, training accuracy 0.809024
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step 2680, change in cost 0.420547
step 2690, training accuracy 0.811123
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step 2690, change in cost 0.421478
step 2700, training accuracy 0.812172
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step 2700, change in cost 0.422424
step 2710, training accuracy 0.816369
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step 2710, change in cost 0.423431
step 2720, training accuracy 0.819517
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step 2720, change in cost 0.424377
step 2730, training accuracy 0.821616
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step 2730, change in cost 0.425262
step 2740, training accuracy 0.822665
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step 2740, change in cost 0.426132
step 2750, training accuracy 0.822665
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step 2750, change in cost 0.427032
step 2760, training accuracy 0.821616
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step 2760, change in cost 0.427872
step 2770, training accuracy 0.823715
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step 2770, change in cost 0.42868
step 2780, training accuracy 0.824764
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step 2780, change in cost 0.42952
step 2790, training accuracy 0.826863
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step 2790, change in cost 0.430283
step 2800, training accuracy 0.826863
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step 2800, change in cost 0.430984
step 2810, training accuracy 0.826863
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step 2810, change in cost 0.431732
step 2820, training accuracy 0.827912
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step 2820, change in cost 0.432526
step 2830, training accuracy 0.827912
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step 2830, change in cost 0.433167
step 2840, training accuracy 0.828961
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step 2840, change in cost 0.433807
step 2850, training accuracy 0.83001
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step 2860, training accuracy 0.83106
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step 2870, training accuracy 0.83106
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step 2880, training accuracy 0.83106
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step 2880, change in cost 0.436249
step 2890, training accuracy 0.832109
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step 2900, training accuracy 0.833158
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step 2900, change in cost 0.437271
step 2910, training accuracy 0.833158
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step 2920, training accuracy 0.833158
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step 2920, change in cost 0.438385
step 2930, training accuracy 0.834208
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step 2940, training accuracy 0.834208
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step 2950, training accuracy 0.834208
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step 2950, change in cost 0.43956
step 2960, training accuracy 0.834208
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step 2970, training accuracy 0.837356
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step 2980, training accuracy 0.837356
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step 2980, change in cost 0.440781
step 2990, training accuracy 0.837356
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step 3000, training accuracy 0.837356
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step 3010, training accuracy 0.837356
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step 3020, training accuracy 0.839454
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step 3040, training accuracy 0.844701
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step 3040, change in cost 0.442368
step 3050, training accuracy 0.84575
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step 3060, training accuracy 0.8468
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step 3070, training accuracy 0.848898
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step 3090, training accuracy 0.850997
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step 3100, training accuracy 0.850997
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step 3110, training accuracy 0.850997
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step 3130, training accuracy 0.852046
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step 3140, training accuracy 0.855194
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step 3150, training accuracy 0.856243
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step 3180, training accuracy 0.858342
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step 3200, training accuracy 0.859391
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step 3210, training accuracy 0.859391
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step 3220, training accuracy 0.859391
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step 3230, training accuracy 0.858342
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step 3240, training accuracy 0.858342
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step 3250, training accuracy 0.859391
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step 3260, training accuracy 0.86149
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step 3270, training accuracy 0.862539
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step 3280, training accuracy 0.863589
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step 3290, training accuracy 0.863589
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step 3310, training accuracy 0.864638
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step 3320, training accuracy 0.864638
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step 3360, training accuracy 0.868835
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step 3370, training accuracy 0.868835
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step 3390, training accuracy 0.870934
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step 3400, training accuracy 0.870934
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step 3400, change in cost 0.435257
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step 3420, training accuracy 0.871983
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step 3430, training accuracy 0.871983
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step 3450, training accuracy 0.873033
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step 3500, training accuracy 0.879328
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step 3590, training accuracy 0.89297
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step 3600, training accuracy 0.89297
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step 3620, training accuracy 0.894019
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step 3630, training accuracy 0.894019
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step 3660, training accuracy 0.89297
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step 3690, training accuracy 0.896118
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step 3700, training accuracy 0.896118
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step 3720, training accuracy 0.896118
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step 3720, change in cost 0.409088
step 3730, training accuracy 0.896118
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step 3740, training accuracy 0.896118
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step 3740, change in cost 0.406998
step 3750, training accuracy 0.896118
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step 3750, change in cost 0.405884
step 3760, training accuracy 0.896118
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step 3760, change in cost 0.404724
step 3770, training accuracy 0.896118
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step 3770, change in cost 0.403763
step 3780, training accuracy 0.897167
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step 3780, change in cost 0.402649
step 3790, training accuracy 0.896118
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step 3790, change in cost 0.401535
step 3800, training accuracy 0.897167
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step 3800, change in cost 0.400352
step 3810, training accuracy 0.898216
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step 3810, change in cost 0.399254
step 3820, training accuracy 0.899265
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step 3820, change in cost 0.398148
step 3830, training accuracy 0.898216
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step 3830, change in cost 0.39695
step 3840, training accuracy 0.899265
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step 3840, change in cost 0.395798
step 3850, training accuracy 0.899265
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step 3850, change in cost 0.394661
step 3860, training accuracy 0.899265
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step 3860, change in cost 0.393478
step 3870, training accuracy 0.900315
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step 3870, change in cost 0.392265
step 3880, training accuracy 0.901364
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step 3880, change in cost 0.391083
step 3890, training accuracy 0.903463
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step 3890, change in cost 0.3899
step 3900, training accuracy 0.903463
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step 3900, change in cost 0.388664
step 3910, training accuracy 0.904512
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step 3910, change in cost 0.387489
step 3920, training accuracy 0.904512
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step 3920, change in cost 0.386246
step 3930, training accuracy 0.904512
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step 3930, change in cost 0.384987
step 3940, training accuracy 0.904512
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step 3940, change in cost 0.38382
step 3950, training accuracy 0.904512
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step 3950, change in cost 0.38253
step 3960, training accuracy 0.905561
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step 3960, change in cost 0.381256
step 3970, training accuracy 0.905561
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step 3970, change in cost 0.380051
step 3980, training accuracy 0.906611
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step 3980, change in cost 0.378777
step 3990, training accuracy 0.90766
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step 3990, change in cost 0.377525
step 4000, training accuracy 0.90766
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step 4000, change in cost 0.376167
step 4010, training accuracy 0.90766
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step 4010, change in cost 0.375008
step 4020, training accuracy 0.909759
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step 4020, change in cost 0.37365
step 4030, training accuracy 0.909759
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step 4030, change in cost 0.372345
step 4040, training accuracy 0.910808
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step 4040, change in cost 0.371101
step 4050, training accuracy 0.911857
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step 4050, change in cost 0.369766
step 4060, training accuracy 0.911857
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step 4060, change in cost 0.368523
step 4070, training accuracy 0.912907
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step 4070, change in cost 0.367111
step 4080, training accuracy 0.913956
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step 4080, change in cost 0.365891
step 4090, training accuracy 0.912907
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step 4090, change in cost 0.364555
step 4100, training accuracy 0.912907
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step 4100, change in cost 0.363197
step 4110, training accuracy 0.912907
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step 4120, training accuracy 0.912907
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step 4120, change in cost 0.36058
step 4130, training accuracy 0.912907
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step 4130, change in cost 0.35923
step 4140, training accuracy 0.912907
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step 4140, change in cost 0.357925
step 4150, training accuracy 0.912907
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step 4150, change in cost 0.356522
step 4160, training accuracy 0.912907
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step 4160, change in cost 0.35527
step 4170, training accuracy 0.912907
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step 4170, change in cost 0.353897
step 4180, training accuracy 0.913956
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step 4180, change in cost 0.35257
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step 4190, change in cost 0.351204
step 4200, training accuracy 0.911857
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step 4200, change in cost 0.349884
step 4210, training accuracy 0.911857
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step 4210, change in cost 0.348549
step 4220, training accuracy 0.912907
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step 4220, change in cost 0.347183
step 4230, training accuracy 0.912907
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step 4230, change in cost 0.345818
step 4240, training accuracy 0.912907
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step 4250, training accuracy 0.912907
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step 4250, change in cost 0.343132
step 4260, training accuracy 0.912907
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step 4260, change in cost 0.341805
step 4270, training accuracy 0.912907
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step 4270, change in cost 0.340385
step 4280, training accuracy 0.913956
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step 4290, training accuracy 0.913956
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step 4290, change in cost 0.337677
step 4300, training accuracy 0.913956
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step 4300, change in cost 0.336349
step 4310, training accuracy 0.913956
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step 4310, change in cost 0.334976
step 4320, training accuracy 0.913956
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step 4320, change in cost 0.333603
step 4330, training accuracy 0.913956
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step 4330, change in cost 0.332321
step 4340, training accuracy 0.913956
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step 4340, change in cost 0.330887
step 4350, training accuracy 0.913956
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step 4350, change in cost 0.329567
step 4360, training accuracy 0.913956
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step 4360, change in cost 0.328247
step 4370, training accuracy 0.913956
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step 4370, change in cost 0.32682
step 4380, training accuracy 0.913956
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step 4380, change in cost 0.325508
step 4390, training accuracy 0.915005
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step 4390, change in cost 0.324112
step 4400, training accuracy 0.915005
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step 4400, change in cost 0.322792
step 4410, training accuracy 0.915005
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step 4410, change in cost 0.321434
step 4420, training accuracy 0.916055
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step 4420, change in cost 0.320099
step 4430, training accuracy 0.916055
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step 4430, change in cost 0.318733
step 4440, training accuracy 0.916055
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step 4440, change in cost 0.317413
step 4450, training accuracy 0.915005
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step 4450, change in cost 0.31601
step 4460, training accuracy 0.915005
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step 4460, change in cost 0.314674
step 4470, training accuracy 0.916055
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step 4470, change in cost 0.31337
step 4480, training accuracy 0.917104
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step 4480, change in cost 0.311981
step 4490, training accuracy 0.917104
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step 4490, change in cost 0.310699
step 4500, training accuracy 0.918153
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step 4500, change in cost 0.309334
step 4510, training accuracy 0.918153
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step 4510, change in cost 0.307983
step 4520, training accuracy 0.917104
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step 4520, change in cost 0.306679
step 4530, training accuracy 0.918153
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step 4530, change in cost 0.305328
step 4540, training accuracy 0.918153
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step 4540, change in cost 0.304024
step 4550, training accuracy 0.918153
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step 4550, change in cost 0.302689
step 4560, training accuracy 0.918153
step 4560, cost 100.443
step 4560, change in cost 0.301353
step 4570, training accuracy 0.918153
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step 4570, change in cost 0.300034
step 4580, training accuracy 0.918153
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step 4580, change in cost 0.298714
step 4590, training accuracy 0.919203
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step 4590, change in cost 0.297424
step 4600, training accuracy 0.919203
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step 4600, change in cost 0.296112
step 4610, training accuracy 0.919203
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step 4610, change in cost 0.2948
step 4620, training accuracy 0.919203
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step 4620, change in cost 0.29348
step 4630, training accuracy 0.919203
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step 4630, change in cost 0.292191
step 4640, training accuracy 0.919203
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step 4640, change in cost 0.290863
step 4650, training accuracy 0.919203
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step 4660, training accuracy 0.919203
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step 4660, change in cost 0.288269
step 4670, training accuracy 0.919203
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step 4670, change in cost 0.287064
step 4680, training accuracy 0.919203
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step 4680, change in cost 0.28569
step 4690, training accuracy 0.919203
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step 4700, training accuracy 0.919203
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step 4700, change in cost 0.283112
step 4710, training accuracy 0.919203
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step 4710, change in cost 0.281906
step 4720, training accuracy 0.920252
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step 4720, change in cost 0.280632
step 4730, training accuracy 0.920252
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step 4730, change in cost 0.27935
step 4740, training accuracy 0.920252
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step 4740, change in cost 0.278076
step 4750, training accuracy 0.921301
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step 4750, change in cost 0.276817
step 4760, training accuracy 0.921301
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step 4760, change in cost 0.275635
step 4770, training accuracy 0.921301
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step 4770, change in cost 0.274277
step 4780, training accuracy 0.92235
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step 4780, change in cost 0.273048
step 4790, training accuracy 0.92235
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step 4790, change in cost 0.271881
step 4800, training accuracy 0.92235
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step 4800, change in cost 0.270599
step 4810, training accuracy 0.92235
step 4810, cost 93.3301
step 4810, change in cost 0.269394
step 4820, training accuracy 0.92235
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step 4820, change in cost 0.268143
step 4830, training accuracy 0.9234
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step 4830, change in cost 0.266899
step 4840, training accuracy 0.9234
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step 4840, change in cost 0.265656
step 4850, training accuracy 0.9234
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step 4850, change in cost 0.264542
step 4860, training accuracy 0.9234
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step 4860, change in cost 0.263268
step 4870, training accuracy 0.9234
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step 4870, change in cost 0.262047
step 4880, training accuracy 0.9234
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step 4880, change in cost 0.26088
step 4890, training accuracy 0.9234
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step 4890, change in cost 0.259605
step 4900, training accuracy 0.9234
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step 4900, change in cost 0.258484
step 4910, training accuracy 0.9234
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step 4910, change in cost 0.257286
step 4920, training accuracy 0.9234
step 4920, cost 90.4472
step 4920, change in cost 0.256081
step 4930, training accuracy 0.9234
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step 4930, change in cost 0.25489
step 4940, training accuracy 0.9234
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step 4940, change in cost 0.253799
step 4950, training accuracy 0.924449
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step 4950, change in cost 0.252563
step 4960, training accuracy 0.924449
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step 4960, change in cost 0.251389
step 4970, training accuracy 0.924449
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step 4970, change in cost 0.250244
step 4980, training accuracy 0.924449
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step 4980, change in cost 0.2491
step 4990, training accuracy 0.924449
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step 5000, training accuracy 0.924449
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step 5000, change in cost 0.246788
step 5010, training accuracy 0.924449
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step 5010, change in cost 0.245667
step 5020, training accuracy 0.925498
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step 5020, change in cost 0.24453
step 5030, training accuracy 0.925498
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step 5030, change in cost 0.24337
step 5040, training accuracy 0.925498
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step 5040, change in cost 0.242279
step 5050, training accuracy 0.925498
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step 5050, change in cost 0.241127
step 5060, training accuracy 0.925498
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step 5060, change in cost 0.240028
step 5070, training accuracy 0.925498
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step 5080, training accuracy 0.927597
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step 5090, training accuracy 0.927597
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step 5100, training accuracy 0.927597
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step 5110, training accuracy 0.927597
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step 5130, training accuracy 0.927597
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step 5140, training accuracy 0.927597
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step 5150, training accuracy 0.927597
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step 5160, training accuracy 0.927597
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step 5160, change in cost 0.229141
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step 5180, training accuracy 0.929696
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step 5200, training accuracy 0.929696
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step 5210, training accuracy 0.929696
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step 5220, training accuracy 0.930745
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step 5230, training accuracy 0.930745
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step 5240, training accuracy 0.930745
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step 5250, training accuracy 0.930745
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step 5260, training accuracy 0.930745
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step 5270, training accuracy 0.930745
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step 5280, training accuracy 0.931794
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step 5290, training accuracy 0.931794
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step 5300, training accuracy 0.931794
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step 5320, training accuracy 0.932844
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step 5330, training accuracy 0.932844
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step 5340, training accuracy 0.932844
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step 5350, training accuracy 0.933893
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step 5360, training accuracy 0.933893
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step 5370, training accuracy 0.933893
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step 5380, training accuracy 0.934942
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step 5420, training accuracy 0.93809
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step 5430, training accuracy 0.93809
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step 5440, training accuracy 0.93809
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step 5490, training accuracy 0.93914
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step 5500, training accuracy 0.93914
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step 5620, training accuracy 0.940189
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step 5630, training accuracy 0.940189
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step 5640, training accuracy 0.940189
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step 5660, training accuracy 0.940189
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step 5800, training accuracy 0.942288
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step 5820, training accuracy 0.942288
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step 5860, training accuracy 0.943337
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step 5900, training accuracy 0.943337
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step 5900, change in cost 0.163963
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step 5940, training accuracy 0.944386
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step 5950, training accuracy 0.944386
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step 25510, change in cost 0.0049305
step 25520, training accuracy 0.995803
step 25520, cost 10.3042
step 25520, change in cost 0.00492859
step 25530, training accuracy 0.995803
step 25530, cost 10.2993
step 25530, change in cost 0.00492287
step 25540, training accuracy 0.995803
step 25540, cost 10.2944
step 25540, change in cost 0.00491714
step 25550, training accuracy 0.995803
step 25550, cost 10.2895
step 25550, change in cost 0.00491142
step 25560, training accuracy 0.995803
step 25560, cost 10.2846
step 25560, change in cost 0.0049057
step 25570, training accuracy 0.995803
step 25570, cost 10.2797
step 25570, change in cost 0.00489902
step 25580, training accuracy 0.995803
step 25580, cost 10.2748
step 25580, change in cost 0.00490093
step 25590, training accuracy 0.995803
step 25590, cost 10.2699
step 25590, change in cost 0.0048933
step 25600, training accuracy 0.995803
step 25600, cost 10.265
step 25600, change in cost 0.00488949
step 25610, training accuracy 0.995803
step 25610, cost 10.2601
step 25610, change in cost 0.00488567
step 25620, training accuracy 0.995803
step 25620, cost 10.2552
step 25620, change in cost 0.004879
step 25630, training accuracy 0.995803
step 25630, cost 10.2503
step 25630, change in cost 0.00488091
step 25640, training accuracy 0.995803
step 25640, cost 10.2455
step 25640, change in cost 0.004879
step 25650, training accuracy 0.995803
step 25650, cost 10.2406
step 25650, change in cost 0.00486946
step 25660, training accuracy 0.995803
step 25660, cost 10.2357
step 25660, change in cost 0.00486469
step 25670, training accuracy 0.995803
step 25670, cost 10.2309
step 25670, change in cost 0.00485611
step 25680, training accuracy 0.995803
step 25680, cost 10.226
step 25680, change in cost 0.0048542
step 25690, training accuracy 0.995803
step 25690, cost 10.2212
step 25690, change in cost 0.00484943
step 25700, training accuracy 0.995803
step 25700, cost 10.2163
step 25700, change in cost 0.00484276
step 25710, training accuracy 0.995803
step 25710, cost 10.2115
step 25710, change in cost 0.00483799
step 25720, training accuracy 0.995803
step 25720, cost 10.2067
step 25720, change in cost 0.0048275
step 25730, training accuracy 0.995803
step 25730, cost 10.2018
step 25730, change in cost 0.0048275
step 25740, training accuracy 0.995803
step 25740, cost 10.197
step 25740, change in cost 0.00482655
step 25750, training accuracy 0.995803
step 25750, cost 10.1922
step 25750, change in cost 0.00481892
step 25760, training accuracy 0.995803
step 25760, cost 10.1874
step 25760, change in cost 0.00481319
step 25770, training accuracy 0.995803
step 25770, cost 10.1826
step 25770, change in cost 0.00481415
step 25780, training accuracy 0.995803
step 25780, cost 10.1778
step 25780, change in cost 0.00480175
step 25790, training accuracy 0.995803
step 25790, cost 10.173
step 25790, change in cost 0.00480652
step 25800, training accuracy 0.995803
step 25800, cost 10.1682
step 25800, change in cost 0.00479889
step 25810, training accuracy 0.995803
step 25810, cost 10.1634
step 25810, change in cost 0.00479507
step 25820, training accuracy 0.995803
step 25820, cost 10.1586
step 25820, change in cost 0.00479412
step 25830, training accuracy 0.995803
step 25830, cost 10.1538
step 25830, change in cost 0.00478935
step 25840, training accuracy 0.995803
step 25840, cost 10.149
step 25840, change in cost 0.00477505
step 25850, training accuracy 0.995803
step 25850, cost 10.1442
step 25850, change in cost 0.00477791
step 25860, training accuracy 0.995803
step 25860, cost 10.1395
step 25860, change in cost 0.00476742
step 25870, training accuracy 0.995803
step 25870, cost 10.1347
step 25870, change in cost 0.00476933
step 25880, training accuracy 0.995803
step 25880, cost 10.1299
step 25880, change in cost 0.00476074
step 25890, training accuracy 0.995803
step 25890, cost 10.1252
step 25890, change in cost 0.00475693
step 25900, training accuracy 0.995803
step 25900, cost 10.1204
step 25900, change in cost 0.00474453
step 25910, training accuracy 0.995803
step 25910, cost 10.1157
step 25910, change in cost 0.00474739
step 25920, training accuracy 0.995803
step 25920, cost 10.1109
step 25920, change in cost 0.00474072
step 25930, training accuracy 0.995803
step 25930, cost 10.1062
step 25930, change in cost 0.00474167
step 25940, training accuracy 0.995803
step 25940, cost 10.1015
step 25940, change in cost 0.00473499
step 25950, training accuracy 0.995803
step 25950, cost 10.0967
step 25950, change in cost 0.00473213
step 25960, training accuracy 0.995803
step 25960, cost 10.092
step 25960, change in cost 0.00473022
step 25970, training accuracy 0.995803
step 25970, cost 10.0873
step 25970, change in cost 0.00471878
step 25980, training accuracy 0.995803
step 25980, cost 10.0826
step 25980, change in cost 0.00472355
step 25990, training accuracy 0.995803
step 25990, cost 10.0778
step 25990, change in cost 0.00471306
step 26000, training accuracy 0.995803
step 26000, cost 10.0731
step 26000, change in cost 0.00471592
step 26010, training accuracy 0.995803
step 26010, cost 10.0684
step 26010, change in cost 0.00470352
step 26020, training accuracy 0.995803
step 26020, cost 10.0637
step 26020, change in cost 0.00469685
step 26030, training accuracy 0.995803
step 26030, cost 10.059
step 26030, change in cost 0.00469208
step 26040, training accuracy 0.995803
step 26040, cost 10.0543
step 26040, change in cost 0.00469112
step 26050, training accuracy 0.995803
step 26050, cost 10.0497
step 26050, change in cost 0.00468636
step 26060, training accuracy 0.995803
step 26060, cost 10.045
step 26060, change in cost 0.00467873
step 26070, training accuracy 0.995803
step 26070, cost 10.0403
step 26070, change in cost 0.004673
step 26080, training accuracy 0.995803
step 26080, cost 10.0356
step 26080, change in cost 0.00467205
step 26090, training accuracy 0.995803
step 26090, cost 10.031
step 26090, change in cost 0.00466824
step 26100, training accuracy 0.995803
step 26100, cost 10.0263
step 26100, change in cost 0.00465965
step 26110, training accuracy 0.995803
step 26110, cost 10.0216
step 26110, change in cost 0.00466251
step 26120, training accuracy 0.995803
step 26120, cost 10.017
step 26120, change in cost 0.00465488
step 26130, training accuracy 0.995803
step 26130, cost 10.0123
step 26130, change in cost 0.00465298
step 26140, training accuracy 0.995803
step 26140, cost 10.0077
step 26140, change in cost 0.0046463
step 26150, training accuracy 0.995803
step 26150, cost 10.003
step 26150, change in cost 0.00464153
step 26160, training accuracy 0.995803
step 26160, cost 9.9984
step 26160, change in cost 0.00464153
step 26170, training accuracy 0.995803
step 26170, cost 9.99376
step 26170, change in cost 0.00463867
step 26180, training accuracy 0.995803
step 26180, cost 9.98913
step 26180, change in cost 0.004632
step 26190, training accuracy 0.995803
step 26190, cost 9.98451
step 26190, change in cost 0.00462246
step 26200, training accuracy 0.995803
step 26200, cost 9.97989
step 26200, change in cost 0.00461674
step 26210, training accuracy 0.995803
step 26210, cost 9.97528
step 26210, change in cost 0.00461674
step 26220, training accuracy 0.995803
step 26220, cost 9.97066
step 26220, change in cost 0.00461483
step 26230, training accuracy 0.995803
step 26230, cost 9.96605
step 26230, change in cost 0.00460911
step 26240, training accuracy 0.995803
step 26240, cost 9.96145
step 26240, change in cost 0.00460052
step 26250, training accuracy 0.995803
step 26250, cost 9.95685
step 26250, change in cost 0.00459766
step 26260, training accuracy 0.995803
step 26260, cost 9.95226
step 26260, change in cost 0.00459099
step 26270, training accuracy 0.995803
step 26270, cost 9.94767
step 26270, change in cost 0.0045929
step 26280, training accuracy 0.995803
step 26280, cost 9.94309
step 26280, change in cost 0.00458336
step 26290, training accuracy 0.995803
step 26290, cost 9.9385
step 26290, change in cost 0.00458336
step 26300, training accuracy 0.995803
step 26300, cost 9.93393
step 26300, change in cost 0.00457573
step 26310, training accuracy 0.995803
step 26310, cost 9.92935
step 26310, change in cost 0.00457287
step 26320, training accuracy 0.995803
step 26320, cost 9.92478
step 26320, change in cost 0.00457191
step 26330, training accuracy 0.995803
step 26330, cost 9.92021
step 26330, change in cost 0.00456905
step 26340, training accuracy 0.995803
step 26340, cost 9.91565
step 26340, change in cost 0.00456333
step 26350, training accuracy 0.995803
step 26350, cost 9.91109
step 26350, change in cost 0.00456142
step 26360, training accuracy 0.995803
step 26360, cost 9.90653
step 26360, change in cost 0.0045557
step 26370, training accuracy 0.995803
step 26370, cost 9.90199
step 26370, change in cost 0.00454235
step 26380, training accuracy 0.995803
step 26380, cost 9.89744
step 26380, change in cost 0.00454617
step 26390, training accuracy 0.995803
step 26390, cost 9.89291
step 26390, change in cost 0.00453758
step 26400, training accuracy 0.995803
step 26400, cost 9.88837
step 26400, change in cost 0.00453568
step 26410, training accuracy 0.995803
step 26410, cost 9.88384
step 26410, change in cost 0.00453568
step 26420, training accuracy 0.995803
step 26420, cost 9.87931
step 26420, change in cost 0.00452328
step 26430, training accuracy 0.995803
step 26430, cost 9.87479
step 26430, change in cost 0.00452042
step 26440, training accuracy 0.995803
step 26440, cost 9.87028
step 26440, change in cost 0.00451565
step 26450, training accuracy 0.995803
step 26450, cost 9.86576
step 26450, change in cost 0.00451374
step 26460, training accuracy 0.995803
step 26460, cost 9.86126
step 26460, change in cost 0.00450706
step 26470, training accuracy 0.995803
step 26470, cost 9.85675
step 26470, change in cost 0.0045042
step 26480, training accuracy 0.995803
step 26480, cost 9.85225
step 26480, change in cost 0.00450134
step 26490, training accuracy 0.995803
step 26490, cost 9.84776
step 26490, change in cost 0.00449371
step 26500, training accuracy 0.995803
step 26500, cost 9.84326
step 26500, change in cost 0.00449657
step 26510, training accuracy 0.995803
step 26510, cost 9.83877
step 26510, change in cost 0.00449085
step 26520, training accuracy 0.995803
step 26520, cost 9.83428
step 26520, change in cost 0.00448513
step 26530, training accuracy 0.995803
step 26530, cost 9.8298
step 26530, change in cost 0.00448418
step 26540, training accuracy 0.995803
step 26540, cost 9.82532
step 26540, change in cost 0.00447655
step 26550, training accuracy 0.995803
step 26550, cost 9.82085
step 26550, change in cost 0.00447559
step 26560, training accuracy 0.995803
step 26560, cost 9.81638
step 26560, change in cost 0.00446606
step 26570, training accuracy 0.995803
step 26570, cost 9.81192
step 26570, change in cost 0.0044632
step 26580, training accuracy 0.995803
step 26580, cost 9.80746
step 26580, change in cost 0.00446224
step 26590, training accuracy 0.995803
step 26590, cost 9.80299
step 26590, change in cost 0.00446129
step 26600, training accuracy 0.995803
step 26600, cost 9.79854
step 26600, change in cost 0.00445461
step 26610, training accuracy 0.995803
step 26610, cost 9.7941
step 26610, change in cost 0.00444508
step 26620, training accuracy 0.995803
step 26620, cost 9.78966
step 26620, change in cost 0.00443745
step 26630, training accuracy 0.995803
step 26630, cost 9.78522
step 26630, change in cost 0.00443935
step 26640, training accuracy 0.995803
step 26640, cost 9.78078
step 26640, change in cost 0.00443554
step 26650, training accuracy 0.995803
step 26650, cost 9.77635
step 26650, change in cost 0.00442886
step 26660, training accuracy 0.995803
step 26660, cost 9.77193
step 26660, change in cost 0.00442791
step 26670, training accuracy 0.995803
step 26670, cost 9.7675
step 26670, change in cost 0.00442219
step 26680, training accuracy 0.995803
step 26680, cost 9.76308
step 26680, change in cost 0.00442791
step 26690, training accuracy 0.995803
step 26690, cost 9.75866
step 26690, change in cost 0.00441647
step 26700, training accuracy 0.995803
step 26700, cost 9.75425
step 26700, change in cost 0.0044136
step 26710, training accuracy 0.995803
step 26710, cost 9.74984
step 26710, change in cost 0.00440979
step 26720, training accuracy 0.995803
step 26720, cost 9.74543
step 26720, change in cost 0.00440598
step 26730, training accuracy 0.995803
step 26730, cost 9.74103
step 26730, change in cost 0.0043993
step 26740, training accuracy 0.995803
step 26740, cost 9.73664
step 26740, change in cost 0.00439453
step 26750, training accuracy 0.995803
step 26750, cost 9.73225
step 26750, change in cost 0.00438976
step 26760, training accuracy 0.995803
step 26760, cost 9.72786
step 26760, change in cost 0.00439072
step 26770, training accuracy 0.995803
step 26770, cost 9.72347
step 26770, change in cost 0.00438404
step 26780, training accuracy 0.995803
step 26780, cost 9.71909
step 26780, change in cost 0.00438499
step 26790, training accuracy 0.995803
step 26790, cost 9.71472
step 26790, change in cost 0.00437164
step 26800, training accuracy 0.995803
step 26800, cost 9.71034
step 26800, change in cost 0.00437164
step 26810, training accuracy 0.995803
step 26810, cost 9.70598
step 26810, change in cost 0.00436687
step 26820, training accuracy 0.995803
step 26820, cost 9.70162
step 26820, change in cost 0.00435638
step 26830, training accuracy 0.995803
step 26830, cost 9.69726
step 26830, change in cost 0.00436306
step 26840, training accuracy 0.995803
step 26840, cost 9.69291
step 26840, change in cost 0.00435066
step 26850, training accuracy 0.995803
step 26850, cost 9.68855
step 26850, change in cost 0.00435352
step 26860, training accuracy 0.995803
step 26860, cost 9.68421
step 26860, change in cost 0.00434685
step 26870, training accuracy 0.995803
step 26870, cost 9.67986
step 26870, change in cost 0.00434685
step 26880, training accuracy 0.995803
step 26880, cost 9.67552
step 26880, change in cost 0.00433826
step 26890, training accuracy 0.995803
step 26890, cost 9.67118
step 26890, change in cost 0.00433922
step 26900, training accuracy 0.995803
step 26900, cost 9.66685
step 26900, change in cost 0.00433254
step 26910, training accuracy 0.995803
step 26910, cost 9.66252
step 26910, change in cost 0.00432777
step 26920, training accuracy 0.995803
step 26920, cost 9.6582
step 26920, change in cost 0.0043211
step 26930, training accuracy 0.995803
step 26930, cost 9.65388
step 26930, change in cost 0.00432205
step 26940, training accuracy 0.995803
step 26940, cost 9.64957
step 26940, change in cost 0.00431156
step 26950, training accuracy 0.995803
step 26950, cost 9.64525
step 26950, change in cost 0.00431347
step 26960, training accuracy 0.995803
step 26960, cost 9.64095
step 26960, change in cost 0.00430775
step 26970, training accuracy 0.995803
step 26970, cost 9.63664
step 26970, change in cost 0.00430679
step 26980, training accuracy 0.995803
step 26980, cost 9.63234
step 26980, change in cost 0.0042963
step 26990, training accuracy 0.995803
step 26990, cost 9.62805
step 26990, change in cost 0.00429535
final accuracy on test set: 0.971429

In [39]:

    

# Turn on interactive plotting
plt.ion()
# Create the main, super plot
fig = plt.figure()
# Create two subplots on their own axes and give titles
ax1 = plt.subplot("211")
ax1.set_title("TRAINING ACCURACY", fontsize=18)
ax2 = plt.subplot("212")
ax2.set_title("TRAINING COST", fontsize=18)
plt.tight_layout()

# Plot progress to our two subplots
accuracyLine, = ax1.plot(epoch_values, accuracy_values)
costLine, = ax2.plot(epoch_values, cost_values)
fig.canvas.draw()


    

In [ ]:

    




    

In [ ]: