In [1]:

    

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


    

In [3]:

    

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






    




/usr/local/lib/python3.4/dist-packages/numpy/lib/npyio.py:1487: UserWarning: genfromtxt: Empty input file: "data/trainX.csv"
  warnings.warn('genfromtxt: Empty input file: "%s"' % fname)






    




---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-9f9cd1372658> in <module>()
     21     return trainX,trainY,testX,testY
     22 
---> 23 trainX,trainY,testX,testY = import_data()

<ipython-input-3-9f9cd1372658> in import_data()
     14 
     15     print("loading training data")
---> 16     trainX = csv_to_numpy_array("data/trainX.csv", delimiter="\t")
     17     trainY = csv_to_numpy_array("data/trainY.csv", delimiter="\t")
     18     print("loading test data")

<ipython-input-3-9f9cd1372658> in csv_to_numpy_array(filePath, delimiter)
      1 def csv_to_numpy_array(filePath, delimiter):
----> 2     return np.genfromtxt(filePath, delimiter=delimiter, dtype=None)
      3 
      4 def import_data():
      5     if "data" not in os.listdir(os.getcwd()):

/usr/local/lib/python3.4/dist-packages/numpy/lib/npyio.py in genfromtxt(fname, dtype, comments, delimiter, skip_header, skip_footer, converters, missing_values, filling_values, usecols, names, excludelist, deletechars, replace_space, autostrip, case_sensitive, defaultfmt, unpack, usemask, loose, invalid_raise, max_rows)
   1815             ddtype = list(zip(names, column_types))
   1816             mdtype = list(zip(names, [np.bool] * len(column_types)))
-> 1817         output = np.array(data, dtype=ddtype)
   1818         if usemask:
   1819             outputmask = np.array(masks, dtype=mdtype)

TypeError: Empty data-type

In [ ]:

    

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

# 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 [14]:

    

# 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 [15]:

    

# 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 [16]:

    

# 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 [17]:

    

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


    

In [18]:

    

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


    

In [19]:

    

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


    

In [20]:

    

# 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 0.466946
step 0, cost 373.17
step 0, change in cost 373.17
step 10, training accuracy 0.466946
step 10, cost 354.924
step 10, change in cost 18.2468
step 20, training accuracy 0.466946
step 20, cost 340.638
step 20, change in cost 14.2855
step 30, training accuracy 0.465897
step 30, cost 327.008
step 30, change in cost 13.6306
step 40, training accuracy 0.465897
step 40, cost 311.114
step 40, change in cost 15.8934
step 50, training accuracy 0.465897
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step 50, change in cost 18.8791
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step 60, change in cost 18.8962
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step 70, change in cost 14.0295
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step 80, change in cost 7.74072
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step 90, change in cost 3.53928
step 100, training accuracy 0.419727
step 100, cost 246.522
step 100, change in cost 1.50746
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step 110, change in cost 0.651306
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step 1230, change in cost 0.0780487
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step 1570, change in cost 0.0765228
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step 1580, change in cost 0.0765228
step 1590, training accuracy 0.581322
step 1590, cost 233.708
step 1590, change in cost 0.0763702
step 1600, training accuracy 0.581322
step 1600, cost 233.632
step 1600, change in cost 0.0764771
step 1610, training accuracy 0.581322
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step 1610, change in cost 0.0763397
step 1620, training accuracy 0.581322
step 1620, cost 233.479
step 1620, change in cost 0.076355
step 1630, training accuracy 0.581322
step 1630, cost 233.403
step 1630, change in cost 0.0761871
step 1640, training accuracy 0.581322
step 1640, cost 233.327
step 1640, change in cost 0.0763702
step 1650, training accuracy 0.581322
step 1650, cost 233.251
step 1650, change in cost 0.0761719
step 1660, training accuracy 0.582371
step 1660, cost 233.174
step 1660, change in cost 0.0760956
step 1670, training accuracy 0.582371
step 1670, cost 233.098
step 1670, change in cost 0.0761414
step 1680, training accuracy 0.582371
step 1680, cost 233.022
step 1680, change in cost 0.0760956
step 1690, training accuracy 0.582371
step 1690, cost 232.946
step 1690, change in cost 0.0760345
step 1700, training accuracy 0.582371
step 1700, cost 232.87
step 1700, change in cost 0.0758667
step 1710, training accuracy 0.582371
step 1710, cost 232.794
step 1710, change in cost 0.076004
step 1720, training accuracy 0.583421
step 1720, cost 232.718
step 1720, change in cost 0.0758667
step 1730, training accuracy 0.58447
step 1730, cost 232.643
step 1730, change in cost 0.0758362
step 1740, training accuracy 0.58447
step 1740, cost 232.567
step 1740, change in cost 0.0758057
step 1750, training accuracy 0.58447
step 1750, cost 232.491
step 1750, change in cost 0.0757294
step 1760, training accuracy 0.58447
step 1760, cost 232.415
step 1760, change in cost 0.0757599
step 1770, training accuracy 0.58447
step 1770, cost 232.34
step 1770, change in cost 0.0756531
step 1780, training accuracy 0.58447
step 1780, cost 232.264
step 1780, change in cost 0.0756378
step 1790, training accuracy 0.58447
step 1790, cost 232.188
step 1790, change in cost 0.0756073
step 1800, training accuracy 0.58447
step 1800, cost 232.113
step 1800, change in cost 0.075531
step 1810, training accuracy 0.585519
step 1810, cost 232.037
step 1810, change in cost 0.0755005
step 1820, training accuracy 0.585519
step 1820, cost 231.962
step 1820, change in cost 0.07547
step 1830, training accuracy 0.585519
step 1830, cost 231.887
step 1830, change in cost 0.0754242
step 1840, training accuracy 0.585519
step 1840, cost 231.811
step 1840, change in cost 0.0754089
step 1850, training accuracy 0.585519
step 1850, cost 231.736
step 1850, change in cost 0.0752563
step 1860, training accuracy 0.586569
step 1860, cost 231.661
step 1860, change in cost 0.0752563
step 1870, training accuracy 0.586569
step 1870, cost 231.585
step 1870, change in cost 0.0753174
step 1880, training accuracy 0.586569
step 1880, cost 231.51
step 1880, change in cost 0.0751648
step 1890, training accuracy 0.586569
step 1890, cost 231.435
step 1890, change in cost 0.0750885
step 1900, training accuracy 0.587618
step 1900, cost 231.36
step 1900, change in cost 0.0751495
step 1910, training accuracy 0.587618
step 1910, cost 231.285
step 1910, change in cost 0.0750275
step 1920, training accuracy 0.587618
step 1920, cost 231.21
step 1920, change in cost 0.0749969
step 1930, training accuracy 0.587618
step 1930, cost 231.135
step 1930, change in cost 0.0749969
step 1940, training accuracy 0.588667
step 1940, cost 231.06
step 1940, change in cost 0.0749207
step 1950, training accuracy 0.589717
step 1950, cost 230.985
step 1950, change in cost 0.0749359
step 1960, training accuracy 0.590766
step 1960, cost 230.91
step 1960, change in cost 0.0747986
step 1970, training accuracy 0.592865
step 1970, cost 230.835
step 1970, change in cost 0.0747528
step 1980, training accuracy 0.592865
step 1980, cost 230.761
step 1980, change in cost 0.0748138
step 1990, training accuracy 0.592865
step 1990, cost 230.686
step 1990, change in cost 0.0747375
step 2000, training accuracy 0.593914
step 2000, cost 230.611
step 2000, change in cost 0.0746155
step 2010, training accuracy 0.593914
step 2010, cost 230.537
step 2010, change in cost 0.0746765
step 2020, training accuracy 0.593914
step 2020, cost 230.462
step 2020, change in cost 0.074585
step 2030, training accuracy 0.593914
step 2030, cost 230.388
step 2030, change in cost 0.0744934
step 2040, training accuracy 0.593914
step 2040, cost 230.313
step 2040, change in cost 0.0745087
step 2050, training accuracy 0.593914
step 2050, cost 230.239
step 2050, change in cost 0.0744781
step 2060, training accuracy 0.593914
step 2060, cost 230.164
step 2060, change in cost 0.0744171
step 2070, training accuracy 0.592865
step 2070, cost 230.09
step 2070, change in cost 0.0743866
step 2080, training accuracy 0.593914
step 2080, cost 230.015
step 2080, change in cost 0.0743561
step 2090, training accuracy 0.593914
step 2090, cost 229.941
step 2090, change in cost 0.074234
step 2100, training accuracy 0.593914
step 2100, cost 229.867
step 2100, change in cost 0.0742645
step 2110, training accuracy 0.594963
step 2110, cost 229.793
step 2110, change in cost 0.0741882
step 2120, training accuracy 0.594963
step 2120, cost 229.719
step 2120, change in cost 0.0741272
step 2130, training accuracy 0.596013
step 2130, cost 229.644
step 2130, change in cost 0.0741272
step 2140, training accuracy 0.598111
step 2140, cost 229.57
step 2140, change in cost 0.0741272
step 2150, training accuracy 0.598111
step 2150, cost 229.496
step 2150, change in cost 0.0739746
step 2160, training accuracy 0.599161
step 2160, cost 229.422
step 2160, change in cost 0.0738983
step 2170, training accuracy 0.599161
step 2170, cost 229.348
step 2170, change in cost 0.0739746
step 2180, training accuracy 0.60021
step 2180, cost 229.275
step 2180, change in cost 0.0739136
step 2190, training accuracy 0.60021
step 2190, cost 229.201
step 2190, change in cost 0.0738678
step 2200, training accuracy 0.60021
step 2200, cost 229.127
step 2200, change in cost 0.0738068
step 2210, training accuracy 0.60021
step 2210, cost 229.053
step 2210, change in cost 0.0738068
step 2220, training accuracy 0.601259
step 2220, cost 228.979
step 2220, change in cost 0.0736847
step 2230, training accuracy 0.602309
step 2230, cost 228.906
step 2230, change in cost 0.0736847
step 2240, training accuracy 0.602309
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step 2240, change in cost 0.0736389
step 2250, training accuracy 0.602309
step 2250, cost 228.758
step 2250, change in cost 0.0735626
step 2260, training accuracy 0.603358
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step 2260, change in cost 0.0735779
step 2270, training accuracy 0.604407
step 2270, cost 228.611
step 2270, change in cost 0.0735168
step 2280, training accuracy 0.604407
step 2280, cost 228.538
step 2280, change in cost 0.0734711
step 2290, training accuracy 0.606506
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step 2290, change in cost 0.0733795
step 2300, training accuracy 0.606506
step 2300, cost 228.391
step 2300, change in cost 0.073349
step 2310, training accuracy 0.606506
step 2310, cost 228.318
step 2310, change in cost 0.0733948
step 2320, training accuracy 0.606506
step 2320, cost 228.245
step 2320, change in cost 0.0732727
step 2330, training accuracy 0.606506
step 2330, cost 228.171
step 2330, change in cost 0.0732727
step 2340, training accuracy 0.607555
step 2340, cost 228.098
step 2340, change in cost 0.0731812
step 2350, training accuracy 0.607555
step 2350, cost 228.025
step 2350, change in cost 0.0732117
step 2360, training accuracy 0.607555
step 2360, cost 227.952
step 2360, change in cost 0.0731049
step 2370, training accuracy 0.607555
step 2370, cost 227.879
step 2370, change in cost 0.0730743
step 2380, training accuracy 0.608604
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step 2380, change in cost 0.0730286
step 2390, training accuracy 0.609654
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step 2390, change in cost 0.0729523
step 2400, training accuracy 0.610703
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step 2400, change in cost 0.0729828
step 2410, training accuracy 0.610703
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step 2410, change in cost 0.072937
step 2420, training accuracy 0.612802
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step 2420, change in cost 0.0728455
step 2430, training accuracy 0.612802
step 2430, cost 227.441
step 2430, change in cost 0.0728302
step 2440, training accuracy 0.612802
step 2440, cost 227.368
step 2440, change in cost 0.0727692
step 2450, training accuracy 0.612802
step 2450, cost 227.296
step 2450, change in cost 0.0727234
step 2460, training accuracy 0.613851
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step 2460, change in cost 0.0726929
step 2470, training accuracy 0.6149
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step 2470, change in cost 0.0726166
step 2480, training accuracy 0.6149
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step 2480, change in cost 0.0726471
step 2490, training accuracy 0.6149
step 2490, cost 227.005
step 2490, change in cost 0.0724945
step 2500, training accuracy 0.6149
step 2500, cost 226.933
step 2500, change in cost 0.0725861
step 2510, training accuracy 0.6149
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step 2510, change in cost 0.0724487
step 2520, training accuracy 0.6149
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step 2520, change in cost 0.0724335
step 2530, training accuracy 0.61595
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step 2530, change in cost 0.0724182
step 2540, training accuracy 0.61595
step 2540, cost 226.643
step 2540, change in cost 0.0723267
step 2550, training accuracy 0.618048
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step 2550, change in cost 0.0723877
step 2560, training accuracy 0.619098
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step 2560, change in cost 0.0722504
step 2570, training accuracy 0.620147
step 2570, cost 226.426
step 2570, change in cost 0.0721436
step 2580, training accuracy 0.621196
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step 2580, change in cost 0.0722198
step 2590, training accuracy 0.621196
step 2590, cost 226.282
step 2590, change in cost 0.0721436
step 2600, training accuracy 0.621196
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step 2600, change in cost 0.0720673
step 2610, training accuracy 0.621196
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step 2610, change in cost 0.072113
step 2620, training accuracy 0.621196
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step 2620, change in cost 0.072052
step 2630, training accuracy 0.622246
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step 2630, change in cost 0.0719299
step 2640, training accuracy 0.622246
step 2640, cost 225.922
step 2640, change in cost 0.0719604
step 2650, training accuracy 0.622246
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step 2650, change in cost 0.0717468
step 2660, training accuracy 0.622246
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step 2660, change in cost 0.0718384
step 2670, training accuracy 0.622246
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step 2670, change in cost 0.0718689
step 2680, training accuracy 0.624344
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step 2680, change in cost 0.0717468
step 2690, training accuracy 0.624344
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step 2690, change in cost 0.071701
step 2700, training accuracy 0.626443
step 2700, cost 225.491
step 2700, change in cost 0.0717163
step 2710, training accuracy 0.626443
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step 2710, change in cost 0.0715637
step 2720, training accuracy 0.626443
step 2720, cost 225.348
step 2720, change in cost 0.0716248
step 2730, training accuracy 0.626443
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step 2730, change in cost 0.0715485
step 2740, training accuracy 0.626443
step 2740, cost 225.205
step 2740, change in cost 0.0714722
step 2750, training accuracy 0.626443
step 2750, cost 225.133
step 2750, change in cost 0.0714722
step 2760, training accuracy 0.626443
step 2760, cost 225.062
step 2760, change in cost 0.0715027
step 2770, training accuracy 0.627492
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step 2770, change in cost 0.0712433
step 2780, training accuracy 0.627492
step 2780, cost 224.919
step 2780, change in cost 0.0713348
step 2790, training accuracy 0.627492
step 2790, cost 224.848
step 2790, change in cost 0.0713959
step 2800, training accuracy 0.628541
step 2800, cost 224.777
step 2800, change in cost 0.0711823
step 2810, training accuracy 0.629591
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step 2810, change in cost 0.071228
step 2820, training accuracy 0.629591
step 2820, cost 224.634
step 2820, change in cost 0.0711517
step 2830, training accuracy 0.629591
step 2830, cost 224.563
step 2830, change in cost 0.0711365
step 2840, training accuracy 0.629591
step 2840, cost 224.492
step 2840, change in cost 0.071106
step 2850, training accuracy 0.629591
step 2850, cost 224.421
step 2850, change in cost 0.0709381
step 2860, training accuracy 0.631689
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step 2860, change in cost 0.0710754
step 2870, training accuracy 0.633788
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step 2870, change in cost 0.0708618
step 2880, training accuracy 0.633788
step 2880, cost 224.208
step 2880, change in cost 0.0709076
step 2890, training accuracy 0.634837
step 2890, cost 224.137
step 2890, change in cost 0.0709229
step 2900, training accuracy 0.636936
step 2900, cost 224.066
step 2900, change in cost 0.0709076
step 2910, training accuracy 0.636936
step 2910, cost 223.996
step 2910, change in cost 0.0706787
step 2920, training accuracy 0.636936
step 2920, cost 223.925
step 2920, change in cost 0.070755
step 2930, training accuracy 0.636936
step 2930, cost 223.854
step 2930, change in cost 0.0707703
step 2940, training accuracy 0.637985
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step 2940, change in cost 0.0705719
step 2950, training accuracy 0.637985
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step 2950, change in cost 0.0706787
step 2960, training accuracy 0.639035
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step 2960, change in cost 0.0706177
step 2970, training accuracy 0.639035
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step 2970, change in cost 0.0705414
step 2980, training accuracy 0.640084
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step 2980, change in cost 0.0704193
step 2990, training accuracy 0.641133
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step 2990, change in cost 0.0704346
step 3000, training accuracy 0.641133
step 3000, cost 223.361
step 3000, change in cost 0.0703888
step 3010, training accuracy 0.641133
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step 3010, change in cost 0.0704193
step 3020, training accuracy 0.643232
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step 3020, change in cost 0.0702515
step 3030, training accuracy 0.643232
step 3030, cost 223.15
step 3030, change in cost 0.0703583
step 3040, training accuracy 0.643232
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step 3040, change in cost 0.0702057
step 3050, training accuracy 0.643232
step 3050, cost 223.009
step 3050, change in cost 0.0701599
step 3060, training accuracy 0.645331
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step 3060, change in cost 0.0702057
step 3070, training accuracy 0.64638
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step 3070, change in cost 0.0701294
step 3080, training accuracy 0.64638
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step 3080, change in cost 0.0700378
step 3090, training accuracy 0.64638
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step 3090, change in cost 0.0699768
step 3100, training accuracy 0.64638
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step 3100, change in cost 0.0700073
step 3110, training accuracy 0.64638
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step 3110, change in cost 0.069931
step 3120, training accuracy 0.647429
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step 3120, change in cost 0.0699005
step 3130, training accuracy 0.647429
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step 3130, change in cost 0.0699158
step 3140, training accuracy 0.647429
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step 3140, change in cost 0.0697784
step 3150, training accuracy 0.647429
step 3150, cost 222.31
step 3150, change in cost 0.069809
step 3160, training accuracy 0.649528
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step 3160, change in cost 0.0697174
step 3170, training accuracy 0.651626
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step 3170, change in cost 0.0697174
step 3180, training accuracy 0.651626
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step 3190, training accuracy 0.651626
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step 3190, change in cost 0.0695496
step 3200, training accuracy 0.651626
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step 3200, change in cost 0.0695343
step 3210, training accuracy 0.652676
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step 3210, change in cost 0.0695801
step 3220, training accuracy 0.652676
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step 3220, change in cost 0.0695496
step 3230, training accuracy 0.654774
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step 3230, change in cost 0.069397
step 3240, training accuracy 0.653725
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step 3250, training accuracy 0.654774
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step 3260, training accuracy 0.654774
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step 3260, change in cost 0.0692902
step 3270, training accuracy 0.654774
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step 3280, training accuracy 0.654774
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step 3280, change in cost 0.0693054
step 3290, training accuracy 0.655824
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step 3300, training accuracy 0.656873
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step 3300, change in cost 0.0691833
step 3310, training accuracy 0.657922
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step 3310, change in cost 0.0690002
step 3320, training accuracy 0.657922
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step 3320, change in cost 0.0691071
step 3330, training accuracy 0.657922
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step 3330, change in cost 0.0691071
step 3340, training accuracy 0.657922
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step 3350, training accuracy 0.657922
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step 3350, change in cost 0.068985
step 3360, training accuracy 0.657922
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step 3360, change in cost 0.0689392
step 3370, training accuracy 0.658972
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step 3380, training accuracy 0.658972
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step 3380, change in cost 0.0687866
step 3390, training accuracy 0.658972
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step 3400, training accuracy 0.658972
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step 3400, change in cost 0.0687714
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step 3420, training accuracy 0.66212
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step 3440, change in cost 0.0686493
step 3450, training accuracy 0.663169
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step 3450, change in cost 0.0685272
step 3460, training accuracy 0.664218
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step 3470, training accuracy 0.665268
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step 3480, training accuracy 0.666317
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step 3490, training accuracy 0.666317
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step 3490, change in cost 0.0683746
step 3500, training accuracy 0.665268
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step 3510, training accuracy 0.665268
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step 3520, training accuracy 0.666317
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step 3530, training accuracy 0.666317
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step 3560, training accuracy 0.667366
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step 3570, change in cost 0.0680237
step 3580, training accuracy 0.667366
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step 3590, training accuracy 0.667366
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step 3590, change in cost 0.0680084
step 3600, training accuracy 0.667366
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step 3610, training accuracy 0.667366
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step 3620, training accuracy 0.667366
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step 3630, training accuracy 0.667366
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step 3640, training accuracy 0.667366
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step 3660, training accuracy 0.669465
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step 3670, training accuracy 0.669465
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step 3690, training accuracy 0.671564
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step 3690, change in cost 0.0675201
step 3700, training accuracy 0.671564
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step 3710, training accuracy 0.672613
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step 3710, change in cost 0.0675354
step 3720, training accuracy 0.672613
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step 3730, training accuracy 0.672613
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step 3740, training accuracy 0.673662
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step 3750, training accuracy 0.673662
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step 3750, change in cost 0.0673065
step 3760, training accuracy 0.673662
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step 3760, change in cost 0.0672302
step 3770, training accuracy 0.674711
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step 3770, change in cost 0.067276
step 3780, training accuracy 0.67681
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step 3780, change in cost 0.0671844
step 3790, training accuracy 0.678909
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step 3800, training accuracy 0.678909
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step 3800, change in cost 0.0671539
step 3810, training accuracy 0.678909
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step 3810, change in cost 0.0670776
step 3820, training accuracy 0.679958
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step 3820, change in cost 0.0669861
step 3830, training accuracy 0.679958
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step 3830, change in cost 0.0669861
step 3840, training accuracy 0.679958
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step 3840, change in cost 0.0669861
step 3850, training accuracy 0.679958
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step 3850, change in cost 0.0668182
step 3860, training accuracy 0.682057
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step 3860, change in cost 0.0670166
step 3870, training accuracy 0.682057
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step 3870, change in cost 0.0667572
step 3880, training accuracy 0.683106
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step 3880, change in cost 0.0667877
step 3890, training accuracy 0.683106
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step 3890, change in cost 0.0667572
step 3900, training accuracy 0.683106
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step 3900, change in cost 0.0666809
step 3910, training accuracy 0.683106
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step 3910, change in cost 0.0666962
step 3920, training accuracy 0.686254
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step 3920, change in cost 0.0666046
step 3930, training accuracy 0.686254
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step 3930, change in cost 0.0666199
step 3940, training accuracy 0.686254
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step 3940, change in cost 0.0665741
step 3950, training accuracy 0.687303
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step 3950, change in cost 0.066452
step 3960, training accuracy 0.689402
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step 3960, change in cost 0.0664825
step 3970, training accuracy 0.689402
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step 3970, change in cost 0.066452
step 3980, training accuracy 0.689402
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step 3980, change in cost 0.066452
step 3990, training accuracy 0.69255
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step 3990, change in cost 0.0663605
step 4000, training accuracy 0.693599
step 4000, cost 216.529
step 4000, change in cost 0.0661621
step 4010, training accuracy 0.694649
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step 4010, change in cost 0.0664062
step 4020, training accuracy 0.694649
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step 4020, change in cost 0.0660858
step 4030, training accuracy 0.694649
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step 4030, change in cost 0.0662537
step 4040, training accuracy 0.695698
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step 4040, change in cost 0.0661469
step 4050, training accuracy 0.696747
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step 4050, change in cost 0.0661163
step 4060, training accuracy 0.696747
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step 4060, change in cost 0.0660553
step 4070, training accuracy 0.698846
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step 4070, change in cost 0.0660553
step 4080, training accuracy 0.698846
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step 4080, change in cost 0.0659637
step 4090, training accuracy 0.700944
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step 4090, change in cost 0.0659332
step 4100, training accuracy 0.701994
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step 4100, change in cost 0.065979
step 4110, training accuracy 0.704092
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step 4110, change in cost 0.0657959
step 4120, training accuracy 0.704092
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step 4120, change in cost 0.0658112
step 4130, training accuracy 0.705142
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step 4130, change in cost 0.0659027
step 4140, training accuracy 0.705142
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step 4140, change in cost 0.0656586
step 4150, training accuracy 0.706191
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step 4150, change in cost 0.0657043
step 4160, training accuracy 0.706191
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step 4160, change in cost 0.0657654
step 4170, training accuracy 0.70724
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step 4170, change in cost 0.0655975
step 4180, training accuracy 0.70829
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step 4180, change in cost 0.0655365
step 4190, training accuracy 0.710388
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step 4190, change in cost 0.0656128
step 4200, training accuracy 0.712487
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step 4200, change in cost 0.0654602
step 4210, training accuracy 0.712487
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step 4210, change in cost 0.0655212
step 4220, training accuracy 0.713536
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step 4220, change in cost 0.0654449
step 4230, training accuracy 0.713536
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step 4230, change in cost 0.0653076
step 4240, training accuracy 0.713536
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step 4240, change in cost 0.0653992
step 4250, training accuracy 0.713536
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step 4250, change in cost 0.0653381
step 4260, training accuracy 0.714586
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step 4260, change in cost 0.0653076
step 4270, training accuracy 0.714586
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step 4270, change in cost 0.0651855
step 4280, training accuracy 0.715635
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step 4280, change in cost 0.0652161
step 4290, training accuracy 0.715635
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step 4290, change in cost 0.0651398
step 4300, training accuracy 0.715635
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step 4300, change in cost 0.0651398
step 4310, training accuracy 0.718783
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step 4310, change in cost 0.0650177
step 4320, training accuracy 0.718783
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step 4320, change in cost 0.0651855
step 4330, training accuracy 0.718783
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step 4330, change in cost 0.0649414
step 4340, training accuracy 0.718783
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step 4340, change in cost 0.0649414
step 4350, training accuracy 0.718783
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step 4350, change in cost 0.0648804
step 4360, training accuracy 0.719832
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step 4360, change in cost 0.0648346
step 4370, training accuracy 0.720881
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step 4370, change in cost 0.0649109
step 4380, training accuracy 0.72298
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step 4380, change in cost 0.0648499
step 4390, training accuracy 0.724029
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step 4390, change in cost 0.0647278
step 4400, training accuracy 0.724029
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step 4400, change in cost 0.0647583
step 4410, training accuracy 0.725079
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step 4410, change in cost 0.064682
step 4420, training accuracy 0.725079
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step 4420, change in cost 0.0646667
step 4430, training accuracy 0.726128
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step 4430, change in cost 0.0645447
step 4440, training accuracy 0.728227
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step 4440, change in cost 0.0646057
step 4450, training accuracy 0.731375
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step 4450, change in cost 0.0645905
step 4460, training accuracy 0.731375
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step 4460, change in cost 0.0644531
step 4470, training accuracy 0.732424
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step 4470, change in cost 0.0644836
step 4480, training accuracy 0.732424
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step 4480, change in cost 0.0643921
step 4490, training accuracy 0.733473
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step 4490, change in cost 0.0643921
step 4500, training accuracy 0.733473
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step 4500, change in cost 0.0643005
step 4510, training accuracy 0.735572
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step 4510, change in cost 0.0642853
step 4520, training accuracy 0.737671
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step 4520, change in cost 0.0643311
step 4530, training accuracy 0.737671
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step 4530, change in cost 0.0641632
step 4540, training accuracy 0.73872
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step 4540, change in cost 0.0642395
step 4550, training accuracy 0.739769
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step 4550, change in cost 0.0641022
step 4560, training accuracy 0.739769
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step 4560, change in cost 0.0641174
step 4570, training accuracy 0.739769
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step 4570, change in cost 0.0640411
step 4580, training accuracy 0.740818
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step 4580, change in cost 0.0640717
step 4590, training accuracy 0.740818
step 4590, cost 212.686
step 4590, change in cost 0.0640411
step 4600, training accuracy 0.741868
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step 4600, change in cost 0.0638885
step 4610, training accuracy 0.741868
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step 4610, change in cost 0.0638123
step 4620, training accuracy 0.743966
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step 4620, change in cost 0.0639343
step 4630, training accuracy 0.743966
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step 4630, change in cost 0.0638428
step 4640, training accuracy 0.743966
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step 4640, change in cost 0.0638275
step 4650, training accuracy 0.745016
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step 4650, change in cost 0.063736
step 4660, training accuracy 0.746065
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step 4660, change in cost 0.063797
step 4670, training accuracy 0.748164
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step 4670, change in cost 0.0635681
step 4680, training accuracy 0.748164
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step 4680, change in cost 0.0636444
step 4690, training accuracy 0.748164
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step 4690, change in cost 0.0636597
step 4700, training accuracy 0.750262
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step 4700, change in cost 0.0635986
step 4710, training accuracy 0.750262
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step 4710, change in cost 0.0635376
step 4720, training accuracy 0.750262
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step 4720, change in cost 0.0634155
step 4730, training accuracy 0.750262
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step 4730, change in cost 0.0634308
step 4740, training accuracy 0.751312
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step 4740, change in cost 0.0635376
step 4750, training accuracy 0.751312
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step 4750, change in cost 0.0632629
step 4760, training accuracy 0.751312
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step 4770, training accuracy 0.752361
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step 4770, change in cost 0.0632935
step 4780, training accuracy 0.75341
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step 4780, change in cost 0.0632324
step 4790, training accuracy 0.75446
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step 4790, change in cost 0.0631866
step 4800, training accuracy 0.75446
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step 4800, change in cost 0.0632629
step 4810, training accuracy 0.75446
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step 4810, change in cost 0.0630493
step 4820, training accuracy 0.755509
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step 4820, change in cost 0.0631866
step 4830, training accuracy 0.755509
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step 4830, change in cost 0.0630493
step 4840, training accuracy 0.755509
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step 4840, change in cost 0.0629883
step 4850, training accuracy 0.755509
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step 4850, change in cost 0.0630646
step 4860, training accuracy 0.755509
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step 4860, change in cost 0.0629883
step 4870, training accuracy 0.756558
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step 4870, change in cost 0.0628662
step 4880, training accuracy 0.756558
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step 4880, change in cost 0.0628815
step 4890, training accuracy 0.757608
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step 4890, change in cost 0.0629272
step 4900, training accuracy 0.757608
step 4900, cost 210.722
step 4900, change in cost 0.0627441
step 4910, training accuracy 0.760755
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step 4910, change in cost 0.0628052
step 4920, training accuracy 0.760755
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step 4920, change in cost 0.0627594
step 4930, training accuracy 0.760755
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step 4930, change in cost 0.0626221
step 4940, training accuracy 0.762854
step 4940, cost 210.471
step 4940, change in cost 0.0626831
step 4950, training accuracy 0.762854
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step 4950, change in cost 0.0625763
step 4960, training accuracy 0.763903
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step 4960, change in cost 0.0626221
step 4970, training accuracy 0.764953
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step 4970, change in cost 0.0625
step 4980, training accuracy 0.767051
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step 4980, change in cost 0.0624695
step 4990, training accuracy 0.768101
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step 5000, training accuracy 0.76915
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step 5010, training accuracy 0.76915
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step 5020, training accuracy 0.770199
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step 5020, change in cost 0.0623932
step 5030, training accuracy 0.770199
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step 5040, training accuracy 0.770199
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step 5040, change in cost 0.0623016
step 5050, training accuracy 0.772298
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step 5050, change in cost 0.0622253
step 5060, training accuracy 0.772298
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step 5060, change in cost 0.0622101
step 5070, training accuracy 0.773347
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step 5070, change in cost 0.0621796
step 5080, training accuracy 0.773347
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step 5080, change in cost 0.0620575
step 5090, training accuracy 0.775446
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step 5090, change in cost 0.062149
step 5100, training accuracy 0.776495
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step 5110, training accuracy 0.776495
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step 5110, change in cost 0.0621033
step 5120, training accuracy 0.778594
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step 5130, training accuracy 0.779643
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step 5130, change in cost 0.0619507
step 5140, training accuracy 0.781742
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step 5150, training accuracy 0.782791
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step 5160, training accuracy 0.78384
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step 5220, training accuracy 0.790136
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step 5220, change in cost 0.0616302
step 5230, training accuracy 0.790136
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step 5240, training accuracy 0.790136
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step 5250, training accuracy 0.790136
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step 5250, change in cost 0.0614319
step 5260, training accuracy 0.790136
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step 5270, training accuracy 0.790136
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step 5280, training accuracy 0.790136
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step 5290, training accuracy 0.792235
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step 5300, training accuracy 0.793284
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step 5310, training accuracy 0.794334
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step 5320, training accuracy 0.794334
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step 5330, training accuracy 0.794334
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step 5330, change in cost 0.0612488
step 5340, training accuracy 0.794334
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step 5360, training accuracy 0.794334
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step 5370, training accuracy 0.794334
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step 5380, training accuracy 0.794334
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step 5390, training accuracy 0.794334
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step 5420, training accuracy 0.795383
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step 5430, training accuracy 0.795383
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step 5440, training accuracy 0.795383
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step 5450, training accuracy 0.795383
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step 5460, training accuracy 0.795383
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step 5470, training accuracy 0.795383
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step 5490, training accuracy 0.794334
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step 5500, training accuracy 0.795383
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step 5520, training accuracy 0.797482
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step 5530, training accuracy 0.798531
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step 5600, training accuracy 0.80063
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step 5620, training accuracy 0.804827
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step 5880, training accuracy 0.816369
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step 5900, training accuracy 0.817419
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step 5900, change in cost 0.0591888
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step 6020, training accuracy 0.820567
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step 6820, training accuracy 0.858342
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step 6870, training accuracy 0.858342
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step 6900, training accuracy 0.860441
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step 6920, training accuracy 0.86149
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step 8280, training accuracy 0.895068
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step 8320, training accuracy 0.896118
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step 8330, training accuracy 0.896118
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step 8340, training accuracy 0.896118
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step 8350, training accuracy 0.896118
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step 8360, training accuracy 0.896118
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step 8380, training accuracy 0.896118
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step 8430, training accuracy 0.896118
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step 8440, training accuracy 0.896118
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step 8460, training accuracy 0.896118
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step 8470, training accuracy 0.896118
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step 8480, training accuracy 0.896118
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step 8500, training accuracy 0.897167
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step 8530, training accuracy 0.896118
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step 8570, training accuracy 0.898216
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step 8590, training accuracy 0.898216
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step 8600, training accuracy 0.898216
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step 8630, training accuracy 0.898216
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step 8640, training accuracy 0.898216
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step 8650, training accuracy 0.898216
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step 8660, training accuracy 0.898216
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step 8660, change in cost 0.0502625
step 8670, training accuracy 0.898216
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step 8670, change in cost 0.0502625
step 8680, training accuracy 0.899265
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step 8680, change in cost 0.0501404
step 8690, training accuracy 0.899265
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step 8700, training accuracy 0.899265
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step 8700, change in cost 0.0501709
step 8710, training accuracy 0.899265
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step 8720, training accuracy 0.899265
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step 8720, change in cost 0.0500031
step 8730, training accuracy 0.899265
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step 8740, training accuracy 0.899265
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step 8740, change in cost 0.0499878
step 8750, training accuracy 0.899265
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step 8750, change in cost 0.0500336
step 8760, training accuracy 0.899265
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step 8770, training accuracy 0.899265
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step 8770, change in cost 0.049942
step 8780, training accuracy 0.899265
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step 8780, change in cost 0.0499573
step 8790, training accuracy 0.899265
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step 8800, training accuracy 0.899265
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step 8810, training accuracy 0.899265
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step 8820, training accuracy 0.899265
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step 8820, change in cost 0.0497742
step 8830, training accuracy 0.899265
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step 8830, change in cost 0.0497131
step 8840, training accuracy 0.899265
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step 8840, change in cost 0.0498505
step 8850, training accuracy 0.899265
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step 8850, change in cost 0.0496826
step 8860, training accuracy 0.899265
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step 8870, training accuracy 0.899265
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step 8880, training accuracy 0.899265
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step 8890, training accuracy 0.899265
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step 8890, change in cost 0.0495605
step 8900, training accuracy 0.899265
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step 8900, change in cost 0.0496063
step 8910, training accuracy 0.900315
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step 8920, training accuracy 0.900315
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step 8930, training accuracy 0.900315
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step 8940, training accuracy 0.900315
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step 8950, training accuracy 0.900315
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step 8950, change in cost 0.0494995
step 8960, training accuracy 0.900315
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step 8960, change in cost 0.0493622
step 8970, training accuracy 0.900315
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step 8980, training accuracy 0.900315
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step 8980, change in cost 0.0493011
step 8990, training accuracy 0.901364
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step 9000, training accuracy 0.901364
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step 9010, training accuracy 0.901364
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step 9010, change in cost 0.0492706
step 9020, training accuracy 0.901364
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step 9020, change in cost 0.0492859
step 9030, training accuracy 0.901364
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step 9040, training accuracy 0.901364
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step 9050, training accuracy 0.902413
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step 9060, training accuracy 0.902413
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step 9070, training accuracy 0.902413
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step 9070, change in cost 0.0490723
step 9080, training accuracy 0.902413
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step 9090, training accuracy 0.902413
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step 9100, training accuracy 0.902413
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step 9110, training accuracy 0.902413
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step 9110, change in cost 0.0489807
step 9120, training accuracy 0.903463
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step 9130, training accuracy 0.903463
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step 9140, training accuracy 0.903463
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step 9150, training accuracy 0.903463
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step 9160, training accuracy 0.903463
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step 9170, training accuracy 0.904512
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step 9180, training accuracy 0.904512
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step 9190, training accuracy 0.904512
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step 9200, training accuracy 0.904512
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step 9220, training accuracy 0.905561
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step 9230, training accuracy 0.905561
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step 9240, training accuracy 0.906611
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step 9260, training accuracy 0.906611
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step 9280, training accuracy 0.90766
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step 9290, training accuracy 0.90766
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step 9300, training accuracy 0.90766
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step 9320, training accuracy 0.90766
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step 9350, training accuracy 0.908709
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step 9380, training accuracy 0.908709
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step 9420, training accuracy 0.908709
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step 9440, training accuracy 0.908709
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step 9450, training accuracy 0.908709
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step 9460, training accuracy 0.908709
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step 9520, training accuracy 0.908709
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step 9600, training accuracy 0.911857
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step 9620, training accuracy 0.912907
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step 9650, training accuracy 0.912907
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step 9660, training accuracy 0.912907
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step 14960, training accuracy 0.934942
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step 14970, training accuracy 0.934942
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step 14980, training accuracy 0.934942
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step 14990, training accuracy 0.934942
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step 15000, training accuracy 0.934942
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step 15010, training accuracy 0.934942
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step 15020, training accuracy 0.934942
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step 15030, training accuracy 0.934942
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step 15040, training accuracy 0.934942
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step 15050, training accuracy 0.934942
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step 15060, training accuracy 0.934942
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step 15060, change in cost 0.0355988
step 15070, training accuracy 0.934942
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step 15080, training accuracy 0.934942
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step 15080, change in cost 0.035614
step 15090, training accuracy 0.934942
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step 15090, change in cost 0.0355835
step 15100, training accuracy 0.934942
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step 15100, change in cost 0.0356445
step 15110, training accuracy 0.934942
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step 15110, change in cost 0.0355072
step 15120, training accuracy 0.934942
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step 15130, training accuracy 0.934942
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step 15140, training accuracy 0.934942
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step 15150, training accuracy 0.934942
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step 15160, training accuracy 0.934942
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step 15180, training accuracy 0.934942
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step 15190, training accuracy 0.934942
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step 15200, training accuracy 0.934942
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step 15210, training accuracy 0.934942
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step 15220, training accuracy 0.934942
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step 15230, training accuracy 0.934942
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step 15230, change in cost 0.0353546
step 15240, training accuracy 0.934942
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step 15250, training accuracy 0.934942
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step 15260, training accuracy 0.934942
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step 15270, training accuracy 0.934942
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step 15280, training accuracy 0.934942
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step 15290, training accuracy 0.934942
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step 15300, training accuracy 0.934942
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step 15300, change in cost 0.0352173
step 15310, training accuracy 0.934942
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step 15310, change in cost 0.0352478
step 15320, training accuracy 0.934942
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step 15320, change in cost 0.0351715
step 15330, training accuracy 0.934942
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step 15330, change in cost 0.035202
step 15340, training accuracy 0.934942
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step 15350, training accuracy 0.934942
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step 15360, training accuracy 0.934942
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step 15360, change in cost 0.0351257
step 15370, training accuracy 0.934942
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step 15370, change in cost 0.0351257
step 15380, training accuracy 0.934942
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step 15380, change in cost 0.0350494
step 15390, training accuracy 0.934942
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step 15390, change in cost 0.0351105
step 15400, training accuracy 0.934942
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step 15400, change in cost 0.0351105
step 15410, training accuracy 0.934942
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step 15410, change in cost 0.0350037
step 15420, training accuracy 0.934942
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step 15420, change in cost 0.0350189
step 15430, training accuracy 0.934942
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step 15430, change in cost 0.0350189
step 15440, training accuracy 0.934942
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step 15450, training accuracy 0.934942
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step 15450, change in cost 0.0350494
step 15460, training accuracy 0.934942
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step 15460, change in cost 0.0348969
step 15470, training accuracy 0.934942
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step 15480, training accuracy 0.934942
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step 15490, training accuracy 0.934942
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step 15500, training accuracy 0.934942
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step 15500, change in cost 0.0348511
step 15510, training accuracy 0.934942
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step 15520, training accuracy 0.934942
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step 15520, change in cost 0.0348816
step 15530, training accuracy 0.934942
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step 15540, training accuracy 0.934942
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step 15540, change in cost 0.0348511
step 15550, training accuracy 0.934942
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step 15560, training accuracy 0.934942
step 15560, cost 160.709
step 15560, change in cost 0.0348511
step 15570, training accuracy 0.934942
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step 15580, training accuracy 0.934942
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step 15590, training accuracy 0.934942
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step 15600, training accuracy 0.934942
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step 15600, change in cost 0.0347443
step 15610, training accuracy 0.934942
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step 15610, change in cost 0.0346985
step 15620, training accuracy 0.934942
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step 15620, change in cost 0.0347443
step 15630, training accuracy 0.934942
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step 15630, change in cost 0.0346222
step 15640, training accuracy 0.934942
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step 15650, training accuracy 0.934942
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step 15650, change in cost 0.0346069
step 15660, training accuracy 0.934942
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step 15670, training accuracy 0.934942
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step 15670, change in cost 0.0346375
step 15680, training accuracy 0.934942
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step 15690, training accuracy 0.934942
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step 15690, change in cost 0.0345612
step 15700, training accuracy 0.934942
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step 15700, change in cost 0.0346069
step 15710, training accuracy 0.934942
step 15710, cost 160.189
step 15710, change in cost 0.0344391
step 15720, training accuracy 0.934942
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step 15720, change in cost 0.0345459
step 15730, training accuracy 0.934942
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step 15730, change in cost 0.0344696
step 15740, training accuracy 0.934942
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step 15740, change in cost 0.0345306
step 15750, training accuracy 0.934942
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step 15750, change in cost 0.0344238
step 15760, training accuracy 0.934942
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step 15760, change in cost 0.0344086
step 15770, training accuracy 0.934942
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step 15770, change in cost 0.0345154
step 15780, training accuracy 0.934942
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step 15780, change in cost 0.0344238
step 15790, training accuracy 0.934942
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step 15800, training accuracy 0.934942
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step 15800, change in cost 0.0344238
step 15810, training accuracy 0.934942
step 15810, cost 159.845
step 15810, change in cost 0.0343018
step 15820, training accuracy 0.934942
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step 15820, change in cost 0.0343323
step 15830, training accuracy 0.934942
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step 15840, training accuracy 0.934942
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step 15840, change in cost 0.0343323
step 15850, training accuracy 0.934942
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step 15860, training accuracy 0.934942
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step 15870, training accuracy 0.934942
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step 15880, training accuracy 0.934942
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step 15890, training accuracy 0.934942
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step 15900, training accuracy 0.934942
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step 15910, training accuracy 0.934942
step 15910, cost 159.502
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step 15920, training accuracy 0.934942
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step 15920, change in cost 0.0341187
step 15930, training accuracy 0.934942
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step 15930, change in cost 0.0342102
step 15940, training accuracy 0.934942
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step 15950, training accuracy 0.934942
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step 15950, change in cost 0.0341339
step 15960, training accuracy 0.934942
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step 15970, training accuracy 0.934942
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step 15970, change in cost 0.0340424
step 15980, training accuracy 0.934942
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step 15990, training accuracy 0.934942
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step 16000, training accuracy 0.934942
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step 16000, change in cost 0.0340271
step 16010, training accuracy 0.934942
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step 16020, training accuracy 0.934942
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step 16020, change in cost 0.0340424
step 16030, training accuracy 0.934942
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step 16030, change in cost 0.0339355
step 16040, training accuracy 0.934942
step 16040, cost 159.059
step 16040, change in cost 0.0339661
step 16050, training accuracy 0.934942
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step 16050, change in cost 0.0340118
step 16060, training accuracy 0.934942
step 16060, cost 158.991
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step 16070, training accuracy 0.934942
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step 16070, change in cost 0.0339661
step 16080, training accuracy 0.934942
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step 16080, change in cost 0.0338745
step 16090, training accuracy 0.934942
step 16090, cost 158.889
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step 16100, training accuracy 0.934942
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step 16100, change in cost 0.0338287
step 16110, training accuracy 0.934942
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step 16120, training accuracy 0.934942
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step 16130, training accuracy 0.934942
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step 16140, training accuracy 0.934942
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step 16150, training accuracy 0.934942
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step 16160, training accuracy 0.934942
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step 16180, training accuracy 0.934942
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step 16190, training accuracy 0.934942
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step 16200, training accuracy 0.934942
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step 16210, training accuracy 0.934942
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step 16220, training accuracy 0.934942
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step 16230, training accuracy 0.934942
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step 16240, training accuracy 0.934942
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step 16250, training accuracy 0.934942
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step 16280, training accuracy 0.934942
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step 16320, training accuracy 0.934942
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step 16330, training accuracy 0.934942
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step 16340, training accuracy 0.934942
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step 16350, training accuracy 0.934942
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step 16360, training accuracy 0.934942
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step 16370, training accuracy 0.934942
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step 16380, training accuracy 0.934942
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step 16390, training accuracy 0.934942
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step 16400, training accuracy 0.934942
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step 16400, change in cost 0.033371
step 16410, training accuracy 0.934942
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step 16420, training accuracy 0.934942
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step 16420, change in cost 0.0333099
step 16430, training accuracy 0.934942
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step 16440, training accuracy 0.934942
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step 16450, training accuracy 0.934942
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step 16460, training accuracy 0.934942
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step 16470, training accuracy 0.934942
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step 16480, training accuracy 0.934942
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step 16490, training accuracy 0.934942
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step 16500, training accuracy 0.934942
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step 16510, training accuracy 0.934942
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step 16520, training accuracy 0.934942
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step 16530, training accuracy 0.934942
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step 16540, training accuracy 0.934942
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step 16550, training accuracy 0.934942
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step 16560, training accuracy 0.934942
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step 16570, training accuracy 0.934942
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step 16590, training accuracy 0.934942
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step 16600, training accuracy 0.934942
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step 16610, training accuracy 0.934942
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step 16610, change in cost 0.0330963
step 16620, training accuracy 0.934942
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step 16640, training accuracy 0.934942
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step 16640, change in cost 0.0330353
step 16650, training accuracy 0.934942
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step 16660, training accuracy 0.934942
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step 16660, change in cost 0.0329895
step 16670, training accuracy 0.934942
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step 16680, training accuracy 0.934942
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step 16680, change in cost 0.0329132
step 16690, training accuracy 0.934942
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step 16700, training accuracy 0.934942
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step 16710, training accuracy 0.934942
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step 16710, change in cost 0.0329742
step 16720, training accuracy 0.934942
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step 16720, change in cost 0.0328217
step 16730, training accuracy 0.934942
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step 16740, training accuracy 0.934942
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step 16750, training accuracy 0.934942
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step 16750, change in cost 0.0328217
step 16760, training accuracy 0.934942
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step 16770, training accuracy 0.934942
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step 16780, training accuracy 0.934942
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step 16780, change in cost 0.0328369
step 16790, training accuracy 0.934942
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step 16800, training accuracy 0.934942
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step 16810, training accuracy 0.934942
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step 16820, training accuracy 0.934942
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step 16820, change in cost 0.0327301
step 16830, training accuracy 0.934942
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step 16830, change in cost 0.0327606
step 16840, training accuracy 0.934942
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step 16850, training accuracy 0.934942
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step 16860, training accuracy 0.934942
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step 16860, change in cost 0.0327148
step 16870, training accuracy 0.934942
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step 16890, training accuracy 0.934942
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step 16920, training accuracy 0.934942
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step 16920, change in cost 0.0325928
step 16930, training accuracy 0.934942
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step 16940, training accuracy 0.934942
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step 16950, training accuracy 0.934942
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step 16960, training accuracy 0.934942
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step 17030, change in cost 0.0324402
step 17040, training accuracy 0.934942
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step 17040, change in cost 0.0323639
step 17050, training accuracy 0.934942
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step 17050, change in cost 0.0323639
step 17060, training accuracy 0.934942
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step 17060, change in cost 0.0323639
step 17070, training accuracy 0.934942
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step 17070, change in cost 0.0323639
step 17080, training accuracy 0.934942
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step 17080, change in cost 0.0323181
step 17090, training accuracy 0.934942
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step 17090, change in cost 0.0323181
step 17100, training accuracy 0.934942
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step 17100, change in cost 0.0322876
step 17110, training accuracy 0.934942
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step 17110, change in cost 0.0323029
step 17120, training accuracy 0.935992
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step 17120, change in cost 0.0322876
step 17130, training accuracy 0.935992
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step 17130, change in cost 0.0321655
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step 17960, training accuracy 0.937041
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step 18620, training accuracy 0.940189
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step 24920, training accuracy 0.948583
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step 24930, training accuracy 0.948583
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step 25020, training accuracy 0.948583
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step 25750, training accuracy 0.948583
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step 25780, training accuracy 0.948583
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step 25790, training accuracy 0.948583
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step 25920, training accuracy 0.949633
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step 25920, change in cost 0.0221252
step 25930, training accuracy 0.949633
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step 25930, change in cost 0.0220795
step 25940, training accuracy 0.949633
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step 25940, change in cost 0.0220947
step 25950, training accuracy 0.949633
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step 25960, training accuracy 0.949633
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step 25970, cost 131.88
step 25970, change in cost 0.0220032
step 25980, training accuracy 0.949633
step 25980, cost 131.858
step 25980, change in cost 0.0220795
step 25990, training accuracy 0.949633
step 25990, cost 131.836
step 25990, change in cost 0.022049
step 26000, training accuracy 0.949633
step 26000, cost 131.814
step 26000, change in cost 0.0220642
step 26010, training accuracy 0.949633
step 26010, cost 131.792
step 26010, change in cost 0.022049
step 26020, training accuracy 0.949633
step 26020, cost 131.77
step 26020, change in cost 0.0219879
step 26030, training accuracy 0.949633
step 26030, cost 131.748
step 26030, change in cost 0.0219574
step 26040, training accuracy 0.949633
step 26040, cost 131.726
step 26040, change in cost 0.0220795
step 26050, training accuracy 0.949633
step 26050, cost 131.704
step 26050, change in cost 0.0219421
step 26060, training accuracy 0.949633
step 26060, cost 131.682
step 26060, change in cost 0.022049
step 26070, training accuracy 0.949633
step 26070, cost 131.66
step 26070, change in cost 0.0219269
step 26080, training accuracy 0.949633
step 26080, cost 131.638
step 26080, change in cost 0.0219727
step 26090, training accuracy 0.949633
step 26090, cost 131.616
step 26090, change in cost 0.0219574
step 26100, training accuracy 0.949633
step 26100, cost 131.594
step 26100, change in cost 0.0219879
step 26110, training accuracy 0.949633
step 26110, cost 131.572
step 26110, change in cost 0.0219574
step 26120, training accuracy 0.949633
step 26120, cost 131.55
step 26120, change in cost 0.0218658
step 26130, training accuracy 0.949633
step 26130, cost 131.528
step 26130, change in cost 0.0220032
step 26140, training accuracy 0.950682
step 26140, cost 131.506
step 26140, change in cost 0.0218964
step 26150, training accuracy 0.950682
step 26150, cost 131.484
step 26150, change in cost 0.0219269
step 26160, training accuracy 0.950682
step 26160, cost 131.462
step 26160, change in cost 0.0218506
step 26170, training accuracy 0.950682
step 26170, cost 131.44
step 26170, change in cost 0.0219269
step 26180, training accuracy 0.950682
step 26180, cost 131.418
step 26180, change in cost 0.0218658
step 26190, training accuracy 0.950682
step 26190, cost 131.397
step 26190, change in cost 0.0218658
step 26200, training accuracy 0.950682
step 26200, cost 131.375
step 26200, change in cost 0.0218658
step 26210, training accuracy 0.950682
step 26210, cost 131.353
step 26210, change in cost 0.0218811
step 26220, training accuracy 0.950682
step 26220, cost 131.331
step 26220, change in cost 0.0218353
step 26230, training accuracy 0.950682
step 26230, cost 131.309
step 26230, change in cost 0.0218353
step 26240, training accuracy 0.950682
step 26240, cost 131.287
step 26240, change in cost 0.0218658
step 26250, training accuracy 0.950682
step 26250, cost 131.266
step 26250, change in cost 0.0217743
step 26260, training accuracy 0.950682
step 26260, cost 131.244
step 26260, change in cost 0.0218506
step 26270, training accuracy 0.950682
step 26270, cost 131.222
step 26270, change in cost 0.0218048
step 26280, training accuracy 0.950682
step 26280, cost 131.2
step 26280, change in cost 0.0218048
step 26290, training accuracy 0.950682
step 26290, cost 131.178
step 26290, change in cost 0.0218048
step 26300, training accuracy 0.950682
step 26300, cost 131.156
step 26300, change in cost 0.0217896
step 26310, training accuracy 0.950682
step 26310, cost 131.135
step 26310, change in cost 0.0217133
step 26320, training accuracy 0.950682
step 26320, cost 131.113
step 26320, change in cost 0.0217743
step 26330, training accuracy 0.950682
step 26330, cost 131.091
step 26330, change in cost 0.0218048
step 26340, training accuracy 0.950682
step 26340, cost 131.069
step 26340, change in cost 0.0217438
step 26350, training accuracy 0.950682
step 26350, cost 131.048
step 26350, change in cost 0.0217438
step 26360, training accuracy 0.950682
step 26360, cost 131.026
step 26360, change in cost 0.0217133
step 26370, training accuracy 0.950682
step 26370, cost 131.004
step 26370, change in cost 0.0217438
step 26380, training accuracy 0.950682
step 26380, cost 130.983
step 26380, change in cost 0.021698
step 26390, training accuracy 0.950682
step 26390, cost 130.961
step 26390, change in cost 0.0217285
step 26400, training accuracy 0.950682
step 26400, cost 130.939
step 26400, change in cost 0.0216675
step 26410, training accuracy 0.950682
step 26410, cost 130.917
step 26410, change in cost 0.0217133
step 26420, training accuracy 0.950682
step 26420, cost 130.896
step 26420, change in cost 0.0216675
step 26430, training accuracy 0.950682
step 26430, cost 130.874
step 26430, change in cost 0.0216827
step 26440, training accuracy 0.950682
step 26440, cost 130.852
step 26440, change in cost 0.0216675
step 26450, training accuracy 0.950682
step 26450, cost 130.831
step 26450, change in cost 0.0216522
step 26460, training accuracy 0.950682
step 26460, cost 130.809
step 26460, change in cost 0.0216522
step 26470, training accuracy 0.950682
step 26470, cost 130.788
step 26470, change in cost 0.0215912
step 26480, training accuracy 0.950682
step 26480, cost 130.766
step 26480, change in cost 0.0216522
step 26490, training accuracy 0.951731
step 26490, cost 130.744
step 26490, change in cost 0.0216064
step 26500, training accuracy 0.951731
step 26500, cost 130.723
step 26500, change in cost 0.0216522
step 26510, training accuracy 0.951731
step 26510, cost 130.701
step 26510, change in cost 0.0215607
step 26520, training accuracy 0.951731
step 26520, cost 130.679
step 26520, change in cost 0.021637
step 26530, training accuracy 0.951731
step 26530, cost 130.658
step 26530, change in cost 0.0215912
step 26540, training accuracy 0.951731
step 26540, cost 130.636
step 26540, change in cost 0.0215912
step 26550, training accuracy 0.951731
step 26550, cost 130.615
step 26550, change in cost 0.0215607
step 26560, training accuracy 0.951731
step 26560, cost 130.593
step 26560, change in cost 0.0215607
step 26570, training accuracy 0.951731
step 26570, cost 130.572
step 26570, change in cost 0.0215607
step 26580, training accuracy 0.951731
step 26580, cost 130.55
step 26580, change in cost 0.0215454
step 26590, training accuracy 0.951731
step 26590, cost 130.528
step 26590, change in cost 0.0215302
step 26600, training accuracy 0.951731
step 26600, cost 130.507
step 26600, change in cost 0.0215302
step 26610, training accuracy 0.951731
step 26610, cost 130.485
step 26610, change in cost 0.0215454
step 26620, training accuracy 0.951731
step 26620, cost 130.464
step 26620, change in cost 0.0214996
step 26630, training accuracy 0.951731
step 26630, cost 130.442
step 26630, change in cost 0.0215454
step 26640, training accuracy 0.951731
step 26640, cost 130.421
step 26640, change in cost 0.0215302
step 26650, training accuracy 0.951731
step 26650, cost 130.399
step 26650, change in cost 0.0214386
step 26660, training accuracy 0.951731
step 26660, cost 130.378
step 26660, change in cost 0.0214691
step 26670, training accuracy 0.951731
step 26670, cost 130.356
step 26670, change in cost 0.0214996
step 26680, training accuracy 0.951731
step 26680, cost 130.335
step 26680, change in cost 0.0214386
step 26690, training accuracy 0.951731
step 26690, cost 130.313
step 26690, change in cost 0.0214996
step 26700, training accuracy 0.951731
step 26700, cost 130.292
step 26700, change in cost 0.0214081
step 26710, training accuracy 0.951731
step 26710, cost 130.271
step 26710, change in cost 0.0214691
step 26720, training accuracy 0.951731
step 26720, cost 130.249
step 26720, change in cost 0.0213928
step 26730, training accuracy 0.951731
step 26730, cost 130.228
step 26730, change in cost 0.0214386
step 26740, training accuracy 0.951731
step 26740, cost 130.206
step 26740, change in cost 0.0214081
step 26750, training accuracy 0.951731
step 26750, cost 130.185
step 26750, change in cost 0.0214386
step 26760, training accuracy 0.951731
step 26760, cost 130.163
step 26760, change in cost 0.0214081
step 26770, training accuracy 0.951731
step 26770, cost 130.142
step 26770, change in cost 0.021347
step 26780, training accuracy 0.951731
step 26780, cost 130.121
step 26780, change in cost 0.0214233
step 26790, training accuracy 0.951731
step 26790, cost 130.099
step 26790, change in cost 0.0214081
step 26800, training accuracy 0.951731
step 26800, cost 130.078
step 26800, change in cost 0.021286
step 26810, training accuracy 0.951731
step 26810, cost 130.057
step 26810, change in cost 0.0213623
step 26820, training accuracy 0.951731
step 26820, cost 130.035
step 26820, change in cost 0.0213623
step 26830, training accuracy 0.951731
step 26830, cost 130.014
step 26830, change in cost 0.0213928
step 26840, training accuracy 0.951731
step 26840, cost 129.993
step 26840, change in cost 0.021286
step 26850, training accuracy 0.951731
step 26850, cost 129.971
step 26850, change in cost 0.021347
step 26860, training accuracy 0.951731
step 26860, cost 129.95
step 26860, change in cost 0.0213165
step 26870, training accuracy 0.951731
step 26870, cost 129.929
step 26870, change in cost 0.0213165
step 26880, training accuracy 0.951731
step 26880, cost 129.907
step 26880, change in cost 0.0212708
step 26890, training accuracy 0.951731
step 26890, cost 129.886
step 26890, change in cost 0.021347
step 26900, training accuracy 0.951731
step 26900, cost 129.865
step 26900, change in cost 0.0212555
step 26910, training accuracy 0.951731
step 26910, cost 129.844
step 26910, change in cost 0.0212555
step 26920, training accuracy 0.951731
step 26920, cost 129.822
step 26920, change in cost 0.021286
step 26930, training accuracy 0.951731
step 26930, cost 129.801
step 26930, change in cost 0.0212708
step 26940, training accuracy 0.951731
step 26940, cost 129.78
step 26940, change in cost 0.021225
step 26950, training accuracy 0.951731
step 26950, cost 129.759
step 26950, change in cost 0.021225
step 26960, training accuracy 0.951731
step 26960, cost 129.737
step 26960, change in cost 0.021286
step 26970, training accuracy 0.951731
step 26970, cost 129.716
step 26970, change in cost 0.021225
step 26980, training accuracy 0.951731
step 26980, cost 129.695
step 26980, change in cost 0.0212097
step 26990, training accuracy 0.951731
step 26990, cost 129.674
step 26990, change in cost 0.0212555
final accuracy on test set: 0.904762

In [21]:

    

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

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


    

In [ ]: