In [64]:

    

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 [65]:

    

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()


    

    




loading training data
loading test data

In [66]:

    

# 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 = 8
# 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 [67]:

    

# 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])


    

In [68]:

    

# 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
Xnn = 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
yGoldnn = tf.placeholder(tf.float32, [None, numLabels])


    

In [69]:

    

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

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

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


    

In [70]:

    

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

weights_1_2nn = tf.Variable(tf.random_normal([numFeatures,numHidden],
                                       mean=0,
                                       stddev=(np.sqrt(6/numFeatures+
                                                         numHidden+1)),
                                       name="weights_1_2"))

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

weights_2_3nn = tf.Variable(tf.random_normal([numHidden,numLabels],
                                       mean=0,
                                       stddev=(np.sqrt(6/numHidden+
                                                         numLabels+1)),
                                       name="weights_2_3"))

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


    

In [71]:

    

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

# PREDICTION ALGORITHM i.e. FEEDFORWARD ALGORITHM
apply_weights_OP = tf.matmul(X, weights, name="apply_weights")
add_bias_OP = tf.add(apply_weights_OP, bias, name="add_bias") 
activation_OP = tf.nn.sigmoid(add_bias_OP, name="activation")


    

In [72]:

    

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

# Neural Network

apply_weights_1nn = tf.matmul(Xnn, weights_1_2nn, name="apply_weightsnn")
add_bias_1nn = tf.add(apply_weights_1nn, bias_hiddennn, name="add_biasnn") 
activation_1nn = tf.nn.sigmoid(add_bias_1nn, name="activationnn")

apply_weights_OPnn = tf.matmul(activation_1nn, weights_2_3nn, name="apply_weights2nn")
add_bias_OPnn = tf.add(apply_weights_OPnn, bias_outnn, name="add_bias2nn") 
activation_OPnn = tf.nn.sigmoid(add_bias_OPnn, name="activation2nn")


    

In [73]:

    

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


    

In [74]:

    

cost_OPnn = tf.nn.l2_loss(activation_OPnn-yGoldnn, name="squared_error_costnn")


    

In [75]:

    

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


    

In [76]:

    

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


    

In [77]:

    

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


    

In [78]:

    

epoch_valuesnn=[]
accuracy_valuesnn=[]
cost_valuesnn=[]


    

In [79]:

    

# 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"))
# Summary op for regression output
activation_summary_OP = tf.histogram_summary("output", activation_OP)
# Summary op for accuracy
accuracy_summary_OP = tf.scalar_summary("accuracy", accuracy_OP)
# Summary op for cost
cost_summary_OP = tf.scalar_summary("cost", cost_OP)
# Summary ops to check how variables (W, b) are updating after each iteration
weightSummary = tf.histogram_summary("weights", weights.eval(session=sess))
biasSummary = tf.histogram_summary("biases", bias.eval(session=sess))
# Merge all summaries
all_summary_OPS = tf.merge_all_summaries()
# Summary writer
writer = tf.train.SummaryWriter("summary_logs", sess.graph_def)

# 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})))


    

    




WARNING:tensorflow:Passing a `GraphDef` to the SummaryWriter is deprecated. Pass a `Graph` object instead, such as `sess.graph`.






    




step 0, training accuracy 0.469045
step 0, cost 257.529
step 0, change in cost 257.529
step 10, training accuracy 0.528856
step 10, cost 244.603
step 10, change in cost 12.9261
step 20, training accuracy 0.56978
step 20, cost 238.027
step 20, change in cost 6.57657
step 30, training accuracy 0.567681
step 30, cost 235.182
step 30, change in cost 2.84454
step 40, training accuracy 0.571878
step 40, cost 234.008
step 40, change in cost 1.17383
step 50, training accuracy 0.575026
step 50, cost 233.506
step 50, change in cost 0.502197
step 60, training accuracy 0.582371
step 60, cost 233.265
step 60, change in cost 0.241333
step 70, training accuracy 0.582371
step 70, cost 233.124
step 70, change in cost 0.14093
step 80, training accuracy 0.582371
step 80, cost 233.022
step 80, change in cost 0.102066
step 90, training accuracy 0.579224
step 90, cost 232.934
step 90, change in cost 0.0872955
step 100, training accuracy 0.580273
step 100, cost 232.853
step 100, change in cost 0.0814209
step 110, training accuracy 0.580273
step 110, cost 232.774
step 110, change in cost 0.079071
step 120, training accuracy 0.579224
step 120, cost 232.696
step 120, change in cost 0.0782471
step 130, training accuracy 0.579224
step 130, cost 232.618
step 130, change in cost 0.0777588
step 140, training accuracy 0.579224
step 140, cost 232.54
step 140, change in cost 0.0777435
step 150, training accuracy 0.580273
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step 150, change in cost 0.0774841
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step 160, change in cost 0.0775604
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step 170, change in cost 0.0774536
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step 180, change in cost 0.0773926
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step 190, change in cost 0.0774231
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step 260, change in cost 0.0771027
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step 270, change in cost 0.0770721
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step 280, change in cost 0.0769806
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step 290, change in cost 0.0770569
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step 790, change in cost 0.0749664
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step 800, cost 227.509
step 800, change in cost 0.0749054
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step 810, change in cost 0.0748444
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step 820, change in cost 0.0748901
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step 850, change in cost 0.0747528
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step 860, change in cost 0.0746765
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step 870, change in cost 0.0745697
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step 880, change in cost 0.0746155
step 890, training accuracy 0.61595
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step 890, change in cost 0.0745697
step 900, training accuracy 0.61595
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step 900, change in cost 0.0745697
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step 910, change in cost 0.0744629
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step 920, change in cost 0.0743866
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step 930, change in cost 0.0744324
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step 940, change in cost 0.0744019
step 950, training accuracy 0.620147
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step 950, change in cost 0.0742493
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step 960, change in cost 0.0742645
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step 970, change in cost 0.0742493
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step 980, change in cost 0.0742188
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step 990, change in cost 0.0740509
step 1000, training accuracy 0.623295
step 1000, cost 226.019
step 1000, change in cost 0.0742798
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step 1010, change in cost 0.0740051
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step 1020, change in cost 0.0739746
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step 1030, change in cost 0.0739594
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step 1050, change in cost 0.0739136
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step 1070, change in cost 0.0738831
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step 1080, change in cost 0.0738068
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step 1090, change in cost 0.073761
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step 1100, cost 225.28
step 1100, change in cost 0.0736084
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step 1110, change in cost 0.0737152
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step 1120, change in cost 0.0736542
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step 1130, change in cost 0.0735016
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step 1140, change in cost 0.0736084
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step 1150, change in cost 0.07341
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step 1160, change in cost 0.0735474
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step 1180, change in cost 0.0733795
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step 1190, change in cost 0.0733337
step 1200, training accuracy 0.632739
step 1200, cost 224.545
step 1200, change in cost 0.0733032
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step 1210, cost 224.472
step 1210, change in cost 0.0732422
step 1220, training accuracy 0.633788
step 1220, cost 224.399
step 1220, change in cost 0.0732422
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step 1230, cost 224.325
step 1230, change in cost 0.0731964
step 1240, training accuracy 0.635887
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step 1240, change in cost 0.0731201
step 1250, training accuracy 0.636936
step 1250, cost 224.179
step 1250, change in cost 0.0730591
step 1260, training accuracy 0.639035
step 1260, cost 224.106
step 1260, change in cost 0.0730743
step 1270, training accuracy 0.640084
step 1270, cost 224.033
step 1270, change in cost 0.0729675
step 1280, training accuracy 0.640084
step 1280, cost 223.96
step 1280, change in cost 0.0730743
step 1290, training accuracy 0.641133
step 1290, cost 223.887
step 1290, change in cost 0.072876
step 1300, training accuracy 0.642183
step 1300, cost 223.814
step 1300, change in cost 0.0729675
step 1310, training accuracy 0.643232
step 1310, cost 223.741
step 1310, change in cost 0.0728455
step 1320, training accuracy 0.644281
step 1320, cost 223.669
step 1320, change in cost 0.0727081
step 1330, training accuracy 0.644281
step 1330, cost 223.596
step 1330, change in cost 0.0728302
step 1340, training accuracy 0.644281
step 1340, cost 223.523
step 1340, change in cost 0.0727081
step 1350, training accuracy 0.644281
step 1350, cost 223.45
step 1350, change in cost 0.0726929
step 1360, training accuracy 0.645331
step 1360, cost 223.378
step 1360, change in cost 0.0726013
step 1370, training accuracy 0.645331
step 1370, cost 223.305
step 1370, change in cost 0.0726929
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step 1380, cost 223.233
step 1380, change in cost 0.0725861
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step 1390, cost 223.16
step 1390, change in cost 0.0724945
step 1400, training accuracy 0.647429
step 1400, cost 223.088
step 1400, change in cost 0.0724945
step 1410, training accuracy 0.648479
step 1410, cost 223.015
step 1410, change in cost 0.0724182
step 1420, training accuracy 0.647429
step 1420, cost 222.943
step 1420, change in cost 0.0723572
step 1430, training accuracy 0.647429
step 1430, cost 222.87
step 1430, change in cost 0.0723572
step 1440, training accuracy 0.647429
step 1440, cost 222.798
step 1440, change in cost 0.0723267
step 1450, training accuracy 0.647429
step 1450, cost 222.726
step 1450, change in cost 0.0723114
step 1460, training accuracy 0.648479
step 1460, cost 222.654
step 1460, change in cost 0.0721741
step 1470, training accuracy 0.648479
step 1470, cost 222.581
step 1470, change in cost 0.0722351
step 1480, training accuracy 0.648479
step 1480, cost 222.509
step 1480, change in cost 0.0722504
step 1490, training accuracy 0.649528
step 1490, cost 222.437
step 1490, change in cost 0.0720215
step 1500, training accuracy 0.649528
step 1500, cost 222.365
step 1500, change in cost 0.0721436
step 1510, training accuracy 0.651626
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step 1510, change in cost 0.0720215
step 1520, training accuracy 0.651626
step 1520, cost 222.221
step 1520, change in cost 0.0719757
step 1530, training accuracy 0.651626
step 1530, cost 222.149
step 1530, change in cost 0.071991
step 1540, training accuracy 0.652676
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step 1540, change in cost 0.0718689
step 1550, training accuracy 0.652676
step 1550, cost 222.005
step 1550, change in cost 0.0719147
step 1560, training accuracy 0.653725
step 1560, cost 221.933
step 1560, change in cost 0.0718536
step 1570, training accuracy 0.654774
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step 1570, change in cost 0.0717773
step 1580, training accuracy 0.654774
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step 1580, change in cost 0.0717316
step 1590, training accuracy 0.655824
step 1590, cost 221.718
step 1590, change in cost 0.071701
step 1600, training accuracy 0.656873
step 1600, cost 221.646
step 1600, change in cost 0.0716705
step 1610, training accuracy 0.660021
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step 1610, change in cost 0.0716705
step 1620, training accuracy 0.660021
step 1620, cost 221.503
step 1620, change in cost 0.0715637
step 1630, training accuracy 0.660021
step 1630, cost 221.432
step 1630, change in cost 0.071579
step 1640, training accuracy 0.660021
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step 1640, change in cost 0.0714722
step 1650, training accuracy 0.66107
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step 1650, change in cost 0.0715332
step 1660, training accuracy 0.66107
step 1660, cost 221.217
step 1660, change in cost 0.0714264
step 1670, training accuracy 0.66107
step 1670, cost 221.146
step 1670, change in cost 0.0713501
step 1680, training accuracy 0.66107
step 1680, cost 221.074
step 1680, change in cost 0.0713959
step 1690, training accuracy 0.66212
step 1690, cost 221.003
step 1690, change in cost 0.071228
step 1700, training accuracy 0.66212
step 1700, cost 220.932
step 1700, change in cost 0.0712738
step 1710, training accuracy 0.663169
step 1710, cost 220.861
step 1710, change in cost 0.0712433
step 1720, training accuracy 0.663169
step 1720, cost 220.789
step 1720, change in cost 0.0712585
step 1730, training accuracy 0.665268
step 1730, cost 220.718
step 1730, change in cost 0.0710602
step 1740, training accuracy 0.666317
step 1740, cost 220.647
step 1740, change in cost 0.071167
step 1750, training accuracy 0.666317
step 1750, cost 220.576
step 1750, change in cost 0.0709381
step 1760, training accuracy 0.666317
step 1760, cost 220.505
step 1760, change in cost 0.0710602
step 1770, training accuracy 0.666317
step 1770, cost 220.434
step 1770, change in cost 0.0709229
step 1780, training accuracy 0.666317
step 1780, cost 220.363
step 1780, change in cost 0.0709686
step 1790, training accuracy 0.667366
step 1790, cost 220.292
step 1790, change in cost 0.0709534
step 1800, training accuracy 0.668416
step 1800, cost 220.222
step 1800, change in cost 0.0707855
step 1810, training accuracy 0.669465
step 1810, cost 220.151
step 1810, change in cost 0.0709076
step 1820, training accuracy 0.672613
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step 1820, change in cost 0.0707703
step 1830, training accuracy 0.673662
step 1830, cost 220.009
step 1830, change in cost 0.0707245
step 1840, training accuracy 0.674711
step 1840, cost 219.939
step 1840, change in cost 0.0706177
step 1850, training accuracy 0.674711
step 1850, cost 219.868
step 1850, change in cost 0.0707397
step 1860, training accuracy 0.67681
step 1860, cost 219.797
step 1860, change in cost 0.0706024
step 1870, training accuracy 0.677859
step 1870, cost 219.727
step 1870, change in cost 0.0705261
step 1880, training accuracy 0.678909
step 1880, cost 219.656
step 1880, change in cost 0.0705872
step 1890, training accuracy 0.677859
step 1890, cost 219.586
step 1890, change in cost 0.0704651
step 1900, training accuracy 0.677859
step 1900, cost 219.515
step 1900, change in cost 0.0704803
step 1910, training accuracy 0.677859
step 1910, cost 219.445
step 1910, change in cost 0.0704193
step 1920, training accuracy 0.678909
step 1920, cost 219.374
step 1920, change in cost 0.0703125
step 1930, training accuracy 0.678909
step 1930, cost 219.304
step 1930, change in cost 0.0703583
step 1940, training accuracy 0.678909
step 1940, cost 219.234
step 1940, change in cost 0.070282
step 1950, training accuracy 0.678909
step 1950, cost 219.164
step 1950, change in cost 0.0702667
step 1960, training accuracy 0.679958
step 1960, cost 219.093
step 1960, change in cost 0.0701904
step 1970, training accuracy 0.681007
step 1970, cost 219.023
step 1970, change in cost 0.0701904
step 1980, training accuracy 0.681007
step 1980, cost 218.953
step 1980, change in cost 0.0701447
step 1990, training accuracy 0.682057
step 1990, cost 218.883
step 1990, change in cost 0.0700226
step 2000, training accuracy 0.682057
step 2000, cost 218.813
step 2000, change in cost 0.0700226
step 2010, training accuracy 0.682057
step 2010, cost 218.743
step 2010, change in cost 0.0700531
step 2020, training accuracy 0.683106
step 2020, cost 218.673
step 2020, change in cost 0.0699615
step 2030, training accuracy 0.683106
step 2030, cost 218.603
step 2030, change in cost 0.0699158
step 2040, training accuracy 0.684155
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step 2040, change in cost 0.0699158
step 2050, training accuracy 0.686254
step 2050, cost 218.463
step 2050, change in cost 0.0699005
step 2060, training accuracy 0.687303
step 2060, cost 218.393
step 2060, change in cost 0.0697632
step 2070, training accuracy 0.687303
step 2070, cost 218.324
step 2070, change in cost 0.0697327
step 2080, training accuracy 0.687303
step 2080, cost 218.254
step 2080, change in cost 0.0697632
step 2090, training accuracy 0.687303
step 2090, cost 218.184
step 2090, change in cost 0.0696259
step 2100, training accuracy 0.687303
step 2100, cost 218.115
step 2100, change in cost 0.0696411
step 2110, training accuracy 0.687303
step 2110, cost 218.045
step 2110, change in cost 0.0695038
step 2120, training accuracy 0.688353
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step 2120, change in cost 0.0696564
step 2130, training accuracy 0.688353
step 2130, cost 217.906
step 2130, change in cost 0.069519
step 2140, training accuracy 0.688353
step 2140, cost 217.837
step 2140, change in cost 0.0694733
step 2150, training accuracy 0.688353
step 2150, cost 217.767
step 2150, change in cost 0.0694122
step 2160, training accuracy 0.689402
step 2160, cost 217.698
step 2160, change in cost 0.0693512
step 2170, training accuracy 0.689402
step 2170, cost 217.628
step 2170, change in cost 0.0693359
step 2180, training accuracy 0.689402
step 2180, cost 217.559
step 2180, change in cost 0.0693817
step 2190, training accuracy 0.690451
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step 2190, change in cost 0.0692596
step 2200, training accuracy 0.690451
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step 2200, change in cost 0.0692902
step 2210, training accuracy 0.69255
step 2210, cost 217.351
step 2210, change in cost 0.0691376
step 2220, training accuracy 0.693599
step 2220, cost 217.282
step 2220, change in cost 0.0692444
step 2230, training accuracy 0.694649
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step 2230, change in cost 0.0690308
step 2240, training accuracy 0.694649
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step 2240, change in cost 0.0691376
step 2250, training accuracy 0.695698
step 2250, cost 217.075
step 2250, change in cost 0.069046
step 2260, training accuracy 0.695698
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step 2260, change in cost 0.068924
step 2270, training accuracy 0.695698
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step 2270, change in cost 0.0689697
step 2280, training accuracy 0.696747
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step 2280, change in cost 0.0689545
step 2290, training accuracy 0.697796
step 2290, cost 216.799
step 2290, change in cost 0.0688171
step 2300, training accuracy 0.698846
step 2300, cost 216.73
step 2300, change in cost 0.0688324
step 2310, training accuracy 0.698846
step 2310, cost 216.662
step 2310, change in cost 0.0688934
step 2320, training accuracy 0.698846
step 2320, cost 216.593
step 2320, change in cost 0.0686646
step 2330, training accuracy 0.699895
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step 2330, change in cost 0.0687866
step 2340, training accuracy 0.700944
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step 2340, change in cost 0.068573
step 2350, training accuracy 0.700944
step 2350, cost 216.387
step 2350, change in cost 0.0686493
step 2360, training accuracy 0.701994
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step 2360, change in cost 0.0686188
step 2370, training accuracy 0.701994
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step 2370, change in cost 0.0685577
step 2380, training accuracy 0.704092
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step 2380, change in cost 0.0684814
step 2390, training accuracy 0.704092
step 2390, cost 216.113
step 2390, change in cost 0.0684967
step 2400, training accuracy 0.705142
step 2400, cost 216.044
step 2400, change in cost 0.0683899
step 2410, training accuracy 0.705142
step 2410, cost 215.976
step 2410, change in cost 0.0684204
step 2420, training accuracy 0.705142
step 2420, cost 215.908
step 2420, change in cost 0.0683441
step 2430, training accuracy 0.705142
step 2430, cost 215.839
step 2430, change in cost 0.0682678
step 2440, training accuracy 0.705142
step 2440, cost 215.771
step 2440, change in cost 0.0683136
step 2450, training accuracy 0.705142
step 2450, cost 215.703
step 2450, change in cost 0.0681915
step 2460, training accuracy 0.705142
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step 2460, change in cost 0.0682373
step 2470, training accuracy 0.705142
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step 2470, change in cost 0.0681305
step 2480, training accuracy 0.706191
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step 2480, change in cost 0.0681152
step 2490, training accuracy 0.706191
step 2490, cost 215.43
step 2490, change in cost 0.0680389
step 2500, training accuracy 0.706191
step 2500, cost 215.362
step 2500, change in cost 0.0680542
step 2510, training accuracy 0.706191
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step 2510, change in cost 0.0679626
step 2520, training accuracy 0.706191
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step 2520, change in cost 0.0680084
step 2530, training accuracy 0.706191
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step 2530, change in cost 0.0679016
step 2540, training accuracy 0.70829
step 2540, cost 215.091
step 2540, change in cost 0.0678406
step 2550, training accuracy 0.70829
step 2550, cost 215.023
step 2550, change in cost 0.0677338
step 2560, training accuracy 0.70829
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step 2560, change in cost 0.0678253
step 2570, training accuracy 0.709339
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step 2570, change in cost 0.0677185
step 2580, training accuracy 0.710388
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step 2580, change in cost 0.067749
step 2590, training accuracy 0.712487
step 2590, cost 214.752
step 2590, change in cost 0.0677338
step 2600, training accuracy 0.712487
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step 2600, change in cost 0.0675964
step 2610, training accuracy 0.713536
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step 2610, change in cost 0.0675507
step 2620, training accuracy 0.713536
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step 2620, change in cost 0.0675354
step 2630, training accuracy 0.713536
step 2630, cost 214.482
step 2630, change in cost 0.0675354
step 2640, training accuracy 0.713536
step 2640, cost 214.414
step 2640, change in cost 0.0674438
step 2650, training accuracy 0.713536
step 2650, cost 214.347
step 2650, change in cost 0.0674133
step 2660, training accuracy 0.713536
step 2660, cost 214.279
step 2660, change in cost 0.0673676
step 2670, training accuracy 0.714586
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step 2670, change in cost 0.0673218
step 2680, training accuracy 0.714586
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step 2680, change in cost 0.0673523
step 2690, training accuracy 0.714586
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step 2690, change in cost 0.0672607
step 2700, training accuracy 0.715635
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step 2700, change in cost 0.0672302
step 2710, training accuracy 0.715635
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step 2710, change in cost 0.067215
step 2720, training accuracy 0.716684
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step 2720, change in cost 0.0671387
step 2730, training accuracy 0.719832
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step 2730, change in cost 0.0670776
step 2740, training accuracy 0.720881
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step 2740, change in cost 0.0670624
step 2750, training accuracy 0.720881
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step 2750, change in cost 0.0671082
step 2760, training accuracy 0.720881
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step 2760, change in cost 0.066864
step 2770, training accuracy 0.724029
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step 2770, change in cost 0.0670013
step 2780, training accuracy 0.725079
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step 2780, change in cost 0.0669861
step 2790, training accuracy 0.725079
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step 2790, change in cost 0.0667114
step 2800, training accuracy 0.727177
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step 2800, change in cost 0.0668945
step 2810, training accuracy 0.727177
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step 2810, change in cost 0.0667572
step 2820, training accuracy 0.728227
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step 2820, change in cost 0.0667572
step 2830, training accuracy 0.730325
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step 2830, change in cost 0.0666809
step 2840, training accuracy 0.730325
step 2840, cost 213.073
step 2840, change in cost 0.0666351
step 2850, training accuracy 0.730325
step 2850, cost 213.007
step 2850, change in cost 0.0666504
step 2860, training accuracy 0.730325
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step 2860, change in cost 0.0666046
step 2870, training accuracy 0.731375
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step 2870, change in cost 0.0666199
step 2880, training accuracy 0.732424
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step 2880, change in cost 0.0665131
step 2890, training accuracy 0.732424
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step 2890, change in cost 0.0664825
step 2900, training accuracy 0.733473
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step 2900, change in cost 0.066391
step 2910, training accuracy 0.733473
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step 2910, change in cost 0.0664215
step 2920, training accuracy 0.734523
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step 2920, change in cost 0.0662994
step 2930, training accuracy 0.734523
step 2930, cost 212.475
step 2930, change in cost 0.06633
step 2940, training accuracy 0.734523
step 2940, cost 212.409
step 2940, change in cost 0.0662689
step 2950, training accuracy 0.734523
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step 2950, change in cost 0.0662384
step 2960, training accuracy 0.734523
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step 2960, change in cost 0.0661621
step 2970, training accuracy 0.735572
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step 2970, change in cost 0.0661621
step 2980, training accuracy 0.737671
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step 2980, change in cost 0.0660553
step 2990, training accuracy 0.73872
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step 3000, training accuracy 0.73872
step 3000, cost 212.012
step 3000, change in cost 0.0660095
step 3010, training accuracy 0.737671
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step 3010, change in cost 0.0659485
step 3020, training accuracy 0.739769
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step 3020, change in cost 0.0660095
step 3030, training accuracy 0.739769
step 3030, cost 211.814
step 3030, change in cost 0.0658875
step 3040, training accuracy 0.739769
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step 3040, change in cost 0.0658875
step 3050, training accuracy 0.739769
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step 3060, training accuracy 0.739769
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step 3070, training accuracy 0.740818
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step 3080, training accuracy 0.741868
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step 3080, change in cost 0.0657654
step 3090, training accuracy 0.742917
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step 3090, change in cost 0.0656891
step 3100, training accuracy 0.742917
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step 3100, change in cost 0.0656128
step 3110, training accuracy 0.742917
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step 3120, training accuracy 0.742917
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step 3130, training accuracy 0.742917
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step 3140, training accuracy 0.745016
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step 3150, training accuracy 0.746065
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step 3160, training accuracy 0.748164
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step 3170, training accuracy 0.748164
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step 3180, training accuracy 0.749213
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step 3190, training accuracy 0.750262
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step 3200, training accuracy 0.751312
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step 3210, training accuracy 0.751312
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step 3210, change in cost 0.0652313
step 3220, training accuracy 0.751312
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step 3220, change in cost 0.0651398
step 3230, training accuracy 0.751312
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step 3230, change in cost 0.0651398
step 3240, training accuracy 0.751312
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step 3240, change in cost 0.0650635
step 3250, training accuracy 0.752361
step 3250, cost 210.374
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step 3260, training accuracy 0.752361
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step 3260, change in cost 0.0650177
step 3270, training accuracy 0.752361
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step 3270, change in cost 0.065033
step 3280, training accuracy 0.752361
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step 3280, change in cost 0.0648804
step 3290, training accuracy 0.752361
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step 3300, training accuracy 0.75446
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step 3310, training accuracy 0.75446
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step 3320, training accuracy 0.755509
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step 3330, training accuracy 0.755509
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step 3340, training accuracy 0.755509
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step 3350, training accuracy 0.756558
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step 3360, training accuracy 0.756558
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step 3370, training accuracy 0.756558
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step 3390, training accuracy 0.756558
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step 3390, change in cost 0.0645142
step 3400, training accuracy 0.756558
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step 3420, training accuracy 0.757608
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step 3450, training accuracy 0.758657
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step 3460, training accuracy 0.758657
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step 3470, training accuracy 0.758657
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step 3480, training accuracy 0.759706
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step 3490, training accuracy 0.760755
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step 3500, training accuracy 0.760755
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step 3550, training accuracy 0.761805
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step 3570, training accuracy 0.762854
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step 3580, training accuracy 0.764953
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step 3590, training accuracy 0.764953
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step 3590, change in cost 0.0637207
step 3600, training accuracy 0.764953
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step 3610, training accuracy 0.764953
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step 3610, change in cost 0.0636749
step 3620, training accuracy 0.766002
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step 3620, change in cost 0.0635834
step 3630, training accuracy 0.766002
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step 3630, change in cost 0.0635529
step 3640, training accuracy 0.767051
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step 3640, change in cost 0.0635071
step 3650, training accuracy 0.768101
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step 3660, training accuracy 0.770199
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step 3660, change in cost 0.0634308
step 3670, training accuracy 0.770199
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step 3680, training accuracy 0.770199
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step 3690, training accuracy 0.770199
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step 3690, change in cost 0.0633087
step 3700, training accuracy 0.771249
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step 3710, training accuracy 0.773347
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step 3720, training accuracy 0.773347
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step 3730, training accuracy 0.773347
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step 3730, change in cost 0.0632019
step 3740, training accuracy 0.773347
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step 3740, change in cost 0.0631256
step 3750, training accuracy 0.774397
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step 3750, change in cost 0.0631561
step 3760, training accuracy 0.774397
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step 3820, training accuracy 0.777545
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step 4000, training accuracy 0.780693
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step 4020, training accuracy 0.780693
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step 4100, training accuracy 0.786988
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step 4110, training accuracy 0.788038
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step 4130, training accuracy 0.789087
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step 4140, training accuracy 0.789087
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step 4150, training accuracy 0.789087
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step 4180, training accuracy 0.789087
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step 4190, training accuracy 0.789087
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step 4220, training accuracy 0.790136
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step 4230, training accuracy 0.790136
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step 4240, training accuracy 0.791186
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step 4310, training accuracy 0.796432
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step 4320, training accuracy 0.797482
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step 4320, change in cost 0.0609589
step 4330, training accuracy 0.797482
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step 4340, training accuracy 0.797482
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step 4350, training accuracy 0.798531
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step 4360, training accuracy 0.798531
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step 4390, training accuracy 0.79958
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step 4420, training accuracy 0.79958
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step 4440, training accuracy 0.80063
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step 4450, training accuracy 0.80063
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step 4460, training accuracy 0.801679
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step 4480, training accuracy 0.801679
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step 4490, training accuracy 0.801679
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step 4500, training accuracy 0.802728
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step 4510, training accuracy 0.804827
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step 4520, training accuracy 0.805876
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step 4520, change in cost 0.0601959
step 4530, training accuracy 0.806925
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step 4550, training accuracy 0.806925
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step 4590, training accuracy 0.809024
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step 4600, training accuracy 0.809024
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step 4610, training accuracy 0.809024
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step 4620, training accuracy 0.809024
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step 4620, change in cost 0.0598145
step 4630, training accuracy 0.811123
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step 4630, change in cost 0.0598602
step 4640, training accuracy 0.811123
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step 4650, training accuracy 0.811123
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step 4660, training accuracy 0.812172
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step 4670, training accuracy 0.812172
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step 4720, training accuracy 0.816369
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step 4740, training accuracy 0.816369
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step 4760, training accuracy 0.817419
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step 4770, training accuracy 0.817419
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step 4790, training accuracy 0.817419
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step 4800, training accuracy 0.817419
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step 4810, training accuracy 0.817419
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step 4820, training accuracy 0.819517
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step 4840, training accuracy 0.819517
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step 4900, training accuracy 0.820567
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step 4920, training accuracy 0.822665
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step 4930, training accuracy 0.822665
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step 4950, training accuracy 0.822665
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step 4960, training accuracy 0.824764
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step 4990, training accuracy 0.825813
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step 5000, training accuracy 0.826863
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step 5090, training accuracy 0.83001
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step 5100, training accuracy 0.83001
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step 5110, training accuracy 0.83001
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step 5260, training accuracy 0.833158
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step 5270, training accuracy 0.833158
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step 5280, training accuracy 0.833158
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step 5300, training accuracy 0.833158
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step 5390, training accuracy 0.839454
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step 5970, training accuracy 0.854145
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step 6090, training accuracy 0.857293
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step 6720, training accuracy 0.878279
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step 6920, training accuracy 0.879328
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step 6960, training accuracy 0.880378
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step 7420, training accuracy 0.888772
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step 8340, training accuracy 0.913956
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step 8590, training accuracy 0.92235
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step 8660, training accuracy 0.92235
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step 8680, training accuracy 0.92235
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step 8720, training accuracy 0.92235
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step 8750, training accuracy 0.9234
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step 8760, training accuracy 0.9234
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step 8770, training accuracy 0.9234
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step 8780, training accuracy 0.9234
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step 8810, training accuracy 0.9234
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step 8820, training accuracy 0.9234
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step 8840, training accuracy 0.9234
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step 8850, training accuracy 0.9234
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step 8860, training accuracy 0.924449
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step 8870, training accuracy 0.924449
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step 8880, training accuracy 0.924449
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step 8890, training accuracy 0.924449
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step 8910, training accuracy 0.924449
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step 8920, training accuracy 0.924449
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step 8930, training accuracy 0.924449
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step 8940, training accuracy 0.924449
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step 8950, training accuracy 0.924449
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step 8960, training accuracy 0.924449
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step 8970, training accuracy 0.924449
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step 8980, training accuracy 0.924449
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step 9000, training accuracy 0.925498
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step 9010, training accuracy 0.925498
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step 9020, training accuracy 0.925498
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step 9040, training accuracy 0.925498
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step 9050, training accuracy 0.925498
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step 9060, training accuracy 0.926548
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step 9070, training accuracy 0.926548
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step 9080, training accuracy 0.926548
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step 9090, training accuracy 0.926548
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step 9150, training accuracy 0.927597
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step 9220, training accuracy 0.929696
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step 9230, training accuracy 0.929696
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step 9290, training accuracy 0.929696
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step 11720, training accuracy 0.943337
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step 15070, training accuracy 0.947534
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step 15090, training accuracy 0.948583
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step 15230, training accuracy 0.948583
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step 15820, training accuracy 0.948583
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step 16720, training accuracy 0.951731
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step 16730, training accuracy 0.951731
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step 16820, training accuracy 0.951731
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step 16840, training accuracy 0.951731
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step 17100, training accuracy 0.951731
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step 17110, training accuracy 0.951731
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step 17130, training accuracy 0.951731
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step 17240, training accuracy 0.951731
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step 17320, training accuracy 0.951731
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step 17350, training accuracy 0.951731
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step 17370, training accuracy 0.951731
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step 17390, training accuracy 0.951731
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step 17400, training accuracy 0.951731
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step 17410, training accuracy 0.951731
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step 17420, training accuracy 0.951731
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step 17430, training accuracy 0.951731
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step 17440, training accuracy 0.951731
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step 17450, training accuracy 0.951731
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step 17460, training accuracy 0.951731
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step 17470, training accuracy 0.951731
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step 17490, training accuracy 0.951731
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step 17500, training accuracy 0.951731
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step 17510, training accuracy 0.951731
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step 17520, training accuracy 0.951731
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step 17520, change in cost 0.0293579
step 17530, training accuracy 0.951731
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step 17530, change in cost 0.0293884
step 17540, training accuracy 0.951731
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step 17540, change in cost 0.0292969
step 17550, training accuracy 0.951731
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step 17550, change in cost 0.0293274
step 17560, training accuracy 0.951731
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step 17560, change in cost 0.0292969
step 17570, training accuracy 0.951731
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step 17570, change in cost 0.0292969
step 17580, training accuracy 0.951731
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step 17580, change in cost 0.0292664
step 17590, training accuracy 0.951731
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step 17590, change in cost 0.0292969
step 17600, training accuracy 0.951731
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step 17600, change in cost 0.0292206
step 17610, training accuracy 0.951731
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step 17610, change in cost 0.0292664
step 17620, training accuracy 0.951731
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step 17620, change in cost 0.0292053
step 17630, training accuracy 0.951731
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step 17630, change in cost 0.0292206
step 17640, training accuracy 0.951731
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step 17640, change in cost 0.0291748
step 17650, training accuracy 0.951731
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step 17650, change in cost 0.0292206
step 17660, training accuracy 0.951731
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step 17660, change in cost 0.0291595
step 17670, training accuracy 0.951731
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step 17670, change in cost 0.0291595
step 17680, training accuracy 0.951731
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step 17690, training accuracy 0.951731
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step 17700, training accuracy 0.951731
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step 17710, training accuracy 0.951731
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step 17720, training accuracy 0.951731
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step 17720, change in cost 0.029129
step 17730, training accuracy 0.951731
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step 17740, training accuracy 0.951731
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step 17750, training accuracy 0.951731
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step 17760, training accuracy 0.951731
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step 17770, training accuracy 0.951731
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step 17780, training accuracy 0.951731
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step 17780, change in cost 0.0289307
step 17790, training accuracy 0.951731
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step 17800, training accuracy 0.951731
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step 17800, change in cost 0.0289917
step 17810, training accuracy 0.951731
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step 17810, change in cost 0.0289001
step 17820, training accuracy 0.951731
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step 17820, change in cost 0.0289917
step 17830, training accuracy 0.951731
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step 17830, change in cost 0.0289307
step 17840, training accuracy 0.951731
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step 17840, change in cost 0.0289307
step 17850, training accuracy 0.951731
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step 17860, training accuracy 0.951731
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step 17860, change in cost 0.0288391
step 17870, training accuracy 0.951731
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step 17880, training accuracy 0.951731
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step 17880, change in cost 0.0289307
step 17890, training accuracy 0.951731
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step 17900, training accuracy 0.951731
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step 17910, training accuracy 0.951731
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step 17910, change in cost 0.0288391
step 17920, training accuracy 0.951731
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step 17920, change in cost 0.0288391
step 17930, training accuracy 0.951731
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step 17940, training accuracy 0.951731
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step 17940, change in cost 0.0287628
step 17950, training accuracy 0.951731
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step 17960, training accuracy 0.951731
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step 17960, change in cost 0.0287933
step 17970, training accuracy 0.951731
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step 17970, change in cost 0.0287323
step 17980, training accuracy 0.951731
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step 17980, change in cost 0.0287476
step 17990, training accuracy 0.951731
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step 18000, training accuracy 0.951731
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step 18000, change in cost 0.0287781
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step 18020, change in cost 0.0286713
step 18030, training accuracy 0.951731
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step 18040, training accuracy 0.951731
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step 18050, training accuracy 0.951731
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step 18060, training accuracy 0.951731
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step 18070, training accuracy 0.951731
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step 18070, change in cost 0.0286255
step 18080, training accuracy 0.951731
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step 18100, training accuracy 0.951731
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step 18110, training accuracy 0.951731
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step 18140, training accuracy 0.951731
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step 18150, training accuracy 0.951731
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step 18160, training accuracy 0.951731
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step 18180, training accuracy 0.951731
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step 18200, training accuracy 0.951731
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step 18220, training accuracy 0.951731
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step 18230, training accuracy 0.951731
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step 18240, training accuracy 0.951731
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step 18290, training accuracy 0.951731
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step 18300, training accuracy 0.951731
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step 18310, training accuracy 0.951731
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step 18310, change in cost 0.0282898
step 18320, training accuracy 0.951731
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step 18330, training accuracy 0.951731
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step 18350, training accuracy 0.951731
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step 18450, training accuracy 0.951731
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step 18500, training accuracy 0.951731
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step 18590, training accuracy 0.952781
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step 18600, training accuracy 0.952781
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step 18620, training accuracy 0.952781
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step 18660, change in cost 0.0278931
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step 18690, training accuracy 0.95383
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step 18700, training accuracy 0.95383
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step 18730, training accuracy 0.95383
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step 18750, training accuracy 0.95383
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step 18760, training accuracy 0.95383
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step 18760, change in cost 0.0277557
step 18770, training accuracy 0.95383
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step 18770, change in cost 0.0276489
step 18780, training accuracy 0.95383
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step 18780, change in cost 0.02771
step 18790, training accuracy 0.95383
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step 18790, change in cost 0.0276947
step 18800, training accuracy 0.95383
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step 18810, training accuracy 0.95383
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step 18810, change in cost 0.0276642
step 18820, training accuracy 0.95383
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step 18820, change in cost 0.0276337
step 18830, training accuracy 0.95383
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step 18830, change in cost 0.0276794
step 18840, training accuracy 0.95383
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step 18840, change in cost 0.0275879
step 18850, training accuracy 0.95383
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step 18850, change in cost 0.0276337
step 18860, training accuracy 0.95383
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step 18860, change in cost 0.0276031
step 18870, training accuracy 0.95383
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step 18870, change in cost 0.0275574
step 18880, training accuracy 0.95383
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step 18880, change in cost 0.0276031
step 18890, training accuracy 0.95383
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step 18890, change in cost 0.0275421
step 18900, training accuracy 0.95383
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step 18900, change in cost 0.0275574
step 18910, training accuracy 0.95383
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step 18910, change in cost 0.0275574
step 18920, training accuracy 0.95383
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step 18920, change in cost 0.0275269
step 18930, training accuracy 0.95383
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step 18930, change in cost 0.0274506
step 18940, training accuracy 0.95383
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step 18940, change in cost 0.0274963
step 18950, training accuracy 0.95383
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step 18950, change in cost 0.0274811
step 18960, training accuracy 0.95383
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step 18960, change in cost 0.0275269
step 18970, training accuracy 0.95383
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step 18970, change in cost 0.0274353
step 18980, training accuracy 0.95383
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step 18990, training accuracy 0.95383
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step 19000, change in cost 0.027359
step 19010, training accuracy 0.95383
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step 19030, change in cost 0.0274048
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step 19040, change in cost 0.0273438
step 19050, training accuracy 0.95383
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step 19050, change in cost 0.027359
step 19060, training accuracy 0.95383
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step 19060, change in cost 0.0273285
step 19070, training accuracy 0.95383
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step 19080, training accuracy 0.95383
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step 19080, change in cost 0.0273438
step 19090, training accuracy 0.95383
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step 19090, change in cost 0.0272827
step 19100, training accuracy 0.95383
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step 19110, change in cost 0.027298
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step 19130, training accuracy 0.95383
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step 19150, training accuracy 0.95383
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step 19160, training accuracy 0.95383
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step 19170, change in cost 0.0272217
step 19180, training accuracy 0.95383
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step 19190, training accuracy 0.95383
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step 19200, training accuracy 0.95383
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step 19210, training accuracy 0.95383
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step 19210, change in cost 0.0271912
step 19220, training accuracy 0.95383
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step 19220, change in cost 0.0271606
step 19230, training accuracy 0.95383
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step 19240, training accuracy 0.95383
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step 19240, change in cost 0.0271454
step 19250, training accuracy 0.95383
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step 24820, training accuracy 0.956978
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step 24920, training accuracy 0.956978
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step 25010, training accuracy 0.956978
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step 25220, training accuracy 0.956978
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step 25400, training accuracy 0.956978
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step 25590, training accuracy 0.956978
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step 25600, training accuracy 0.956978
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step 25620, training accuracy 0.956978
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step 25720, training accuracy 0.956978
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step 25740, training accuracy 0.956978
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step 25750, training accuracy 0.956978
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step 25770, training accuracy 0.956978
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step 25780, training accuracy 0.956978
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step 25780, change in cost 0.0206451
step 25790, training accuracy 0.956978
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step 25800, training accuracy 0.956978
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step 25810, training accuracy 0.956978
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step 25820, training accuracy 0.956978
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step 25820, change in cost 0.0205994
step 25830, training accuracy 0.956978
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step 25830, change in cost 0.0206146
step 25840, training accuracy 0.956978
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step 25850, training accuracy 0.956978
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step 25850, change in cost 0.020607
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step 25860, change in cost 0.0205765
step 25870, training accuracy 0.956978
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step 25870, change in cost 0.0205841
step 25880, training accuracy 0.956978
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step 25890, training accuracy 0.956978
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step 25890, change in cost 0.0205688
step 25900, training accuracy 0.956978
step 25900, cost 126.962
step 25900, change in cost 0.0205688
step 25910, training accuracy 0.956978
step 25910, cost 126.942
step 25910, change in cost 0.0205536
step 25920, training accuracy 0.956978
step 25920, cost 126.921
step 25920, change in cost 0.0205154
step 25930, training accuracy 0.956978
step 25930, cost 126.901
step 25930, change in cost 0.020546
step 25940, training accuracy 0.956978
step 25940, cost 126.88
step 25940, change in cost 0.0205536
step 25950, training accuracy 0.956978
step 25950, cost 126.86
step 25950, change in cost 0.0205078
step 25960, training accuracy 0.956978
step 25960, cost 126.839
step 25960, change in cost 0.0204849
step 25970, training accuracy 0.956978
step 25970, cost 126.819
step 25970, change in cost 0.020546
step 25980, training accuracy 0.956978
step 25980, cost 126.798
step 25980, change in cost 0.0204544
step 25990, training accuracy 0.956978
step 25990, cost 126.778
step 25990, change in cost 0.0204849
step 26000, training accuracy 0.956978
step 26000, cost 126.757
step 26000, change in cost 0.0205002
step 26010, training accuracy 0.956978
step 26010, cost 126.737
step 26010, change in cost 0.0204239
step 26020, training accuracy 0.956978
step 26020, cost 126.716
step 26020, change in cost 0.020462
step 26030, training accuracy 0.956978
step 26030, cost 126.696
step 26030, change in cost 0.0204391
step 26040, training accuracy 0.956978
step 26040, cost 126.675
step 26040, change in cost 0.0204849
step 26050, training accuracy 0.958027
step 26050, cost 126.655
step 26050, change in cost 0.0204315
step 26060, training accuracy 0.958027
step 26060, cost 126.634
step 26060, change in cost 0.0204391
step 26070, training accuracy 0.958027
step 26070, cost 126.614
step 26070, change in cost 0.0203857
step 26080, training accuracy 0.958027
step 26080, cost 126.594
step 26080, change in cost 0.0204163
step 26090, training accuracy 0.958027
step 26090, cost 126.573
step 26090, change in cost 0.020401
step 26100, training accuracy 0.958027
step 26100, cost 126.553
step 26100, change in cost 0.0204315
step 26110, training accuracy 0.958027
step 26110, cost 126.532
step 26110, change in cost 0.0203629
step 26120, training accuracy 0.958027
step 26120, cost 126.512
step 26120, change in cost 0.0203552
step 26130, training accuracy 0.958027
step 26130, cost 126.492
step 26130, change in cost 0.020401
step 26140, training accuracy 0.958027
step 26140, cost 126.471
step 26140, change in cost 0.0203705
step 26150, training accuracy 0.958027
step 26150, cost 126.451
step 26150, change in cost 0.0203705
step 26160, training accuracy 0.958027
step 26160, cost 126.431
step 26160, change in cost 0.0203552
step 26170, training accuracy 0.958027
step 26170, cost 126.41
step 26170, change in cost 0.0203476
step 26180, training accuracy 0.958027
step 26180, cost 126.39
step 26180, change in cost 0.0203323
step 26190, training accuracy 0.958027
step 26190, cost 126.37
step 26190, change in cost 0.02034
step 26200, training accuracy 0.958027
step 26200, cost 126.349
step 26200, change in cost 0.0202942
step 26210, training accuracy 0.958027
step 26210, cost 126.329
step 26210, change in cost 0.0203323
step 26220, training accuracy 0.958027
step 26220, cost 126.309
step 26220, change in cost 0.0203018
step 26230, training accuracy 0.958027
step 26230, cost 126.288
step 26230, change in cost 0.0203018
step 26240, training accuracy 0.958027
step 26240, cost 126.268
step 26240, change in cost 0.0202713
step 26250, training accuracy 0.958027
step 26250, cost 126.248
step 26250, change in cost 0.0202942
step 26260, training accuracy 0.958027
step 26260, cost 126.227
step 26260, change in cost 0.0202637
step 26270, training accuracy 0.958027
step 26270, cost 126.207
step 26270, change in cost 0.0202332
step 26280, training accuracy 0.958027
step 26280, cost 126.187
step 26280, change in cost 0.0203247
step 26290, training accuracy 0.958027
step 26290, cost 126.167
step 26290, change in cost 0.0202408
step 26300, training accuracy 0.958027
step 26300, cost 126.146
step 26300, change in cost 0.020195
step 26310, training accuracy 0.958027
step 26310, cost 126.126
step 26310, change in cost 0.020256
step 26320, training accuracy 0.958027
step 26320, cost 126.106
step 26320, change in cost 0.0202408
step 26330, training accuracy 0.958027
step 26330, cost 126.086
step 26330, change in cost 0.0202026
step 26340, training accuracy 0.958027
step 26340, cost 126.066
step 26340, change in cost 0.0202026
step 26350, training accuracy 0.958027
step 26350, cost 126.045
step 26350, change in cost 0.0202332
step 26360, training accuracy 0.958027
step 26360, cost 126.025
step 26360, change in cost 0.0201645
step 26370, training accuracy 0.958027
step 26370, cost 126.005
step 26370, change in cost 0.020195
step 26380, training accuracy 0.958027
step 26380, cost 125.985
step 26380, change in cost 0.020195
step 26390, training accuracy 0.958027
step 26390, cost 125.965
step 26390, change in cost 0.0201569
step 26400, training accuracy 0.958027
step 26400, cost 125.944
step 26400, change in cost 0.0201874
step 26410, training accuracy 0.958027
step 26410, cost 125.924
step 26410, change in cost 0.0201492
step 26420, training accuracy 0.958027
step 26420, cost 125.904
step 26420, change in cost 0.020134
step 26430, training accuracy 0.958027
step 26430, cost 125.884
step 26430, change in cost 0.0201797
step 26440, training accuracy 0.958027
step 26440, cost 125.864
step 26440, change in cost 0.0200958
step 26450, training accuracy 0.959077
step 26450, cost 125.844
step 26450, change in cost 0.0201721
step 26460, training accuracy 0.959077
step 26460, cost 125.824
step 26460, change in cost 0.020134
step 26470, training accuracy 0.959077
step 26470, cost 125.804
step 26470, change in cost 0.0200729
step 26480, training accuracy 0.959077
step 26480, cost 125.783
step 26480, change in cost 0.0200729
step 26490, training accuracy 0.959077
step 26490, cost 125.763
step 26490, change in cost 0.020134
step 26500, training accuracy 0.959077
step 26500, cost 125.743
step 26500, change in cost 0.0200958
step 26510, training accuracy 0.959077
step 26510, cost 125.723
step 26510, change in cost 0.0200882
step 26520, training accuracy 0.959077
step 26520, cost 125.703
step 26520, change in cost 0.02005
step 26530, training accuracy 0.959077
step 26530, cost 125.683
step 26530, change in cost 0.0200729
step 26540, training accuracy 0.959077
step 26540, cost 125.663
step 26540, change in cost 0.0200806
step 26550, training accuracy 0.959077
step 26550, cost 125.643
step 26550, change in cost 0.0200348
step 26560, training accuracy 0.959077
step 26560, cost 125.623
step 26560, change in cost 0.0200272
step 26570, training accuracy 0.959077
step 26570, cost 125.603
step 26570, change in cost 0.0200272
step 26580, training accuracy 0.959077
step 26580, cost 125.583
step 26580, change in cost 0.0200577
step 26590, training accuracy 0.959077
step 26590, cost 125.563
step 26590, change in cost 0.019989
step 26600, training accuracy 0.959077
step 26600, cost 125.543
step 26600, change in cost 0.0200195
step 26610, training accuracy 0.959077
step 26610, cost 125.523
step 26610, change in cost 0.0199966
step 26620, training accuracy 0.959077
step 26620, cost 125.503
step 26620, change in cost 0.019989
step 26630, training accuracy 0.959077
step 26630, cost 125.483
step 26630, change in cost 0.0200043
step 26640, training accuracy 0.959077
step 26640, cost 125.463
step 26640, change in cost 0.019989
step 26650, training accuracy 0.959077
step 26650, cost 125.443
step 26650, change in cost 0.0199738
step 26660, training accuracy 0.959077
step 26660, cost 125.423
step 26660, change in cost 0.0199509
step 26670, training accuracy 0.959077
step 26670, cost 125.403
step 26670, change in cost 0.0199814
step 26680, training accuracy 0.959077
step 26680, cost 125.383
step 26680, change in cost 0.0199661
step 26690, training accuracy 0.959077
step 26690, cost 125.363
step 26690, change in cost 0.0199356
step 26700, training accuracy 0.959077
step 26700, cost 125.343
step 26700, change in cost 0.0198822
step 26710, training accuracy 0.959077
step 26710, cost 125.323
step 26710, change in cost 0.0199585
step 26720, training accuracy 0.959077
step 26720, cost 125.303
step 26720, change in cost 0.0198975
step 26730, training accuracy 0.959077
step 26730, cost 125.283
step 26730, change in cost 0.0199585
step 26740, training accuracy 0.959077
step 26740, cost 125.263
step 26740, change in cost 0.0198822
step 26750, training accuracy 0.959077
step 26750, cost 125.244
step 26750, change in cost 0.0198746
step 26760, training accuracy 0.959077
step 26760, cost 125.224
step 26760, change in cost 0.0199051
step 26770, training accuracy 0.959077
step 26770, cost 125.204
step 26770, change in cost 0.0198898
step 26780, training accuracy 0.959077
step 26780, cost 125.184
step 26780, change in cost 0.0198593
step 26790, training accuracy 0.959077
step 26790, cost 125.164
step 26790, change in cost 0.0199127
step 26800, training accuracy 0.959077
step 26800, cost 125.144
step 26800, change in cost 0.0198517
step 26810, training accuracy 0.959077
step 26810, cost 125.124
step 26810, change in cost 0.0198441
step 26820, training accuracy 0.959077
step 26820, cost 125.104
step 26820, change in cost 0.0198135
step 26830, training accuracy 0.959077
step 26830, cost 125.085
step 26830, change in cost 0.0198593
step 26840, training accuracy 0.959077
step 26840, cost 125.065
step 26840, change in cost 0.0198288
step 26850, training accuracy 0.959077
step 26850, cost 125.045
step 26850, change in cost 0.0198441
step 26860, training accuracy 0.959077
step 26860, cost 125.025
step 26860, change in cost 0.0198135
step 26870, training accuracy 0.959077
step 26870, cost 125.005
step 26870, change in cost 0.0197983
step 26880, training accuracy 0.959077
step 26880, cost 124.985
step 26880, change in cost 0.0198135
step 26890, training accuracy 0.959077
step 26890, cost 124.966
step 26890, change in cost 0.019783
step 26900, training accuracy 0.959077
step 26900, cost 124.946
step 26900, change in cost 0.0197983
step 26910, training accuracy 0.959077
step 26910, cost 124.926
step 26910, change in cost 0.0197983
step 26920, training accuracy 0.959077
step 26920, cost 124.906
step 26920, change in cost 0.019722
step 26930, training accuracy 0.959077
step 26930, cost 124.887
step 26930, change in cost 0.0197906
step 26940, training accuracy 0.959077
step 26940, cost 124.867
step 26940, change in cost 0.0197296
step 26950, training accuracy 0.959077
step 26950, cost 124.847
step 26950, change in cost 0.0197678
step 26960, training accuracy 0.959077
step 26960, cost 124.827
step 26960, change in cost 0.0197449
step 26970, training accuracy 0.959077
step 26970, cost 124.808
step 26970, change in cost 0.0197144
step 26980, training accuracy 0.959077
step 26980, cost 124.788
step 26980, change in cost 0.0197296
step 26990, training accuracy 0.959077
step 26990, cost 124.768
step 26990, change in cost 0.0196991
final accuracy on test set: 0.942857

In [80]:

    

# 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_OPnn = tf.equal(tf.argmax(activation_OPnn,1),tf.argmax(yGoldnn,1))
# False is 0 and True is 1, what was our average?
accuracy_OPnn = tf.reduce_mean(tf.cast(correct_predictions_OPnn, "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_OPnn, feed_dict={Xnn: trainX, yGoldnn: trainY})
        # Report occasional stats
        if i % 10 == 0:
            # Add epoch to epoch_values
            epoch_valuesnn.append(i)
            # Generate accuracy stats on test data
            train_accuracy, newCost = sess.run(
                [accuracy_OPnn, cost_OPnn], 
                feed_dict={Xnn: trainX, yGoldnn: trainY}
            )
            # Add accuracy to live graphing variable
            accuracy_valuesnn.append(train_accuracy)
            # Add cost to live graphing variable
            cost_valuesnn.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_OPnn, 
                                                     feed_dict={Xnn: testX, 
                                                                yGoldnn: testY})))


    

    




step 0, training accuracy 0.466946
step 0, cost 463.352
step 0, change in cost 463.352
step 10, training accuracy 0.466946
step 10, cost 452.735
step 10, change in cost 10.6168
step 20, training accuracy 0.466946
step 20, cost 413.871
step 20, change in cost 38.8636
step 30, training accuracy 0.466946
step 30, cost 370.842
step 30, change in cost 43.0297
step 40, training accuracy 0.466946
step 40, cost 369.716
step 40, change in cost 1.12534
step 50, training accuracy 0.466946
step 50, cost 369.591
step 50, change in cost 0.125549
step 60, training accuracy 0.466946
step 60, cost 369.463
step 60, change in cost 0.127716
step 70, training accuracy 0.466946
step 70, cost 369.328
step 70, change in cost 0.134888
step 80, training accuracy 0.466946
step 80, cost 369.185
step 80, change in cost 0.143768
step 90, training accuracy 0.466946
step 90, cost 369.03
step 90, change in cost 0.154388
step 100, training accuracy 0.466946
step 100, cost 368.862
step 100, change in cost 0.168091
step 110, training accuracy 0.466946
step 110, cost 368.677
step 110, change in cost 0.18512
step 120, training accuracy 0.466946
step 120, cost 368.47
step 120, change in cost 0.207031
step 130, training accuracy 0.466946
step 130, cost 368.234
step 130, change in cost 0.23584
step 140, training accuracy 0.466946
step 140, cost 367.959
step 140, change in cost 0.275177
step 150, training accuracy 0.466946
step 150, cost 367.629
step 150, change in cost 0.329926
step 160, training accuracy 0.466946
step 160, cost 367.22
step 160, change in cost 0.409332
step 170, training accuracy 0.466946
step 170, cost 366.688
step 170, change in cost 0.53125
step 180, training accuracy 0.466946
step 180, cost 365.958
step 180, change in cost 0.730865
step 190, training accuracy 0.466946
step 190, cost 364.867
step 190, change in cost 1.0903
step 200, training accuracy 0.466946
step 200, cost 363.034
step 200, change in cost 1.83353
step 210, training accuracy 0.466946
step 210, cost 359.278
step 210, change in cost 3.75568
step 220, training accuracy 0.466946
step 220, cost 347.988
step 220, change in cost 11.2896
step 230, training accuracy 0.473242
step 230, cost 279.189
step 230, change in cost 68.7994
step 240, training accuracy 0.581322
step 240, cost 235.647
step 240, change in cost 43.542
step 250, training accuracy 0.583421
step 250, cost 235.319
step 250, change in cost 0.328049
step 260, training accuracy 0.58447
step 260, cost 235.118
step 260, change in cost 0.201355
step 270, training accuracy 0.583421
step 270, cost 234.917
step 270, change in cost 0.200668
step 280, training accuracy 0.58447
step 280, cost 234.716
step 280, change in cost 0.200424
step 290, training accuracy 0.585519
step 290, cost 234.516
step 290, change in cost 0.200119
step 300, training accuracy 0.586569
step 300, cost 234.316
step 300, change in cost 0.19989
step 310, training accuracy 0.586569
step 310, cost 234.117
step 310, change in cost 0.199554
step 320, training accuracy 0.587618
step 320, cost 233.918
step 320, change in cost 0.199387
step 330, training accuracy 0.588667
step 330, cost 233.719
step 330, change in cost 0.199036
step 340, training accuracy 0.590766
step 340, cost 233.52
step 340, change in cost 0.198807
step 350, training accuracy 0.590766
step 350, cost 233.321
step 350, change in cost 0.198486
step 360, training accuracy 0.592865
step 360, cost 233.123
step 360, change in cost 0.198257
step 370, training accuracy 0.594963
step 370, cost 232.925
step 370, change in cost 0.197952
step 380, training accuracy 0.594963
step 380, cost 232.727
step 380, change in cost 0.197754
step 390, training accuracy 0.594963
step 390, cost 232.53
step 390, change in cost 0.197357
step 400, training accuracy 0.594963
step 400, cost 232.333
step 400, change in cost 0.197189
step 410, training accuracy 0.597062
step 410, cost 232.136
step 410, change in cost 0.196777
step 420, training accuracy 0.598111
step 420, cost 231.939
step 420, change in cost 0.196564
step 430, training accuracy 0.599161
step 430, cost 231.743
step 430, change in cost 0.196213
step 440, training accuracy 0.598111
step 440, cost 231.547
step 440, change in cost 0.195953
step 450, training accuracy 0.601259
step 450, cost 231.352
step 450, change in cost 0.195694
step 460, training accuracy 0.602309
step 460, cost 231.156
step 460, change in cost 0.195389
step 470, training accuracy 0.603358
step 470, cost 230.961
step 470, change in cost 0.195129
step 480, training accuracy 0.605456
step 480, cost 230.766
step 480, change in cost 0.19487
step 490, training accuracy 0.605456
step 490, cost 230.571
step 490, change in cost 0.194626
step 500, training accuracy 0.608604
step 500, cost 230.377
step 500, change in cost 0.194214
step 510, training accuracy 0.610703
step 510, cost 230.183
step 510, change in cost 0.194061
step 520, training accuracy 0.610703
step 520, cost 229.989
step 520, change in cost 0.193741
step 530, training accuracy 0.611752
step 530, cost 229.796
step 530, change in cost 0.193604
step 540, training accuracy 0.612802
step 540, cost 229.603
step 540, change in cost 0.193283
step 550, training accuracy 0.612802
step 550, cost 229.41
step 550, change in cost 0.193039
step 560, training accuracy 0.6149
step 560, cost 229.217
step 560, change in cost 0.192841
step 570, training accuracy 0.61595
step 570, cost 229.024
step 570, change in cost 0.192612
step 580, training accuracy 0.618048
step 580, cost 228.832
step 580, change in cost 0.192383
step 590, training accuracy 0.619098
step 590, cost 228.639
step 590, change in cost 0.192322
step 600, training accuracy 0.620147
step 600, cost 228.447
step 600, change in cost 0.191971
step 610, training accuracy 0.620147
step 610, cost 228.255
step 610, change in cost 0.191956
step 620, training accuracy 0.621196
step 620, cost 228.064
step 620, change in cost 0.191742
step 630, training accuracy 0.622246
step 630, cost 227.872
step 630, change in cost 0.19165
step 640, training accuracy 0.623295
step 640, cost 227.681
step 640, change in cost 0.191528
step 650, training accuracy 0.623295
step 650, cost 227.489
step 650, change in cost 0.191406
step 660, training accuracy 0.623295
step 660, cost 227.298
step 660, change in cost 0.191345
step 670, training accuracy 0.624344
step 670, cost 227.107
step 670, change in cost 0.191208
step 680, training accuracy 0.624344
step 680, cost 226.915
step 680, change in cost 0.19133
step 690, training accuracy 0.622246
step 690, cost 226.724
step 690, change in cost 0.191177
step 700, training accuracy 0.622246
step 700, cost 226.533
step 700, change in cost 0.191269
step 710, training accuracy 0.623295
step 710, cost 226.342
step 710, change in cost 0.191284
step 720, training accuracy 0.625394
step 720, cost 226.15
step 720, change in cost 0.191254
step 730, training accuracy 0.625394
step 730, cost 225.959
step 730, change in cost 0.191422
step 740, training accuracy 0.627492
step 740, cost 225.767
step 740, change in cost 0.191467
step 750, training accuracy 0.628541
step 750, cost 225.576
step 750, change in cost 0.19162
step 760, training accuracy 0.631689
step 760, cost 225.384
step 760, change in cost 0.191757
step 770, training accuracy 0.631689
step 770, cost 225.192
step 770, change in cost 0.191895
step 780, training accuracy 0.633788
step 780, cost 225
step 780, change in cost 0.192093
step 790, training accuracy 0.634837
step 790, cost 224.808
step 790, change in cost 0.192383
step 800, training accuracy 0.636936
step 800, cost 224.615
step 800, change in cost 0.192535
step 810, training accuracy 0.636936
step 810, cost 224.422
step 810, change in cost 0.192841
step 820, training accuracy 0.639035
step 820, cost 224.229
step 820, change in cost 0.193161
step 830, training accuracy 0.640084
step 830, cost 224.035
step 830, change in cost 0.193604
step 840, training accuracy 0.640084
step 840, cost 223.842
step 840, change in cost 0.193848
step 850, training accuracy 0.639035
step 850, cost 223.647
step 850, change in cost 0.194244
step 860, training accuracy 0.639035
step 860, cost 223.453
step 860, change in cost 0.194702
step 870, training accuracy 0.639035
step 870, cost 223.258
step 870, change in cost 0.195175
step 880, training accuracy 0.640084
step 880, cost 223.062
step 880, change in cost 0.195511
step 890, training accuracy 0.641133
step 890, cost 222.866
step 890, change in cost 0.196121
step 900, training accuracy 0.641133
step 900, cost 222.669
step 900, change in cost 0.196777
step 910, training accuracy 0.641133
step 910, cost 222.472
step 910, change in cost 0.19722
step 920, training accuracy 0.641133
step 920, cost 222.274
step 920, change in cost 0.1978
step 930, training accuracy 0.642183
step 930, cost 222.076
step 930, change in cost 0.198547
step 940, training accuracy 0.642183
step 940, cost 221.876
step 940, change in cost 0.199142
step 950, training accuracy 0.643232
step 950, cost 221.677
step 950, change in cost 0.199783
step 960, training accuracy 0.643232
step 960, cost 221.476
step 960, change in cost 0.200562
step 970, training accuracy 0.643232
step 970, cost 221.275
step 970, change in cost 0.201355
step 980, training accuracy 0.644281
step 980, cost 221.073
step 980, change in cost 0.202194
step 990, training accuracy 0.644281
step 990, cost 220.87
step 990, change in cost 0.202896
step 1000, training accuracy 0.645331
step 1000, cost 220.666
step 1000, change in cost 0.203842
step 1010, training accuracy 0.647429
step 1010, cost 220.461
step 1010, change in cost 0.204697
step 1020, training accuracy 0.648479
step 1020, cost 220.255
step 1020, change in cost 0.205582
step 1030, training accuracy 0.648479
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step 1030, change in cost 0.206589
step 1040, training accuracy 0.649528
step 1040, cost 219.841
step 1040, change in cost 0.207504
step 1050, training accuracy 0.649528
step 1050, cost 219.633
step 1050, change in cost 0.208618
step 1060, training accuracy 0.650577
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step 1060, change in cost 0.209625
step 1070, training accuracy 0.649528
step 1070, cost 219.212
step 1070, change in cost 0.210709
step 1080, training accuracy 0.649528
step 1080, cost 219.001
step 1080, change in cost 0.211792
step 1090, training accuracy 0.650577
step 1090, cost 218.788
step 1090, change in cost 0.212997
step 1100, training accuracy 0.651626
step 1100, cost 218.574
step 1100, change in cost 0.214142
step 1110, training accuracy 0.654774
step 1110, cost 218.358
step 1110, change in cost 0.215363
step 1120, training accuracy 0.654774
step 1120, cost 218.142
step 1120, change in cost 0.216629
step 1130, training accuracy 0.657922
step 1130, cost 217.924
step 1130, change in cost 0.21785
step 1140, training accuracy 0.660021
step 1140, cost 217.704
step 1140, change in cost 0.219193
step 1150, training accuracy 0.660021
step 1150, cost 217.484
step 1150, change in cost 0.22049
step 1160, training accuracy 0.660021
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step 1160, change in cost 0.221832
step 1170, training accuracy 0.66107
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step 1170, change in cost 0.223282
step 1180, training accuracy 0.66107
step 1180, cost 216.814
step 1180, change in cost 0.224655
step 1190, training accuracy 0.66212
step 1190, cost 216.588
step 1190, change in cost 0.226257
step 1200, training accuracy 0.66212
step 1200, cost 216.36
step 1200, change in cost 0.227676
step 1210, training accuracy 0.66212
step 1210, cost 216.131
step 1210, change in cost 0.229004
step 1220, training accuracy 0.663169
step 1220, cost 215.901
step 1220, change in cost 0.230698
step 1230, training accuracy 0.663169
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step 1230, change in cost 0.232224
step 1240, training accuracy 0.663169
step 1240, cost 215.435
step 1240, change in cost 0.233795
step 1250, training accuracy 0.664218
step 1250, cost 215.199
step 1250, change in cost 0.235443
step 1260, training accuracy 0.665268
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step 1260, change in cost 0.237061
step 1270, training accuracy 0.664218
step 1270, cost 214.723
step 1270, change in cost 0.238693
step 1280, training accuracy 0.664218
step 1280, cost 214.483
step 1280, change in cost 0.240387
step 1290, training accuracy 0.664218
step 1290, cost 214.241
step 1290, change in cost 0.242081
step 1300, training accuracy 0.665268
step 1300, cost 213.997
step 1300, change in cost 0.24379
step 1310, training accuracy 0.666317
step 1310, cost 213.752
step 1310, change in cost 0.245483
step 1320, training accuracy 0.670514
step 1320, cost 213.504
step 1320, change in cost 0.247314
step 1330, training accuracy 0.671564
step 1330, cost 213.255
step 1330, change in cost 0.249039
step 1340, training accuracy 0.672613
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step 1340, change in cost 0.250732
step 1350, training accuracy 0.673662
step 1350, cost 212.752
step 1350, change in cost 0.252579
step 1360, training accuracy 0.673662
step 1360, cost 212.498
step 1360, change in cost 0.254333
step 1370, training accuracy 0.673662
step 1370, cost 212.242
step 1370, change in cost 0.256073
step 1380, training accuracy 0.67681
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step 1380, change in cost 0.257965
step 1390, training accuracy 0.67681
step 1390, cost 211.724
step 1390, change in cost 0.259598
step 1400, training accuracy 0.679958
step 1400, cost 211.463
step 1400, change in cost 0.261475
step 1410, training accuracy 0.684155
step 1410, cost 211.199
step 1410, change in cost 0.26329
step 1420, training accuracy 0.686254
step 1420, cost 210.934
step 1420, change in cost 0.264984
step 1430, training accuracy 0.687303
step 1430, cost 210.668
step 1430, change in cost 0.266724
step 1440, training accuracy 0.688353
step 1440, cost 210.399
step 1440, change in cost 0.268509
step 1450, training accuracy 0.688353
step 1450, cost 210.129
step 1450, change in cost 0.270233
step 1460, training accuracy 0.690451
step 1460, cost 209.857
step 1460, change in cost 0.271912
step 1470, training accuracy 0.695698
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step 1470, change in cost 0.273697
step 1480, training accuracy 0.694649
step 1480, cost 209.308
step 1480, change in cost 0.275284
step 1490, training accuracy 0.695698
step 1490, cost 209.031
step 1490, change in cost 0.276917
step 1500, training accuracy 0.697796
step 1500, cost 208.752
step 1500, change in cost 0.278595
step 1510, training accuracy 0.698846
step 1510, cost 208.472
step 1510, change in cost 0.280029
step 1520, training accuracy 0.698846
step 1520, cost 208.191
step 1520, change in cost 0.281616
step 1530, training accuracy 0.698846
step 1530, cost 207.908
step 1530, change in cost 0.283173
step 1540, training accuracy 0.699895
step 1540, cost 207.623
step 1540, change in cost 0.284546
step 1550, training accuracy 0.701994
step 1550, cost 207.337
step 1550, change in cost 0.286041
step 1560, training accuracy 0.701994
step 1560, cost 207.05
step 1560, change in cost 0.287399
step 1570, training accuracy 0.703043
step 1570, cost 206.761
step 1570, change in cost 0.288696
step 1580, training accuracy 0.703043
step 1580, cost 206.471
step 1580, change in cost 0.289963
step 1590, training accuracy 0.703043
step 1590, cost 206.18
step 1590, change in cost 0.291199
step 1600, training accuracy 0.703043
step 1600, cost 205.887
step 1600, change in cost 0.292435
step 1610, training accuracy 0.704092
step 1610, cost 205.594
step 1610, change in cost 0.293533
step 1620, training accuracy 0.704092
step 1620, cost 205.299
step 1620, change in cost 0.294617
step 1630, training accuracy 0.703043
step 1630, cost 205.003
step 1630, change in cost 0.2957
step 1640, training accuracy 0.703043
step 1640, cost 204.707
step 1640, change in cost 0.296478
step 1650, training accuracy 0.705142
step 1650, cost 204.409
step 1650, change in cost 0.297607
step 1660, training accuracy 0.705142
step 1660, cost 204.111
step 1660, change in cost 0.298416
step 1670, training accuracy 0.705142
step 1670, cost 203.812
step 1670, change in cost 0.29921
step 1680, training accuracy 0.70724
step 1680, cost 203.512
step 1680, change in cost 0.300003
step 1690, training accuracy 0.710388
step 1690, cost 203.211
step 1690, change in cost 0.300766
step 1700, training accuracy 0.712487
step 1700, cost 202.91
step 1700, change in cost 0.301392
step 1710, training accuracy 0.713536
step 1710, cost 202.608
step 1710, change in cost 0.302017
step 1720, training accuracy 0.713536
step 1720, cost 202.305
step 1720, change in cost 0.302521
step 1730, training accuracy 0.714586
step 1730, cost 202.002
step 1730, change in cost 0.303207
step 1740, training accuracy 0.714586
step 1740, cost 201.698
step 1740, change in cost 0.303604
step 1750, training accuracy 0.714586
step 1750, cost 201.394
step 1750, change in cost 0.304077
step 1760, training accuracy 0.717734
step 1760, cost 201.09
step 1760, change in cost 0.304504
step 1770, training accuracy 0.717734
step 1770, cost 200.785
step 1770, change in cost 0.30484
step 1780, training accuracy 0.717734
step 1780, cost 200.479
step 1780, change in cost 0.305298
step 1790, training accuracy 0.719832
step 1790, cost 200.174
step 1790, change in cost 0.305496
step 1800, training accuracy 0.720881
step 1800, cost 199.868
step 1800, change in cost 0.305908
step 1810, training accuracy 0.721931
step 1810, cost 199.562
step 1810, change in cost 0.306107
step 1820, training accuracy 0.72298
step 1820, cost 199.256
step 1820, change in cost 0.30629
step 1830, training accuracy 0.724029
step 1830, cost 198.949
step 1830, change in cost 0.306488
step 1840, training accuracy 0.725079
step 1840, cost 198.642
step 1840, change in cost 0.306824
step 1850, training accuracy 0.726128
step 1850, cost 198.335
step 1850, change in cost 0.3069
step 1860, training accuracy 0.726128
step 1860, cost 198.028
step 1860, change in cost 0.307144
step 1870, training accuracy 0.727177
step 1870, cost 197.721
step 1870, change in cost 0.307236
step 1880, training accuracy 0.728227
step 1880, cost 197.414
step 1880, change in cost 0.307434
step 1890, training accuracy 0.728227
step 1890, cost 197.106
step 1890, change in cost 0.307556
step 1900, training accuracy 0.729276
step 1900, cost 196.798
step 1900, change in cost 0.307617
step 1910, training accuracy 0.730325
step 1910, cost 196.491
step 1910, change in cost 0.307831
step 1920, training accuracy 0.732424
step 1920, cost 196.183
step 1920, change in cost 0.307968
step 1930, training accuracy 0.733473
step 1930, cost 195.875
step 1930, change in cost 0.308029
step 1940, training accuracy 0.735572
step 1940, cost 195.567
step 1940, change in cost 0.308136
step 1950, training accuracy 0.737671
step 1950, cost 195.258
step 1950, change in cost 0.308334
step 1960, training accuracy 0.737671
step 1960, cost 194.95
step 1960, change in cost 0.308426
step 1970, training accuracy 0.739769
step 1970, cost 194.641
step 1970, change in cost 0.308609
step 1980, training accuracy 0.739769
step 1980, cost 194.332
step 1980, change in cost 0.308701
step 1990, training accuracy 0.740818
step 1990, cost 194.024
step 1990, change in cost 0.308838
step 2000, training accuracy 0.741868
step 2000, cost 193.715
step 2000, change in cost 0.309036
step 2010, training accuracy 0.741868
step 2010, cost 193.406
step 2010, change in cost 0.309082
step 2020, training accuracy 0.741868
step 2020, cost 193.096
step 2020, change in cost 0.30928
step 2030, training accuracy 0.741868
step 2030, cost 192.787
step 2030, change in cost 0.309586
step 2040, training accuracy 0.741868
step 2040, cost 192.477
step 2040, change in cost 0.309631
step 2050, training accuracy 0.741868
step 2050, cost 192.167
step 2050, change in cost 0.309891
step 2060, training accuracy 0.743966
step 2060, cost 191.857
step 2060, change in cost 0.310104
step 2070, training accuracy 0.743966
step 2070, cost 191.547
step 2070, change in cost 0.310287
step 2080, training accuracy 0.743966
step 2080, cost 191.236
step 2080, change in cost 0.310455
step 2090, training accuracy 0.743966
step 2090, cost 190.926
step 2090, change in cost 0.310715
step 2100, training accuracy 0.743966
step 2100, cost 190.615
step 2100, change in cost 0.310959
step 2110, training accuracy 0.745016
step 2110, cost 190.303
step 2110, change in cost 0.311218
step 2120, training accuracy 0.745016
step 2120, cost 189.992
step 2120, change in cost 0.311554
step 2130, training accuracy 0.745016
step 2130, cost 189.68
step 2130, change in cost 0.311676
step 2140, training accuracy 0.746065
step 2140, cost 189.368
step 2140, change in cost 0.312042
step 2150, training accuracy 0.746065
step 2150, cost 189.056
step 2150, change in cost 0.312286
step 2160, training accuracy 0.746065
step 2160, cost 188.743
step 2160, change in cost 0.312592
step 2170, training accuracy 0.746065
step 2170, cost 188.43
step 2170, change in cost 0.312897
step 2180, training accuracy 0.748164
step 2180, cost 188.117
step 2180, change in cost 0.313187
step 2190, training accuracy 0.748164
step 2190, cost 187.804
step 2190, change in cost 0.313553
step 2200, training accuracy 0.749213
step 2200, cost 187.49
step 2200, change in cost 0.313797
step 2210, training accuracy 0.749213
step 2210, cost 187.176
step 2210, change in cost 0.314194
step 2220, training accuracy 0.749213
step 2220, cost 186.861
step 2220, change in cost 0.314484
step 2230, training accuracy 0.749213
step 2230, cost 186.546
step 2230, change in cost 0.31485
step 2240, training accuracy 0.749213
step 2240, cost 186.231
step 2240, change in cost 0.315155
step 2250, training accuracy 0.749213
step 2250, cost 185.916
step 2250, change in cost 0.315552
step 2260, training accuracy 0.750262
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step 2260, change in cost 0.315857
step 2270, training accuracy 0.751312
step 2270, cost 185.283
step 2270, change in cost 0.316269
step 2280, training accuracy 0.75341
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step 2280, change in cost 0.316589
step 2290, training accuracy 0.75446
step 2290, cost 184.65
step 2290, change in cost 0.31694
step 2300, training accuracy 0.75446
step 2300, cost 184.333
step 2300, change in cost 0.317322
step 2310, training accuracy 0.75446
step 2310, cost 184.015
step 2310, change in cost 0.317703
step 2320, training accuracy 0.755509
step 2320, cost 183.697
step 2320, change in cost 0.318024
step 2330, training accuracy 0.759706
step 2330, cost 183.378
step 2330, change in cost 0.318497
step 2340, training accuracy 0.759706
step 2340, cost 183.06
step 2340, change in cost 0.318771
step 2350, training accuracy 0.759706
step 2350, cost 182.74
step 2350, change in cost 0.319168
step 2360, training accuracy 0.759706
step 2360, cost 182.421
step 2360, change in cost 0.31955
step 2370, training accuracy 0.759706
step 2370, cost 182.101
step 2370, change in cost 0.319931
step 2380, training accuracy 0.760755
step 2380, cost 181.781
step 2380, change in cost 0.320251
step 2390, training accuracy 0.759706
step 2390, cost 181.46
step 2390, change in cost 0.320724
step 2400, training accuracy 0.759706
step 2400, cost 181.139
step 2400, change in cost 0.321045
step 2410, training accuracy 0.761805
step 2410, cost 180.818
step 2410, change in cost 0.32135
step 2420, training accuracy 0.761805
step 2420, cost 180.496
step 2420, change in cost 0.321869
step 2430, training accuracy 0.761805
step 2430, cost 180.174
step 2430, change in cost 0.322098
step 2440, training accuracy 0.761805
step 2440, cost 179.851
step 2440, change in cost 0.322495
step 2450, training accuracy 0.762854
step 2450, cost 179.528
step 2450, change in cost 0.322891
step 2460, training accuracy 0.762854
step 2460, cost 179.205
step 2460, change in cost 0.323212
step 2470, training accuracy 0.764953
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step 2470, change in cost 0.323532
step 2480, training accuracy 0.766002
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step 2480, change in cost 0.32399
step 2490, training accuracy 0.766002
step 2490, cost 178.233
step 2490, change in cost 0.324326
step 2500, training accuracy 0.766002
step 2500, cost 177.909
step 2500, change in cost 0.324615
step 2510, training accuracy 0.767051
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step 2510, change in cost 0.324875
step 2520, training accuracy 0.767051
step 2520, cost 177.258
step 2520, change in cost 0.325317
step 2530, training accuracy 0.768101
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step 2530, change in cost 0.325638
step 2540, training accuracy 0.770199
step 2540, cost 176.607
step 2540, change in cost 0.325958
step 2550, training accuracy 0.771249
step 2550, cost 176.28
step 2550, change in cost 0.326309
step 2560, training accuracy 0.773347
step 2560, cost 175.954
step 2560, change in cost 0.326599
step 2570, training accuracy 0.773347
step 2570, cost 175.627
step 2570, change in cost 0.326874
step 2580, training accuracy 0.773347
step 2580, cost 175.3
step 2580, change in cost 0.327225
step 2590, training accuracy 0.775446
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step 2590, change in cost 0.327515
step 2600, training accuracy 0.775446
step 2600, cost 174.644
step 2600, change in cost 0.32785
step 2610, training accuracy 0.774397
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step 2610, change in cost 0.32811
step 2620, training accuracy 0.774397
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step 2620, change in cost 0.328369
step 2630, training accuracy 0.775446
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step 2630, change in cost 0.328674
step 2640, training accuracy 0.775446
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step 2640, change in cost 0.328964
step 2650, training accuracy 0.775446
step 2650, cost 173.001
step 2650, change in cost 0.329193
step 2660, training accuracy 0.775446
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step 2660, change in cost 0.329468
step 2670, training accuracy 0.777545
step 2670, cost 172.342
step 2670, change in cost 0.329758
step 2680, training accuracy 0.777545
step 2680, cost 172.012
step 2680, change in cost 0.330017
step 2690, training accuracy 0.777545
step 2690, cost 171.682
step 2690, change in cost 0.3302
step 2700, training accuracy 0.777545
step 2700, cost 171.351
step 2700, change in cost 0.330429
step 2710, training accuracy 0.778594
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step 2710, change in cost 0.330734
step 2720, training accuracy 0.779643
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step 2720, change in cost 0.330902
step 2730, training accuracy 0.780693
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step 2730, change in cost 0.331131
step 2740, training accuracy 0.780693
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step 2740, change in cost 0.331329
step 2750, training accuracy 0.782791
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step 2750, change in cost 0.331497
step 2760, training accuracy 0.782791
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step 2760, change in cost 0.331711
step 2770, training accuracy 0.78489
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step 2770, change in cost 0.331924
step 2780, training accuracy 0.78489
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step 2780, change in cost 0.332123
step 2790, training accuracy 0.78489
step 2790, cost 168.368
step 2790, change in cost 0.33223
step 2800, training accuracy 0.785939
step 2800, cost 168.035
step 2800, change in cost 0.332474
step 2810, training accuracy 0.785939
step 2810, cost 167.703
step 2810, change in cost 0.332626
step 2820, training accuracy 0.786988
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step 2820, change in cost 0.332748
step 2830, training accuracy 0.788038
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step 2830, change in cost 0.33287
step 2840, training accuracy 0.789087
step 2840, cost 166.704
step 2840, change in cost 0.333023
step 2850, training accuracy 0.789087
step 2850, cost 166.371
step 2850, change in cost 0.333099
step 2860, training accuracy 0.790136
step 2860, cost 166.037
step 2860, change in cost 0.333374
step 2870, training accuracy 0.791186
step 2870, cost 165.704
step 2870, change in cost 0.333405
step 2880, training accuracy 0.792235
step 2880, cost 165.371
step 2880, change in cost 0.333481
step 2890, training accuracy 0.792235
step 2890, cost 165.037
step 2890, change in cost 0.333649
step 2900, training accuracy 0.794334
step 2900, cost 164.703
step 2900, change in cost 0.333725
step 2910, training accuracy 0.795383
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step 2910, change in cost 0.333817
step 2920, training accuracy 0.798531
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step 2920, change in cost 0.333832
step 2930, training accuracy 0.79958
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step 2930, change in cost 0.333969
step 2940, training accuracy 0.80063
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step 2940, change in cost 0.334061
step 2950, training accuracy 0.80063
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step 2950, change in cost 0.334106
step 2960, training accuracy 0.80063
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step 2960, change in cost 0.334061
step 2970, training accuracy 0.801679
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step 2970, change in cost 0.334167
step 2980, training accuracy 0.801679
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step 2980, change in cost 0.334167
step 2990, training accuracy 0.802728
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step 2990, change in cost 0.334274
step 3000, training accuracy 0.803778
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step 3000, change in cost 0.334259
step 3010, training accuracy 0.803778
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step 3010, change in cost 0.334274
step 3020, training accuracy 0.805876
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step 3020, change in cost 0.334274
step 3030, training accuracy 0.806925
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step 3030, change in cost 0.334229
step 3040, training accuracy 0.807975
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step 3040, change in cost 0.334274
step 3050, training accuracy 0.809024
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step 3050, change in cost 0.334274
step 3060, training accuracy 0.809024
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step 3060, change in cost 0.334152
step 3070, training accuracy 0.809024
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step 3070, change in cost 0.334183
step 3080, training accuracy 0.811123
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step 3080, change in cost 0.334167
step 3090, training accuracy 0.811123
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step 3090, change in cost 0.334061
step 3100, training accuracy 0.812172
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step 3100, change in cost 0.334076
step 3110, training accuracy 0.813221
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step 3110, change in cost 0.334015
step 3120, training accuracy 0.813221
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step 3120, change in cost 0.333923
step 3130, training accuracy 0.813221
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step 3130, change in cost 0.333878
step 3140, training accuracy 0.814271
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step 3140, change in cost 0.333694
step 3150, training accuracy 0.81532
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step 3150, change in cost 0.333694
step 3160, training accuracy 0.816369
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step 3160, change in cost 0.333588
step 3170, training accuracy 0.816369
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step 3170, change in cost 0.333496
step 3180, training accuracy 0.819517
step 3180, cost 155.351
step 3180, change in cost 0.333298
step 3190, training accuracy 0.819517
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step 3190, change in cost 0.333267
step 3200, training accuracy 0.820567
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step 3200, change in cost 0.333054
step 3210, training accuracy 0.821616
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step 3210, change in cost 0.333008
step 3220, training accuracy 0.822665
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step 3220, change in cost 0.332794
step 3230, training accuracy 0.822665
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step 3230, change in cost 0.332657
step 3240, training accuracy 0.822665
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step 3240, change in cost 0.332611
step 3250, training accuracy 0.822665
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step 3250, change in cost 0.332321
step 3260, training accuracy 0.822665
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step 3260, change in cost 0.332245
step 3270, training accuracy 0.823715
step 3270, cost 152.357
step 3270, change in cost 0.331924
step 3280, training accuracy 0.823715
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step 3280, change in cost 0.331818
step 3290, training accuracy 0.824764
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step 3290, change in cost 0.33168
step 3300, training accuracy 0.823715
step 3300, cost 151.362
step 3300, change in cost 0.331436
step 3310, training accuracy 0.823715
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step 3310, change in cost 0.331207
step 3320, training accuracy 0.824764
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step 3320, change in cost 0.331039
step 3330, training accuracy 0.825813
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step 3330, change in cost 0.330795
step 3340, training accuracy 0.826863
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step 3340, change in cost 0.330566
step 3350, training accuracy 0.828961
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step 3350, change in cost 0.330353
step 3360, training accuracy 0.83001
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step 3360, change in cost 0.330124
step 3370, training accuracy 0.83001
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step 3370, change in cost 0.329865
step 3380, training accuracy 0.83001
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step 3380, change in cost 0.329559
step 3390, training accuracy 0.83001
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step 3390, change in cost 0.329346
step 3400, training accuracy 0.83106
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step 3400, change in cost 0.329071
step 3410, training accuracy 0.83106
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step 3410, change in cost 0.328796
step 3420, training accuracy 0.832109
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step 3420, change in cost 0.328537
step 3430, training accuracy 0.832109
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step 3430, change in cost 0.328217
step 3440, training accuracy 0.832109
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step 3440, change in cost 0.327942
step 3450, training accuracy 0.832109
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step 3450, change in cost 0.327652
step 3460, training accuracy 0.834208
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step 3460, change in cost 0.327332
step 3470, training accuracy 0.834208
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step 3470, change in cost 0.327026
step 3480, training accuracy 0.834208
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step 3480, change in cost 0.326675
step 3490, training accuracy 0.835257
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step 3490, change in cost 0.326355
step 3500, training accuracy 0.835257
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step 3500, change in cost 0.32608
step 3510, training accuracy 0.835257
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step 3510, change in cost 0.325745
step 3520, training accuracy 0.835257
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step 3520, change in cost 0.325333
step 3530, training accuracy 0.836306
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step 3530, change in cost 0.325043
step 3540, training accuracy 0.837356
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step 3540, change in cost 0.324646
step 3550, training accuracy 0.837356
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step 3550, change in cost 0.324234
step 3560, training accuracy 0.837356
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step 3560, change in cost 0.323975
step 3570, training accuracy 0.837356
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step 3570, change in cost 0.323532
step 3580, training accuracy 0.838405
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step 3580, change in cost 0.323166
step 3590, training accuracy 0.838405
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step 3590, change in cost 0.322815
step 3600, training accuracy 0.839454
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step 3600, change in cost 0.322342
step 3610, training accuracy 0.839454
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step 3610, change in cost 0.322021
step 3620, training accuracy 0.840504
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step 3620, change in cost 0.321625
step 3630, training accuracy 0.841553
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step 3630, change in cost 0.321152
step 3640, training accuracy 0.842602
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step 3640, change in cost 0.320709
step 3650, training accuracy 0.842602
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step 3650, change in cost 0.320328
step 3660, training accuracy 0.842602
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step 3660, change in cost 0.319962
step 3670, training accuracy 0.842602
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step 3670, change in cost 0.319412
step 3680, training accuracy 0.842602
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step 3680, change in cost 0.319061
step 3690, training accuracy 0.842602
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step 3690, change in cost 0.318649
step 3700, training accuracy 0.843652
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step 3700, change in cost 0.31813
step 3710, training accuracy 0.844701
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step 3710, change in cost 0.317703
step 3720, training accuracy 0.844701
step 3720, cost 137.709
step 3720, change in cost 0.317261
step 3730, training accuracy 0.844701
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step 3730, change in cost 0.316803
step 3740, training accuracy 0.84575
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step 3740, change in cost 0.316315
step 3750, training accuracy 0.8468
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step 3750, change in cost 0.315796
step 3760, training accuracy 0.847849
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step 3760, change in cost 0.315292
step 3770, training accuracy 0.847849
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step 3770, change in cost 0.314911
step 3780, training accuracy 0.847849
step 3780, cost 135.815
step 3780, change in cost 0.314392
step 3790, training accuracy 0.847849
step 3790, cost 135.501
step 3790, change in cost 0.313858
step 3800, training accuracy 0.847849
step 3800, cost 135.188
step 3800, change in cost 0.3134
step 3810, training accuracy 0.849948
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step 3810, change in cost 0.312866
step 3820, training accuracy 0.849948
step 3820, cost 134.563
step 3820, change in cost 0.312347
step 3830, training accuracy 0.850997
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step 3830, change in cost 0.311859
step 3840, training accuracy 0.850997
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step 3840, change in cost 0.31134
step 3850, training accuracy 0.852046
step 3850, cost 133.629
step 3850, change in cost 0.310822
step 3860, training accuracy 0.853095
step 3860, cost 133.318
step 3860, change in cost 0.310272
step 3870, training accuracy 0.853095
step 3870, cost 133.009
step 3870, change in cost 0.309784
step 3880, training accuracy 0.853095
step 3880, cost 132.699
step 3880, change in cost 0.309189
step 3890, training accuracy 0.854145
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step 3890, change in cost 0.308731
step 3900, training accuracy 0.855194
step 3900, cost 132.083
step 3900, change in cost 0.30809
step 3910, training accuracy 0.855194
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step 3910, change in cost 0.307602
step 3920, training accuracy 0.855194
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step 3920, change in cost 0.306976
step 3930, training accuracy 0.856243
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step 3930, change in cost 0.306549
step 3940, training accuracy 0.856243
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step 3940, change in cost 0.305832
step 3950, training accuracy 0.856243
step 3950, cost 130.55
step 3950, change in cost 0.305374
step 3960, training accuracy 0.856243
step 3960, cost 130.245
step 3960, change in cost 0.304794
step 3970, training accuracy 0.856243
step 3970, cost 129.941
step 3970, change in cost 0.304199
step 3980, training accuracy 0.857293
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step 3980, change in cost 0.303574
step 3990, training accuracy 0.859391
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step 3990, change in cost 0.303055
step 4000, training accuracy 0.859391
step 4000, cost 129.032
step 4000, change in cost 0.302475
step 4010, training accuracy 0.86149
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step 4010, change in cost 0.301865
step 4020, training accuracy 0.86149
step 4020, cost 128.429
step 4020, change in cost 0.3013
step 4030, training accuracy 0.86149
step 4030, cost 128.128
step 4030, change in cost 0.300629
step 4040, training accuracy 0.86149
step 4040, cost 127.828
step 4040, change in cost 0.300072
step 4050, training accuracy 0.86149
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step 4050, change in cost 0.299515
step 4060, training accuracy 0.862539
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step 4060, change in cost 0.298866
step 4070, training accuracy 0.862539
step 4070, cost 126.932
step 4070, change in cost 0.298294
step 4080, training accuracy 0.863589
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step 4080, change in cost 0.297615
step 4090, training accuracy 0.865687
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step 4090, change in cost 0.297081
step 4100, training accuracy 0.865687
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step 4100, change in cost 0.296379
step 4110, training accuracy 0.865687
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step 4110, change in cost 0.295822
step 4120, training accuracy 0.866737
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step 4120, change in cost 0.295158
step 4130, training accuracy 0.866737
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step 4130, change in cost 0.294556
step 4140, training accuracy 0.867786
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step 4140, change in cost 0.293945
step 4150, training accuracy 0.867786
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step 4150, change in cost 0.293251
step 4160, training accuracy 0.867786
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step 4160, change in cost 0.292618
step 4170, training accuracy 0.868835
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step 4170, change in cost 0.291992
step 4180, training accuracy 0.868835
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step 4180, change in cost 0.291382
step 4190, training accuracy 0.869885
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step 4190, change in cost 0.29071
step 4200, training accuracy 0.869885
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step 4200, change in cost 0.290077
step 4210, training accuracy 0.869885
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step 4210, change in cost 0.289398
step 4220, training accuracy 0.869885
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step 4220, change in cost 0.288773
step 4230, training accuracy 0.870934
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step 4230, change in cost 0.288116
step 4240, training accuracy 0.871983
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step 4240, change in cost 0.287476
step 4250, training accuracy 0.873033
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step 4250, change in cost 0.286766
step 4260, training accuracy 0.874082
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step 4260, change in cost 0.286133
step 4270, training accuracy 0.874082
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step 4270, change in cost 0.2855
step 4280, training accuracy 0.874082
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step 4280, change in cost 0.284821
step 4290, training accuracy 0.874082
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step 4290, change in cost 0.284126
step 4300, training accuracy 0.874082
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step 4300, change in cost 0.283401
step 4310, training accuracy 0.875131
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step 4310, change in cost 0.282799
step 4320, training accuracy 0.875131
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step 4320, change in cost 0.282135
step 4330, training accuracy 0.875131
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step 4330, change in cost 0.28141
step 4340, training accuracy 0.875131
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step 4340, change in cost 0.280739
step 4350, training accuracy 0.875131
step 4350, cost 118.839
step 4350, change in cost 0.280144
step 4360, training accuracy 0.87618
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step 4360, change in cost 0.279358
step 4370, training accuracy 0.87618
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step 4370, change in cost 0.278732
step 4380, training accuracy 0.87618
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step 4380, change in cost 0.278023
step 4390, training accuracy 0.87723
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step 4390, change in cost 0.277328
step 4400, training accuracy 0.87723
step 4400, cost 117.449
step 4400, change in cost 0.276657
step 4410, training accuracy 0.87723
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step 4410, change in cost 0.275948
step 4420, training accuracy 0.87723
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step 4420, change in cost 0.275322
step 4430, training accuracy 0.878279
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step 4430, change in cost 0.274559
step 4440, training accuracy 0.879328
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step 4440, change in cost 0.273888
step 4450, training accuracy 0.880378
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step 4450, change in cost 0.273163
step 4460, training accuracy 0.880378
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step 4460, change in cost 0.272491
step 4470, training accuracy 0.880378
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step 4470, change in cost 0.27179
step 4480, training accuracy 0.880378
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step 4480, change in cost 0.271103
step 4490, training accuracy 0.880378
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step 4490, change in cost 0.270409
step 4500, training accuracy 0.881427
step 4500, cost 114.721
step 4500, change in cost 0.269737
step 4510, training accuracy 0.881427
step 4510, cost 114.452
step 4510, change in cost 0.268951
step 4520, training accuracy 0.881427
step 4520, cost 114.184
step 4520, change in cost 0.268318
step 4530, training accuracy 0.882476
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step 4530, change in cost 0.267593
step 4540, training accuracy 0.882476
step 4540, cost 113.649
step 4540, change in cost 0.266884
step 4550, training accuracy 0.882476
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step 4550, change in cost 0.266205
step 4560, training accuracy 0.884575
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step 4560, change in cost 0.265434
step 4570, training accuracy 0.884575
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step 4570, change in cost 0.26474
step 4580, training accuracy 0.885624
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step 4580, change in cost 0.264107
step 4590, training accuracy 0.885624
step 4590, cost 112.325
step 4590, change in cost 0.263298
step 4600, training accuracy 0.887723
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step 4600, change in cost 0.26265
step 4610, training accuracy 0.888772
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step 4610, change in cost 0.26194
step 4620, training accuracy 0.888772
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step 4620, change in cost 0.261177
step 4630, training accuracy 0.889822
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step 4630, change in cost 0.260513
step 4640, training accuracy 0.889822
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step 4640, change in cost 0.259796
step 4650, training accuracy 0.889822
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step 4650, change in cost 0.259064
step 4660, training accuracy 0.888772
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step 4660, change in cost 0.258385
step 4670, training accuracy 0.888772
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step 4670, change in cost 0.257652
step 4680, training accuracy 0.888772
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step 4680, change in cost 0.256889
step 4690, training accuracy 0.888772
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step 4690, change in cost 0.256271
step 4700, training accuracy 0.889822
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step 4700, change in cost 0.255493
step 4710, training accuracy 0.889822
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step 4710, change in cost 0.254799
step 4720, training accuracy 0.889822
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step 4720, change in cost 0.254105
step 4730, training accuracy 0.889822
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step 4730, change in cost 0.253357
step 4740, training accuracy 0.889822
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step 4740, change in cost 0.252686
step 4750, training accuracy 0.890871
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step 4750, change in cost 0.251923
step 4760, training accuracy 0.890871
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step 4760, change in cost 0.251228
step 4770, training accuracy 0.89192
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step 4770, change in cost 0.250519
step 4780, training accuracy 0.89192
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step 4780, change in cost 0.249786
step 4790, training accuracy 0.89192
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step 4790, change in cost 0.249092
step 4800, training accuracy 0.89192
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step 4800, change in cost 0.24836
step 4810, training accuracy 0.89192
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step 4810, change in cost 0.247688
step 4820, training accuracy 0.89192
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step 4820, change in cost 0.24691
step 4830, training accuracy 0.89192
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step 4830, change in cost 0.246246
step 4840, training accuracy 0.89192
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step 4850, training accuracy 0.89192
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step 4850, change in cost 0.244789
step 4860, training accuracy 0.89297
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step 4870, training accuracy 0.895068
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step 4880, training accuracy 0.896118
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step 4890, training accuracy 0.896118
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step 4890, change in cost 0.241982
step 4900, training accuracy 0.896118
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step 4910, training accuracy 0.896118
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step 4920, training accuracy 0.896118
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step 4930, training accuracy 0.896118
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step 4930, change in cost 0.239143
step 4940, training accuracy 0.897167
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step 4940, change in cost 0.238396
step 4950, training accuracy 0.897167
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step 4960, training accuracy 0.897167
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step 4960, change in cost 0.236908
step 4970, training accuracy 0.897167
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step 4980, training accuracy 0.897167
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step 4990, training accuracy 0.897167
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step 5000, training accuracy 0.898216
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step 5020, training accuracy 0.899265
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step 5030, training accuracy 0.899265
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step 5040, training accuracy 0.900315
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step 5050, training accuracy 0.900315
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step 5060, training accuracy 0.900315
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step 5070, training accuracy 0.900315
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step 5080, training accuracy 0.900315
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step 5090, training accuracy 0.900315
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step 5100, training accuracy 0.901364
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step 5110, training accuracy 0.902413
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step 5120, training accuracy 0.902413
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step 5130, training accuracy 0.903463
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step 5140, training accuracy 0.903463
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step 5150, training accuracy 0.904512
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step 5160, training accuracy 0.904512
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step 5170, training accuracy 0.904512
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step 5180, training accuracy 0.904512
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step 5190, training accuracy 0.904512
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step 5200, training accuracy 0.905561
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step 5210, training accuracy 0.905561
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step 5220, training accuracy 0.905561
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step 5230, training accuracy 0.905561
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step 5240, training accuracy 0.906611
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step 5250, training accuracy 0.906611
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step 5260, training accuracy 0.906611
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step 5270, training accuracy 0.908709
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step 5280, training accuracy 0.908709
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step 5290, training accuracy 0.908709
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step 5300, training accuracy 0.908709
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step 5310, training accuracy 0.909759
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step 5320, training accuracy 0.910808
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step 5320, change in cost 0.212067
step 5330, training accuracy 0.910808
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step 5330, change in cost 0.211449
step 5340, training accuracy 0.911857
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step 5350, training accuracy 0.911857
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step 5350, change in cost 0.210152
step 5360, training accuracy 0.911857
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step 5370, training accuracy 0.911857
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step 5390, training accuracy 0.912907
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step 5400, training accuracy 0.912907
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step 5410, training accuracy 0.912907
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step 5430, training accuracy 0.913956
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step 5440, training accuracy 0.913956
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step 5440, change in cost 0.204185
step 5450, training accuracy 0.913956
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step 5450, change in cost 0.203514
step 5460, training accuracy 0.913956
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step 5470, training accuracy 0.915005
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step 5480, training accuracy 0.915005
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step 5490, training accuracy 0.915005
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step 5500, training accuracy 0.916055
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step 5500, change in cost 0.200279
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step 5520, training accuracy 0.916055
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step 5520, change in cost 0.199028
step 5530, training accuracy 0.916055
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step 5530, change in cost 0.198387
step 5540, training accuracy 0.916055
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step 10000, training accuracy 0.959077
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step 10200, training accuracy 0.959077
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step 10240, training accuracy 0.959077
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step 25040, change in cost 0.00657845
step 25050, training accuracy 0.991605
step 25050, cost 13.205
step 25050, change in cost 0.00657368
step 25060, training accuracy 0.991605
step 25060, cost 13.1984
step 25060, change in cost 0.00657272
step 25070, training accuracy 0.991605
step 25070, cost 13.1918
step 25070, change in cost 0.00656319
step 25080, training accuracy 0.991605
step 25080, cost 13.1853
step 25080, change in cost 0.00655556
step 25090, training accuracy 0.991605
step 25090, cost 13.1787
step 25090, change in cost 0.00655365
step 25100, training accuracy 0.991605
step 25100, cost 13.1722
step 25100, change in cost 0.00654411
step 25110, training accuracy 0.991605
step 25110, cost 13.1657
step 25110, change in cost 0.00653744
step 25120, training accuracy 0.991605
step 25120, cost 13.1591
step 25120, change in cost 0.00653553
step 25130, training accuracy 0.991605
step 25130, cost 13.1526
step 25130, change in cost 0.00653076
step 25140, training accuracy 0.991605
step 25140, cost 13.1461
step 25140, change in cost 0.00651836
step 25150, training accuracy 0.991605
step 25150, cost 13.1396
step 25150, change in cost 0.00652218
step 25160, training accuracy 0.991605
step 25160, cost 13.133
step 25160, change in cost 0.00650501
step 25170, training accuracy 0.991605
step 25170, cost 13.1265
step 25170, change in cost 0.00649643
step 25180, training accuracy 0.991605
step 25180, cost 13.12
step 25180, change in cost 0.00650024
step 25190, training accuracy 0.991605
step 25190, cost 13.1136
step 25190, change in cost 0.00649166
step 25200, training accuracy 0.991605
step 25200, cost 13.1071
step 25200, change in cost 0.00648689
step 25210, training accuracy 0.991605
step 25210, cost 13.1006
step 25210, change in cost 0.00648117
step 25220, training accuracy 0.991605
step 25220, cost 13.0941
step 25220, change in cost 0.00647259
step 25230, training accuracy 0.991605
step 25230, cost 13.0876
step 25230, change in cost 0.00646687
step 25240, training accuracy 0.991605
step 25240, cost 13.0812
step 25240, change in cost 0.00646305
step 25250, training accuracy 0.991605
step 25250, cost 13.0747
step 25250, change in cost 0.00645638
step 25260, training accuracy 0.991605
step 25260, cost 13.0683
step 25260, change in cost 0.00645351
step 25270, training accuracy 0.991605
step 25270, cost 13.0618
step 25270, change in cost 0.00644588
step 25280, training accuracy 0.991605
step 25280, cost 13.0554
step 25280, change in cost 0.00644588
step 25290, training accuracy 0.991605
step 25290, cost 13.0489
step 25290, change in cost 0.00643539
step 25300, training accuracy 0.991605
step 25300, cost 13.0425
step 25300, change in cost 0.0064373
step 25310, training accuracy 0.991605
step 25310, cost 13.0361
step 25310, change in cost 0.00641918
step 25320, training accuracy 0.991605
step 25320, cost 13.0297
step 25320, change in cost 0.00642014
step 25330, training accuracy 0.991605
step 25330, cost 13.0233
step 25330, change in cost 0.00641346
step 25340, training accuracy 0.991605
step 25340, cost 13.0169
step 25340, change in cost 0.00640583
step 25350, training accuracy 0.991605
step 25350, cost 13.0105
step 25350, change in cost 0.00639915
step 25360, training accuracy 0.991605
step 25360, cost 13.0041
step 25360, change in cost 0.00639153
step 25370, training accuracy 0.991605
step 25370, cost 12.9977
step 25370, change in cost 0.0063982
step 25380, training accuracy 0.991605
step 25380, cost 12.9913
step 25380, change in cost 0.00637817
step 25390, training accuracy 0.991605
step 25390, cost 12.9849
step 25390, change in cost 0.00637913
step 25400, training accuracy 0.991605
step 25400, cost 12.9785
step 25400, change in cost 0.00637245
step 25410, training accuracy 0.991605
step 25410, cost 12.9722
step 25410, change in cost 0.00636768
step 25420, training accuracy 0.991605
step 25420, cost 12.9658
step 25420, change in cost 0.00636101
step 25430, training accuracy 0.991605
step 25430, cost 12.9595
step 25430, change in cost 0.00635338
step 25440, training accuracy 0.991605
step 25440, cost 12.9531
step 25440, change in cost 0.00634861
step 25450, training accuracy 0.991605
step 25450, cost 12.9468
step 25450, change in cost 0.0063467
step 25460, training accuracy 0.991605
step 25460, cost 12.9404
step 25460, change in cost 0.00634289
step 25470, training accuracy 0.991605
step 25470, cost 12.9341
step 25470, change in cost 0.00632954
step 25480, training accuracy 0.991605
step 25480, cost 12.9278
step 25480, change in cost 0.00632763
step 25490, training accuracy 0.991605
step 25490, cost 12.9214
step 25490, change in cost 0.00632381
step 25500, training accuracy 0.991605
step 25500, cost 12.9151
step 25500, change in cost 0.00631714
step 25510, training accuracy 0.991605
step 25510, cost 12.9088
step 25510, change in cost 0.00630951
step 25520, training accuracy 0.991605
step 25520, cost 12.9025
step 25520, change in cost 0.00631523
step 25530, training accuracy 0.991605
step 25530, cost 12.8962
step 25530, change in cost 0.00629616
step 25540, training accuracy 0.991605
step 25540, cost 12.8899
step 25540, change in cost 0.00630093
step 25550, training accuracy 0.991605
step 25550, cost 12.8836
step 25550, change in cost 0.00629139
step 25560, training accuracy 0.991605
step 25560, cost 12.8773
step 25560, change in cost 0.00628185
step 25570, training accuracy 0.991605
step 25570, cost 12.871
step 25570, change in cost 0.00627422
step 25580, training accuracy 0.991605
step 25580, cost 12.8648
step 25580, change in cost 0.00627518
step 25590, training accuracy 0.991605
step 25590, cost 12.8585
step 25590, change in cost 0.00626469
step 25600, training accuracy 0.991605
step 25600, cost 12.8522
step 25600, change in cost 0.00626183
step 25610, training accuracy 0.991605
step 25610, cost 12.846
step 25610, change in cost 0.00626087
step 25620, training accuracy 0.991605
step 25620, cost 12.8397
step 25620, change in cost 0.00624847
step 25630, training accuracy 0.991605
step 25630, cost 12.8335
step 25630, change in cost 0.00624752
step 25640, training accuracy 0.991605
step 25640, cost 12.8272
step 25640, change in cost 0.00624466
step 25650, training accuracy 0.991605
step 25650, cost 12.821
step 25650, change in cost 0.00623608
step 25660, training accuracy 0.991605
step 25660, cost 12.8148
step 25660, change in cost 0.00622559
step 25670, training accuracy 0.991605
step 25670, cost 12.8086
step 25670, change in cost 0.00622654
step 25680, training accuracy 0.991605
step 25680, cost 12.8023
step 25680, change in cost 0.00621414
step 25690, training accuracy 0.991605
step 25690, cost 12.7961
step 25690, change in cost 0.006217
step 25700, training accuracy 0.991605
step 25700, cost 12.7899
step 25700, change in cost 0.00620556
step 25710, training accuracy 0.991605
step 25710, cost 12.7837
step 25710, change in cost 0.00620174
step 25720, training accuracy 0.991605
step 25720, cost 12.7775
step 25720, change in cost 0.00619411
step 25730, training accuracy 0.991605
step 25730, cost 12.7713
step 25730, change in cost 0.00619125
step 25740, training accuracy 0.991605
step 25740, cost 12.7651
step 25740, change in cost 0.00619221
step 25750, training accuracy 0.991605
step 25750, cost 12.759
step 25750, change in cost 0.0061779
step 25760, training accuracy 0.991605
step 25760, cost 12.7528
step 25760, change in cost 0.00617695
step 25770, training accuracy 0.991605
step 25770, cost 12.7466
step 25770, change in cost 0.00617218
step 25780, training accuracy 0.991605
step 25780, cost 12.7404
step 25780, change in cost 0.0061655
step 25790, training accuracy 0.991605
step 25790, cost 12.7343
step 25790, change in cost 0.00616169
step 25800, training accuracy 0.991605
step 25800, cost 12.7281
step 25800, change in cost 0.00615501
step 25810, training accuracy 0.991605
step 25810, cost 12.722
step 25810, change in cost 0.00614548
step 25820, training accuracy 0.991605
step 25820, cost 12.7158
step 25820, change in cost 0.00614166
step 25830, training accuracy 0.991605
step 25830, cost 12.7097
step 25830, change in cost 0.0061388
step 25840, training accuracy 0.991605
step 25840, cost 12.7036
step 25840, change in cost 0.00613499
step 25850, training accuracy 0.991605
step 25850, cost 12.6974
step 25850, change in cost 0.00613308
step 25860, training accuracy 0.991605
step 25860, cost 12.6913
step 25860, change in cost 0.00612354
step 25870, training accuracy 0.991605
step 25870, cost 12.6852
step 25870, change in cost 0.00611591
step 25880, training accuracy 0.991605
step 25880, cost 12.6791
step 25880, change in cost 0.0061121
step 25890, training accuracy 0.991605
step 25890, cost 12.673
step 25890, change in cost 0.00611019
step 25900, training accuracy 0.991605
step 25900, cost 12.6669
step 25900, change in cost 0.00609684
step 25910, training accuracy 0.991605
step 25910, cost 12.6608
step 25910, change in cost 0.00609589
step 25920, training accuracy 0.991605
step 25920, cost 12.6547
step 25920, change in cost 0.00609207
step 25930, training accuracy 0.991605
step 25930, cost 12.6486
step 25930, change in cost 0.00608253
step 25940, training accuracy 0.991605
step 25940, cost 12.6425
step 25940, change in cost 0.00607967
step 25950, training accuracy 0.991605
step 25950, cost 12.6365
step 25950, change in cost 0.00607204
step 25960, training accuracy 0.991605
step 25960, cost 12.6304
step 25960, change in cost 0.00606823
step 25970, training accuracy 0.991605
step 25970, cost 12.6243
step 25970, change in cost 0.00606251
step 25980, training accuracy 0.991605
step 25980, cost 12.6183
step 25980, change in cost 0.0060606
step 25990, training accuracy 0.991605
step 25990, cost 12.6122
step 25990, change in cost 0.00605392
step 26000, training accuracy 0.991605
step 26000, cost 12.6062
step 26000, change in cost 0.00605202
step 26010, training accuracy 0.991605
step 26010, cost 12.6001
step 26010, change in cost 0.00604153
step 26020, training accuracy 0.991605
step 26020, cost 12.5941
step 26020, change in cost 0.0060358
step 26030, training accuracy 0.991605
step 26030, cost 12.588
step 26030, change in cost 0.00603676
step 26040, training accuracy 0.991605
step 26040, cost 12.582
step 26040, change in cost 0.00603008
step 26050, training accuracy 0.991605
step 26050, cost 12.576
step 26050, change in cost 0.00602436
step 26060, training accuracy 0.991605
step 26060, cost 12.57
step 26060, change in cost 0.0060091
step 26070, training accuracy 0.991605
step 26070, cost 12.564
step 26070, change in cost 0.00601673
step 26080, training accuracy 0.991605
step 26080, cost 12.558
step 26080, change in cost 0.0060091
step 26090, training accuracy 0.991605
step 26090, cost 12.552
step 26090, change in cost 0.00600338
step 26100, training accuracy 0.991605
step 26100, cost 12.546
step 26100, change in cost 0.00599384
step 26110, training accuracy 0.991605
step 26110, cost 12.54
step 26110, change in cost 0.00599194
step 26120, training accuracy 0.991605
step 26120, cost 12.534
step 26120, change in cost 0.00599003
step 26130, training accuracy 0.991605
step 26130, cost 12.528
step 26130, change in cost 0.00598335
step 26140, training accuracy 0.991605
step 26140, cost 12.522
step 26140, change in cost 0.00597668
step 26150, training accuracy 0.991605
step 26150, cost 12.516
step 26150, change in cost 0.00597286
step 26160, training accuracy 0.991605
step 26160, cost 12.5101
step 26160, change in cost 0.00596237
step 26170, training accuracy 0.991605
step 26170, cost 12.5041
step 26170, change in cost 0.00596333
step 26180, training accuracy 0.991605
step 26180, cost 12.4982
step 26180, change in cost 0.00596046
step 26190, training accuracy 0.991605
step 26190, cost 12.4922
step 26190, change in cost 0.00594902
step 26200, training accuracy 0.991605
step 26200, cost 12.4863
step 26200, change in cost 0.0059433
step 26210, training accuracy 0.991605
step 26210, cost 12.4803
step 26210, change in cost 0.00594234
step 26220, training accuracy 0.991605
step 26220, cost 12.4744
step 26220, change in cost 0.00593758
step 26230, training accuracy 0.991605
step 26230, cost 12.4685
step 26230, change in cost 0.00592995
step 26240, training accuracy 0.991605
step 26240, cost 12.4625
step 26240, change in cost 0.00592899
step 26250, training accuracy 0.991605
step 26250, cost 12.4566
step 26250, change in cost 0.00592232
step 26260, training accuracy 0.991605
step 26260, cost 12.4507
step 26260, change in cost 0.00591278
step 26270, training accuracy 0.991605
step 26270, cost 12.4448
step 26270, change in cost 0.00591278
step 26280, training accuracy 0.991605
step 26280, cost 12.4389
step 26280, change in cost 0.00590515
step 26290, training accuracy 0.991605
step 26290, cost 12.433
step 26290, change in cost 0.0059042
step 26300, training accuracy 0.991605
step 26300, cost 12.4271
step 26300, change in cost 0.00589657
step 26310, training accuracy 0.991605
step 26310, cost 12.4212
step 26310, change in cost 0.00588799
step 26320, training accuracy 0.991605
step 26320, cost 12.4153
step 26320, change in cost 0.00588989
step 26330, training accuracy 0.991605
step 26330, cost 12.4094
step 26330, change in cost 0.00588322
step 26340, training accuracy 0.991605
step 26340, cost 12.4035
step 26340, change in cost 0.00586987
step 26350, training accuracy 0.991605
step 26350, cost 12.3977
step 26350, change in cost 0.00587559
step 26360, training accuracy 0.991605
step 26360, cost 12.3918
step 26360, change in cost 0.00586319
step 26370, training accuracy 0.991605
step 26370, cost 12.3859
step 26370, change in cost 0.005867
step 26380, training accuracy 0.991605
step 26380, cost 12.3801
step 26380, change in cost 0.00584602
step 26390, training accuracy 0.991605
step 26390, cost 12.3742
step 26390, change in cost 0.00585079
step 26400, training accuracy 0.991605
step 26400, cost 12.3684
step 26400, change in cost 0.00584984
step 26410, training accuracy 0.991605
step 26410, cost 12.3625
step 26410, change in cost 0.00584698
step 26420, training accuracy 0.991605
step 26420, cost 12.3567
step 26420, change in cost 0.00583267
step 26430, training accuracy 0.991605
step 26430, cost 12.3509
step 26430, change in cost 0.00583363
step 26440, training accuracy 0.991605
step 26440, cost 12.345
step 26440, change in cost 0.005826
step 26450, training accuracy 0.991605
step 26450, cost 12.3392
step 26450, change in cost 0.00581646
step 26460, training accuracy 0.991605
step 26460, cost 12.3334
step 26460, change in cost 0.00582027
step 26470, training accuracy 0.991605
step 26470, cost 12.3276
step 26470, change in cost 0.00581455
step 26480, training accuracy 0.991605
step 26480, cost 12.3218
step 26480, change in cost 0.00580025
step 26490, training accuracy 0.991605
step 26490, cost 12.316
step 26490, change in cost 0.0058012
step 26500, training accuracy 0.991605
step 26500, cost 12.3102
step 26500, change in cost 0.00580025
step 26510, training accuracy 0.991605
step 26510, cost 12.3044
step 26510, change in cost 0.00579453
step 26520, training accuracy 0.991605
step 26520, cost 12.2986
step 26520, change in cost 0.00578499
step 26530, training accuracy 0.991605
step 26530, cost 12.2928
step 26530, change in cost 0.00578785
step 26540, training accuracy 0.991605
step 26540, cost 12.2871
step 26540, change in cost 0.00577259
step 26550, training accuracy 0.991605
step 26550, cost 12.2813
step 26550, change in cost 0.00577164
step 26560, training accuracy 0.991605
step 26560, cost 12.2755
step 26560, change in cost 0.00576591
step 26570, training accuracy 0.991605
step 26570, cost 12.2698
step 26570, change in cost 0.00576496
step 26580, training accuracy 0.991605
step 26580, cost 12.264
step 26580, change in cost 0.00575829
step 26590, training accuracy 0.991605
step 26590, cost 12.2582
step 26590, change in cost 0.0057497
step 26600, training accuracy 0.991605
step 26600, cost 12.2525
step 26600, change in cost 0.00574875
step 26610, training accuracy 0.991605
step 26610, cost 12.2467
step 26610, change in cost 0.00574875
step 26620, training accuracy 0.991605
step 26620, cost 12.241
step 26620, change in cost 0.00573921
step 26630, training accuracy 0.991605
step 26630, cost 12.2353
step 26630, change in cost 0.00573158
step 26640, training accuracy 0.991605
step 26640, cost 12.2295
step 26640, change in cost 0.00572968
step 26650, training accuracy 0.991605
step 26650, cost 12.2238
step 26650, change in cost 0.00572491
step 26660, training accuracy 0.991605
step 26660, cost 12.2181
step 26660, change in cost 0.005723
step 26670, training accuracy 0.991605
step 26670, cost 12.2124
step 26670, change in cost 0.00570965
step 26680, training accuracy 0.991605
step 26680, cost 12.2067
step 26680, change in cost 0.00571346
step 26690, training accuracy 0.991605
step 26690, cost 12.201
step 26690, change in cost 0.00571156
step 26700, training accuracy 0.991605
step 26700, cost 12.1953
step 26700, change in cost 0.0056982
step 26710, training accuracy 0.991605
step 26710, cost 12.1896
step 26710, change in cost 0.00570393
step 26720, training accuracy 0.991605
step 26720, cost 12.1839
step 26720, change in cost 0.00568962
step 26730, training accuracy 0.991605
step 26730, cost 12.1782
step 26730, change in cost 0.0056839
step 26740, training accuracy 0.991605
step 26740, cost 12.1725
step 26740, change in cost 0.00568008
step 26750, training accuracy 0.991605
step 26750, cost 12.1668
step 26750, change in cost 0.00568199
step 26760, training accuracy 0.991605
step 26760, cost 12.1612
step 26760, change in cost 0.00566387
step 26770, training accuracy 0.991605
step 26770, cost 12.1555
step 26770, change in cost 0.00567341
step 26780, training accuracy 0.991605
step 26780, cost 12.1498
step 26780, change in cost 0.00566387
step 26790, training accuracy 0.991605
step 26790, cost 12.1442
step 26790, change in cost 0.00565434
step 26800, training accuracy 0.991605
step 26800, cost 12.1385
step 26800, change in cost 0.00565815
step 26810, training accuracy 0.991605
step 26810, cost 12.1329
step 26810, change in cost 0.00564957
step 26820, training accuracy 0.991605
step 26820, cost 12.1272
step 26820, change in cost 0.00564003
step 26830, training accuracy 0.991605
step 26830, cost 12.1216
step 26830, change in cost 0.00563908
step 26840, training accuracy 0.991605
step 26840, cost 12.116
step 26840, change in cost 0.0056324
step 26850, training accuracy 0.991605
step 26850, cost 12.1103
step 26850, change in cost 0.00563049
step 26860, training accuracy 0.991605
step 26860, cost 12.1047
step 26860, change in cost 0.00562572
step 26870, training accuracy 0.991605
step 26870, cost 12.0991
step 26870, change in cost 0.00562096
step 26880, training accuracy 0.991605
step 26880, cost 12.0935
step 26880, change in cost 0.00561428
step 26890, training accuracy 0.991605
step 26890, cost 12.0878
step 26890, change in cost 0.00561333
step 26900, training accuracy 0.991605
step 26900, cost 12.0822
step 26900, change in cost 0.00560856
step 26910, training accuracy 0.991605
step 26910, cost 12.0766
step 26910, change in cost 0.00560188
step 26920, training accuracy 0.991605
step 26920, cost 12.071
step 26920, change in cost 0.00559807
step 26930, training accuracy 0.991605
step 26930, cost 12.0654
step 26930, change in cost 0.00559425
step 26940, training accuracy 0.991605
step 26940, cost 12.0599
step 26940, change in cost 0.00558853
step 26950, training accuracy 0.991605
step 26950, cost 12.0543
step 26950, change in cost 0.00558567
step 26960, training accuracy 0.991605
step 26960, cost 12.0487
step 26960, change in cost 0.00558376
step 26970, training accuracy 0.991605
step 26970, cost 12.0431
step 26970, change in cost 0.00557613
step 26980, training accuracy 0.991605
step 26980, cost 12.0375
step 26980, change in cost 0.0055685
step 26990, training accuracy 0.991605
step 26990, cost 12.032
step 26990, change in cost 0.00556087
final accuracy on test set: 0.990476

In [81]:

    

# 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("221")
ax1.set_title("TRAINING ACCURACY LOGISTIC REGRESSION", fontsize=10)
ax2 = plt.subplot("222")
ax2.set_title("TRAINING COST LOGISTIC REGRESSION", fontsize=10)

ax3 = plt.subplot("223")
ax3.set_title("TRAINING ACCURACY NEURAL NETWORK", fontsize=10)
ax4 = plt.subplot("224")
ax4.set_title("TRAINING COST NEURAL NETWORK", fontsize=10)

plt.tight_layout()

# Plot progress to our two subplots
accuracyLine, = ax1.plot(epoch_values, accuracy_values)
costLine, = ax2.plot(epoch_values, cost_values)
accuracyLinenn, = ax3.plot(epoch_valuesnn, accuracy_valuesnn)
costLinenn, = ax4.plot(epoch_valuesnn, cost_valuesnn)
fig.canvas.draw()


    

In [ ]: