In [1]:

    

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import os
import sys
from six.moves import cPickle as pickle
%matplotlib inline


    

    




/home/josh/anaconda2/envs/tensorflow/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')

In [2]:

    

#pickle_file = 'train.pickle'
'''
with open(pickle_file, 'rb') as f:
    save = pickle.load(f)
    train_X_1 = save['data']
    train_outcome_1 = save['outcome']
    del save  # hint to help gc free up memory
'''
    
pickle_file = 'train2.pickle'

with open(pickle_file, 'rb') as f:
    save = pickle.load(f)
    train_X_0 = save['data']
    train_outcome_0 = save['outcome']
    del save  # hint to help gc free up memory
    
'''
pickle_file = 'test.pickle'

with open(pickle_file, 'rb') as f:
    save = pickle.load(f)
    test_X_1 = save['data']
    test_outcome_1 = save['outcome']
    del save  # hint to help gc free up memory
    
'''

pickle_file = 'test2.pickle'

with open(pickle_file, 'rb') as f:
    save = pickle.load(f)
    test_X_0 = save['data']
    test_outcome_0 = save['outcome']
    del save  # hint to help gc free up memory


    

In [3]:

    

#reformat the label
#for each digit, add a 'end_digit' as '10'
#for each label, add a digit size
#each of them is a one-hot coding

def label_reformat(label, max_size = 5):
    digit_size = np.asarray([len(x) for x in label])
    digit_size[digit_size > max_size]= max_size
    digit_size = ((np.arange(max_size)+1) == digit_size[:,None]).astype(np.float32)
    
    digits = {}
    end_digit = 10.0
    for i in range(max_size):
        digit_coding = np.asarray( [x[i] if len(x)>i else end_digit for x in label])
        digit_coding = (np.arange(end_digit+1) == digit_coding[:,None]).astype(np.float32)
        digits['digit_'+ str(i)] = digit_coding
        
    return digit_size, digits


    

In [4]:

    

#train_X_0 = np.vstack((train_X_1 ,train_X_2 ))


    

In [5]:

    

train_X_0.shape


    

    
Out[5]:





(33402, 64, 64, 3)

In [6]:

    

#train_X_0 = np.vstack((train_X_1 ,train_X_2 ))

image_size = train_X_0.shape[1]
num_channels = train_X_0.shape[3]
batch_size = 200
val_size = 50
test_size = 50


#train_label = train_outcome_1['label'] +  train_outcome_2['label']
train_label = train_outcome_0['label']
train_digit_size, train_digits = label_reformat(train_label)
train_X = train_X_0


val_label = test_outcome_0['label']
val_digit_size, val_digits = label_reformat(val_label)
val_X = test_X_0

val_size = val_X.shape[0]


    

In [7]:

    

print train_digit_size.shape
print train_digits['digit_0'].shape
print train_X.shape


    

    




(33402, 5)
(33402, 11)
(33402, 64, 64, 3)

In [8]:

    

plt.imshow(train_X[0,:,:,:])
plt.show()
print train_digits['digit_0'][0]
print train_digits['digit_1'][0]


    

    













    




[ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.]

In [9]:

    

plt.imshow(val_X[1,:,:,:])
plt.show()
print val_digits['digit_0'][1]
print val_digits['digit_1'][1]


    

    













    




[ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]
[ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]

In [10]:

    

def next_batch(X, y_dsize, y_ds, batch_size=50, replace = True):
    idx = np.random.choice(X.shape[0],batch_size, replace = replace)
    batch_x = X[idx,:,:,:]
    batch_y_dsize = y_dsize[idx,:]
    batch_y_d1 = y_ds['digit_0'][idx,:]
    batch_y_d2 = y_ds['digit_1'][idx,:]
    batch_y_d3 = y_ds['digit_2'][idx,:]
    batch_y_d4 = y_ds['digit_3'][idx,:]
    batch_y_d5 = y_ds['digit_4'][idx,:]
    
    return batch_x, batch_y_dsize, batch_y_d1, batch_y_d2, batch_y_d3, batch_y_d4, batch_y_d5


    

In [13]:

    

graph = tf.Graph()
with graph.as_default():
    
    def weight_variable(shape):
        initial = tf.truncated_normal(shape, stddev=0.1)
        return tf.Variable(initial)

    def bias_variable(shape):
        initial = tf.constant(0.1, shape=shape)
        return tf.Variable(initial)

    def conv2d(x, W):
        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

    def max_pool_2x2(x):
        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    
    x_image = tf.placeholder(tf.float32, shape=(batch_size, image_size, image_size, num_channels))

    y_d1 = tf.placeholder(tf.float32, shape=(batch_size, 11))
    y_d2 = tf.placeholder(tf.float32, shape=(batch_size, 11))
    y_d3 = tf.placeholder(tf.float32, shape=(batch_size, 11))
    y_d4 = tf.placeholder(tf.float32, shape=(batch_size, 11))
    y_d5 = tf.placeholder(tf.float32, shape=(batch_size, 11))

    y_dsize = tf.placeholder(tf.float32, shape=(batch_size, 5))
    
    val_x_image = tf.placeholder(tf.float32, shape=(val_size, image_size, image_size, num_channels))

    val_y_d1 = tf.placeholder(tf.float32, shape=(val_size, 11))
    val_y_d2 = tf.placeholder(tf.float32, shape=(val_size, 11))
    val_y_d3 = tf.placeholder(tf.float32, shape=(val_size, 11))
    val_y_d4 = tf.placeholder(tf.float32, shape=(val_size, 11))
    val_y_d5 = tf.placeholder(tf.float32, shape=(val_size, 11))

    val_y_dsize = tf.placeholder(tf.float32, shape=(val_size, 5))

    test_x_image = tf.placeholder(tf.float32, shape=(test_size, image_size, image_size, num_channels))

    test_y_d1 = tf.placeholder(tf.float32, shape=(test_size, 11))
    test_y_d2 = tf.placeholder(tf.float32, shape=(test_size, 11))
    test_y_d3 = tf.placeholder(tf.float32, shape=(test_size, 11))
    test_y_d4 = tf.placeholder(tf.float32, shape=(test_size, 11))
    test_y_d5 = tf.placeholder(tf.float32, shape=(test_size, 11))

    test_y_dsize = tf.placeholder(tf.float32, shape=(test_size, 5))
    
    
    W_conv1 = weight_variable([5, 5, num_channels, 32])
    b_conv1 = bias_variable([32])

    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)
    
    
    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])

    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)
    
    W_fc1 = weight_variable([16 * 16 * 64, 1024])
    b_fc1 = bias_variable([1024])

    h_pool2_flat = tf.reshape(h_pool2, [-1, 16*16*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
    
    #W_conv3 = weight_variable([5, 5, 64, 256])
    #b_conv3 = bias_variable([256])

    #h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)
    #h_pool3 = max_pool_2x2(h_conv3)
    
    #W_fc1 = weight_variable([8 * 8 * 256, 1024])
    #b_fc1 = bias_variable([1024])

    #h_pool3_flat = tf.reshape(h_pool3, [-1, 8*8*256])
    #h_fc1 = tf.nn.relu(tf.matmul(h_pool3_flat, W_fc1) + b_fc1)
    
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
    
    
    #first digit
    W_fc2_d1 = weight_variable([1024, 11])
    b_fc2_d1 = bias_variable([11])

    y_conv_d1 = tf.matmul(h_fc1_drop, W_fc2_d1) + b_fc2_d1

    #second digit
    W_fc2_d2 = weight_variable([1024, 11])
    b_fc2_d2 = bias_variable([11])

    y_conv_d2 = tf.matmul(h_fc1_drop, W_fc2_d2) + b_fc2_d2

    #third digit
    W_fc2_d3 = weight_variable([1024, 11])
    b_fc2_d3 = bias_variable([11])

    y_conv_d3 = tf.matmul(h_fc1_drop, W_fc2_d3) + b_fc2_d3

    #fourth digit
    W_fc2_d4 = weight_variable([1024, 11])
    b_fc2_d4 = bias_variable([11])

    y_conv_d4 = tf.matmul(h_fc1_drop, W_fc2_d4) + b_fc2_d4

    #fifth digit
    W_fc2_d5 = weight_variable([1024, 11])
    b_fc2_d5 = bias_variable([11])

    y_conv_d5 = tf.matmul(h_fc1_drop, W_fc2_d5) + b_fc2_d5

    #digit size
    W_fc2_dsize = weight_variable([1024, 5])
    b_fc2_dsize = bias_variable([5])

    y_conv_dsize = tf.matmul(h_fc1_drop, W_fc2_dsize) + b_fc2_dsize

    
    cross_entropy = ( tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv_d1, y_d1)) 
                     + tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv_d2, y_d2))
                     + tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv_d3, y_d3))
                     + tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv_d4, y_d4))
                     + tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv_d5, y_d5))
                     + tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv_dsize, y_dsize))
                     )

    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
    
    
    #let's just check the first digit
    #correct_prediction = tf.equal(tf.argmax(y_conv_d1,1), tf.argmax(y_d1,1)) 
    correct_prediction = ( tf.cast( tf.equal(tf.argmax(y_conv_d1,1), tf.argmax(y_d1,1)) , tf.float32) 
                          * tf.cast( tf.equal(tf.argmax(y_conv_d1,1), tf.argmax(y_d1,1)) , tf.float32)
                          * tf.cast( tf.equal(tf.argmax(y_conv_d1,1), tf.argmax(y_d1,1)) , tf.float32)
                          * tf.cast( tf.equal(tf.argmax(y_conv_d1,1), tf.argmax(y_d1,1)) , tf.float32)
                          * tf.cast( tf.equal(tf.argmax(y_conv_d1,1), tf.argmax(y_d1,1)) , tf.float32)
                          )
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))


    

In [21]:

    

num_steps = 50000
summary_frequency = 50


tr_acc, ts_acc = [], []

with tf.Session(graph=graph) as session:

    tf.initialize_all_variables().run()
    print('Initialized')

    for i in range(num_steps):
        (batch_x, batch_y_dsize, 
         batch_y_d1, batch_y_d2, 
         batch_y_d3, batch_y_d4, batch_y_d5) = next_batch(train_X, 
                                                          train_digit_size, 
                                                          train_digits, batch_size)
        feed_dict={
                x_image: batch_x, y_dsize: batch_y_dsize,
                y_d1: batch_y_d1, y_d2: batch_y_d2, y_d3: batch_y_d3,
                y_d4: batch_y_d4, y_d5: batch_y_d5,
                keep_prob: 0.5}
        
        session.run(train_step,feed_dict=feed_dict)
    
        if i%summary_frequency == 0:
            train_accuracy = accuracy.eval(feed_dict=feed_dict)
            print("step %d, training accuracy %g"%(i, train_accuracy))
            tr_acc.append(train_accuracy)
        
        #if i%(summary_frequency*10) ==0:
            (batch_x, batch_y_dsize, 
             batch_y_d1, batch_y_d2, 
             batch_y_d3, batch_y_d4, batch_y_d5) = next_batch(val_X, 
                                                              val_digit_size, 
                                                              val_digits, batch_size)
            val_accuracy = accuracy.eval(feed_dict={
                        x_image: batch_x, y_dsize: batch_y_dsize,
                        y_d1: batch_y_d1, y_d2: batch_y_d2, y_d3: batch_y_d3,
                        y_d4: batch_y_d4, y_d5: batch_y_d5,
                        keep_prob: 1.0})
            ts_acc.append(val_accuracy)
            print("step %d, val accuracy %g"%(i, val_accuracy))
        
tr_acc, ts_acc = np.array(tr_acc), np.array(ts_acc)


    

    




Initialized
step 0, training accuracy 0.125
step 0, val accuracy 0.105
step 50, training accuracy 0.235
step 50, val accuracy 0.27
step 100, training accuracy 0.22
step 100, val accuracy 0.27
step 150, training accuracy 0.24
step 150, val accuracy 0.325
step 200, training accuracy 0.25
step 200, val accuracy 0.27
step 250, training accuracy 0.27
step 250, val accuracy 0.32
step 300, training accuracy 0.24
step 300, val accuracy 0.36
step 350, training accuracy 0.27
step 350, val accuracy 0.3
step 400, training accuracy 0.26
step 400, val accuracy 0.3
step 450, training accuracy 0.29
step 450, val accuracy 0.305
step 500, training accuracy 0.285
step 500, val accuracy 0.29
step 550, training accuracy 0.26
step 550, val accuracy 0.405
step 600, training accuracy 0.335
step 600, val accuracy 0.395
step 650, training accuracy 0.315
step 650, val accuracy 0.355
step 700, training accuracy 0.285
step 700, val accuracy 0.34
step 750, training accuracy 0.39
step 750, val accuracy 0.375
step 800, training accuracy 0.275
step 800, val accuracy 0.38
step 850, training accuracy 0.34
step 850, val accuracy 0.395
step 900, training accuracy 0.395
step 900, val accuracy 0.33
step 950, training accuracy 0.39
step 950, val accuracy 0.445
step 1000, training accuracy 0.385
step 1000, val accuracy 0.47
step 1050, training accuracy 0.45
step 1050, val accuracy 0.46
step 1100, training accuracy 0.46
step 1100, val accuracy 0.44
step 1150, training accuracy 0.485
step 1150, val accuracy 0.435
step 1200, training accuracy 0.4
step 1200, val accuracy 0.54
step 1250, training accuracy 0.45
step 1250, val accuracy 0.52
step 1300, training accuracy 0.445
step 1300, val accuracy 0.515
step 1350, training accuracy 0.475
step 1350, val accuracy 0.585
step 1400, training accuracy 0.52
step 1400, val accuracy 0.52
step 1450, training accuracy 0.52
step 1450, val accuracy 0.6
step 1500, training accuracy 0.48
step 1500, val accuracy 0.59
step 1550, training accuracy 0.535
step 1550, val accuracy 0.525
step 1600, training accuracy 0.525
step 1600, val accuracy 0.58
step 1650, training accuracy 0.595
step 1650, val accuracy 0.56
step 1700, training accuracy 0.6
step 1700, val accuracy 0.625
step 1750, training accuracy 0.545
step 1750, val accuracy 0.575
step 1800, training accuracy 0.555
step 1800, val accuracy 0.59
step 1850, training accuracy 0.595
step 1850, val accuracy 0.635
step 1900, training accuracy 0.545
step 1900, val accuracy 0.63
step 1950, training accuracy 0.635
step 1950, val accuracy 0.655
step 2000, training accuracy 0.6
step 2000, val accuracy 0.64
step 2050, training accuracy 0.615
step 2050, val accuracy 0.65
step 2100, training accuracy 0.635
step 2100, val accuracy 0.7
step 2150, training accuracy 0.685
step 2150, val accuracy 0.665
step 2200, training accuracy 0.665
step 2200, val accuracy 0.685
step 2250, training accuracy 0.59
step 2250, val accuracy 0.625
step 2300, training accuracy 0.675
step 2300, val accuracy 0.645
step 2350, training accuracy 0.69
step 2350, val accuracy 0.67
step 2400, training accuracy 0.66
step 2400, val accuracy 0.69
step 2450, training accuracy 0.64
step 2450, val accuracy 0.655
step 2500, training accuracy 0.67
step 2500, val accuracy 0.665
step 2550, training accuracy 0.635
step 2550, val accuracy 0.685
step 2600, training accuracy 0.695
step 2600, val accuracy 0.665
step 2650, training accuracy 0.69
step 2650, val accuracy 0.725
step 2700, training accuracy 0.675
step 2700, val accuracy 0.68
step 2750, training accuracy 0.655
step 2750, val accuracy 0.71
step 2800, training accuracy 0.595
step 2800, val accuracy 0.68
step 2850, training accuracy 0.69
step 2850, val accuracy 0.695
step 2900, training accuracy 0.75
step 2900, val accuracy 0.74
step 2950, training accuracy 0.725
step 2950, val accuracy 0.765
step 3000, training accuracy 0.725
step 3000, val accuracy 0.7
step 3050, training accuracy 0.705
step 3050, val accuracy 0.75
step 3100, training accuracy 0.7
step 3100, val accuracy 0.67
step 3150, training accuracy 0.71
step 3150, val accuracy 0.745
step 3200, training accuracy 0.695
step 3200, val accuracy 0.76
step 3250, training accuracy 0.75
step 3250, val accuracy 0.76
step 3300, training accuracy 0.685
step 3300, val accuracy 0.71
step 3350, training accuracy 0.715
step 3350, val accuracy 0.72
step 3400, training accuracy 0.715
step 3400, val accuracy 0.755
step 3450, training accuracy 0.73
step 3450, val accuracy 0.72
step 3500, training accuracy 0.765
step 3500, val accuracy 0.745
step 3550, training accuracy 0.745
step 3550, val accuracy 0.72
step 3600, training accuracy 0.735
step 3600, val accuracy 0.785
step 3650, training accuracy 0.76
step 3650, val accuracy 0.71
step 3700, training accuracy 0.73
step 3700, val accuracy 0.775
step 3750, training accuracy 0.77
step 3750, val accuracy 0.75
step 3800, training accuracy 0.775
step 3800, val accuracy 0.715
step 3850, training accuracy 0.765
step 3850, val accuracy 0.77
step 3900, training accuracy 0.775
step 3900, val accuracy 0.75
step 3950, training accuracy 0.785
step 3950, val accuracy 0.725
step 4000, training accuracy 0.76
step 4000, val accuracy 0.785
step 4050, training accuracy 0.745
step 4050, val accuracy 0.715
step 4100, training accuracy 0.735
step 4100, val accuracy 0.77
step 4150, training accuracy 0.795
step 4150, val accuracy 0.73
step 4200, training accuracy 0.715
step 4200, val accuracy 0.76
step 4250, training accuracy 0.805
step 4250, val accuracy 0.77
step 4300, training accuracy 0.74
step 4300, val accuracy 0.775
step 4350, training accuracy 0.81
step 4350, val accuracy 0.755
step 4400, training accuracy 0.74
step 4400, val accuracy 0.74
step 4450, training accuracy 0.775
step 4450, val accuracy 0.77
step 4500, training accuracy 0.82
step 4500, val accuracy 0.78
step 4550, training accuracy 0.77
step 4550, val accuracy 0.835
step 4600, training accuracy 0.84
step 4600, val accuracy 0.74
step 4650, training accuracy 0.825
step 4650, val accuracy 0.75
step 4700, training accuracy 0.785
step 4700, val accuracy 0.77
step 4750, training accuracy 0.805
step 4750, val accuracy 0.775
step 4800, training accuracy 0.785
step 4800, val accuracy 0.775
step 4850, training accuracy 0.78
step 4850, val accuracy 0.73
step 4900, training accuracy 0.77
step 4900, val accuracy 0.76
step 4950, training accuracy 0.815
step 4950, val accuracy 0.79
step 5000, training accuracy 0.81
step 5000, val accuracy 0.78
step 5050, training accuracy 0.815
step 5050, val accuracy 0.795
step 5100, training accuracy 0.77
step 5100, val accuracy 0.755
step 5150, training accuracy 0.78
step 5150, val accuracy 0.805
step 5200, training accuracy 0.82
step 5200, val accuracy 0.815
step 5250, training accuracy 0.78
step 5250, val accuracy 0.76
step 5300, training accuracy 0.82
step 5300, val accuracy 0.79
step 5350, training accuracy 0.885
step 5350, val accuracy 0.82
step 5400, training accuracy 0.805
step 5400, val accuracy 0.78
step 5450, training accuracy 0.76
step 5450, val accuracy 0.755
step 5500, training accuracy 0.845
step 5500, val accuracy 0.795
step 5550, training accuracy 0.845
step 5550, val accuracy 0.82
step 5600, training accuracy 0.835
step 5600, val accuracy 0.78
step 5650, training accuracy 0.8
step 5650, val accuracy 0.76
step 5700, training accuracy 0.795
step 5700, val accuracy 0.82
step 5750, training accuracy 0.775
step 5750, val accuracy 0.805
step 5800, training accuracy 0.775
step 5800, val accuracy 0.805
step 5850, training accuracy 0.815
step 5850, val accuracy 0.785
step 5900, training accuracy 0.82
step 5900, val accuracy 0.78
step 5950, training accuracy 0.815
step 5950, val accuracy 0.785
step 6000, training accuracy 0.795
step 6000, val accuracy 0.74
step 6050, training accuracy 0.845
step 6050, val accuracy 0.78
step 6100, training accuracy 0.82
step 6100, val accuracy 0.785
step 6150, training accuracy 0.855
step 6150, val accuracy 0.815
step 6200, training accuracy 0.87
step 6200, val accuracy 0.79
step 6250, training accuracy 0.87
step 6250, val accuracy 0.78
step 6300, training accuracy 0.87
step 6300, val accuracy 0.825
step 6350, training accuracy 0.88
step 6350, val accuracy 0.8
step 6400, training accuracy 0.81
step 6400, val accuracy 0.835
step 6450, training accuracy 0.82
step 6450, val accuracy 0.81
step 6500, training accuracy 0.835
step 6500, val accuracy 0.825
step 6550, training accuracy 0.855
step 6550, val accuracy 0.77
step 6600, training accuracy 0.87
step 6600, val accuracy 0.79
step 6650, training accuracy 0.82
step 6650, val accuracy 0.825
step 6700, training accuracy 0.88
step 6700, val accuracy 0.735
step 6750, training accuracy 0.885
step 6750, val accuracy 0.775
step 6800, training accuracy 0.885
step 6800, val accuracy 0.8
step 6850, training accuracy 0.865
step 6850, val accuracy 0.81
step 6900, training accuracy 0.85
step 6900, val accuracy 0.81
step 6950, training accuracy 0.83
step 6950, val accuracy 0.795
step 7000, training accuracy 0.88
step 7000, val accuracy 0.785
step 7050, training accuracy 0.84
step 7050, val accuracy 0.76
step 7100, training accuracy 0.845
step 7100, val accuracy 0.75
step 7150, training accuracy 0.855
step 7150, val accuracy 0.765
step 7200, training accuracy 0.865
step 7200, val accuracy 0.815
step 7250, training accuracy 0.855
step 7250, val accuracy 0.8
step 7300, training accuracy 0.855
step 7300, val accuracy 0.8
step 7350, training accuracy 0.865
step 7350, val accuracy 0.785
step 7400, training accuracy 0.855
step 7400, val accuracy 0.82
step 7450, training accuracy 0.875
step 7450, val accuracy 0.78
step 7500, training accuracy 0.875
step 7500, val accuracy 0.77
step 7550, training accuracy 0.855
step 7550, val accuracy 0.805
step 7600, training accuracy 0.865
step 7600, val accuracy 0.83
step 7650, training accuracy 0.905
step 7650, val accuracy 0.815
step 7700, training accuracy 0.875
step 7700, val accuracy 0.83
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step 44200, training accuracy 0.985
step 44200, val accuracy 0.835
step 44250, training accuracy 0.995
step 44250, val accuracy 0.835
step 44300, training accuracy 0.995
step 44300, val accuracy 0.86
step 44350, training accuracy 0.995
step 44350, val accuracy 0.835
step 44400, training accuracy 1
step 44400, val accuracy 0.835
step 44450, training accuracy 1
step 44450, val accuracy 0.82
step 44500, training accuracy 1
step 44500, val accuracy 0.81
step 44550, training accuracy 0.995
step 44550, val accuracy 0.795
step 44600, training accuracy 0.99
step 44600, val accuracy 0.81
step 44650, training accuracy 1
step 44650, val accuracy 0.81
step 44700, training accuracy 1
step 44700, val accuracy 0.785
step 44750, training accuracy 0.995
step 44750, val accuracy 0.835
step 44800, training accuracy 1
step 44800, val accuracy 0.835
step 44850, training accuracy 0.995
step 44850, val accuracy 0.84
step 44900, training accuracy 0.995
step 44900, val accuracy 0.865
step 44950, training accuracy 0.99
step 44950, val accuracy 0.85
step 45000, training accuracy 0.995
step 45000, val accuracy 0.805
step 45050, training accuracy 0.995
step 45050, val accuracy 0.795
step 45100, training accuracy 0.995
step 45100, val accuracy 0.85
step 45150, training accuracy 0.995
step 45150, val accuracy 0.81
step 45200, training accuracy 0.985
step 45200, val accuracy 0.815
step 45250, training accuracy 0.995
step 45250, val accuracy 0.845
step 45300, training accuracy 0.985
step 45300, val accuracy 0.835
step 45350, training accuracy 1
step 45350, val accuracy 0.87
step 45400, training accuracy 0.99
step 45400, val accuracy 0.77
step 45450, training accuracy 0.98
step 45450, val accuracy 0.84
step 45500, training accuracy 0.995
step 45500, val accuracy 0.865
step 45550, training accuracy 1
step 45550, val accuracy 0.8
step 45600, training accuracy 0.985
step 45600, val accuracy 0.84
step 45650, training accuracy 1
step 45650, val accuracy 0.835
step 45700, training accuracy 0.995
step 45700, val accuracy 0.82
step 45750, training accuracy 1
step 45750, val accuracy 0.805
step 45800, training accuracy 0.99
step 45800, val accuracy 0.845
step 45850, training accuracy 0.99
step 45850, val accuracy 0.81
step 45900, training accuracy 0.99
step 45900, val accuracy 0.86
step 45950, training accuracy 1
step 45950, val accuracy 0.825
step 46000, training accuracy 0.99
step 46000, val accuracy 0.835
step 46050, training accuracy 0.975
step 46050, val accuracy 0.85
step 46100, training accuracy 1
step 46100, val accuracy 0.86
step 46150, training accuracy 0.995
step 46150, val accuracy 0.825
step 46200, training accuracy 1
step 46200, val accuracy 0.87
step 46250, training accuracy 1
step 46250, val accuracy 0.84
step 46300, training accuracy 0.99
step 46300, val accuracy 0.85
step 46350, training accuracy 1
step 46350, val accuracy 0.85
step 46400, training accuracy 0.99
step 46400, val accuracy 0.87
step 46450, training accuracy 0.99
step 46450, val accuracy 0.795
step 46500, training accuracy 1
step 46500, val accuracy 0.805
step 46550, training accuracy 0.995
step 46550, val accuracy 0.83
step 46600, training accuracy 0.99
step 46600, val accuracy 0.85
step 46650, training accuracy 0.99
step 46650, val accuracy 0.855
step 46700, training accuracy 0.995
step 46700, val accuracy 0.845
step 46750, training accuracy 0.985
step 46750, val accuracy 0.86
step 46800, training accuracy 0.975
step 46800, val accuracy 0.82
step 46850, training accuracy 0.985
step 46850, val accuracy 0.865
step 46900, training accuracy 0.995
step 46900, val accuracy 0.795
step 46950, training accuracy 0.995
step 46950, val accuracy 0.865
step 47000, training accuracy 1
step 47000, val accuracy 0.845
step 47050, training accuracy 0.995
step 47050, val accuracy 0.83
step 47100, training accuracy 0.995
step 47100, val accuracy 0.8
step 47150, training accuracy 1
step 47150, val accuracy 0.825
step 47200, training accuracy 0.985
step 47200, val accuracy 0.82
step 47250, training accuracy 0.99
step 47250, val accuracy 0.845
step 47300, training accuracy 1
step 47300, val accuracy 0.81
step 47350, training accuracy 0.99
step 47350, val accuracy 0.845
step 47400, training accuracy 0.995
step 47400, val accuracy 0.84
step 47450, training accuracy 0.99
step 47450, val accuracy 0.895
step 47500, training accuracy 0.99
step 47500, val accuracy 0.79
step 47550, training accuracy 1
step 47550, val accuracy 0.8
step 47600, training accuracy 1
step 47600, val accuracy 0.8
step 47650, training accuracy 0.99
step 47650, val accuracy 0.82
step 47700, training accuracy 0.995
step 47700, val accuracy 0.82
step 47750, training accuracy 0.995
step 47750, val accuracy 0.845
step 47800, training accuracy 0.965
step 47800, val accuracy 0.815
step 47850, training accuracy 1
step 47850, val accuracy 0.835
step 47900, training accuracy 0.995
step 47900, val accuracy 0.83
step 47950, training accuracy 0.985
step 47950, val accuracy 0.835
step 48000, training accuracy 0.99
step 48000, val accuracy 0.75
step 48050, training accuracy 0.995
step 48050, val accuracy 0.845
step 48100, training accuracy 0.995
step 48100, val accuracy 0.81
step 48150, training accuracy 0.99
step 48150, val accuracy 0.845
step 48200, training accuracy 0.995
step 48200, val accuracy 0.84
step 48250, training accuracy 0.99
step 48250, val accuracy 0.83
step 48300, training accuracy 0.995
step 48300, val accuracy 0.785
step 48350, training accuracy 0.995
step 48350, val accuracy 0.805
step 48400, training accuracy 0.975
step 48400, val accuracy 0.84
step 48450, training accuracy 0.99
step 48450, val accuracy 0.84
step 48500, training accuracy 1
step 48500, val accuracy 0.81
step 48550, training accuracy 0.995
step 48550, val accuracy 0.825
step 48600, training accuracy 1
step 48600, val accuracy 0.835
step 48650, training accuracy 0.975
step 48650, val accuracy 0.85
step 48700, training accuracy 1
step 48700, val accuracy 0.835
step 48750, training accuracy 0.995
step 48750, val accuracy 0.825
step 48800, training accuracy 0.99
step 48800, val accuracy 0.82
step 48850, training accuracy 0.99
step 48850, val accuracy 0.85
step 48900, training accuracy 0.99
step 48900, val accuracy 0.805
step 48950, training accuracy 0.97
step 48950, val accuracy 0.875
step 49000, training accuracy 0.985
step 49000, val accuracy 0.8
step 49050, training accuracy 0.995
step 49050, val accuracy 0.81
step 49100, training accuracy 0.99
step 49100, val accuracy 0.805
step 49150, training accuracy 0.995
step 49150, val accuracy 0.83
step 49200, training accuracy 0.985
step 49200, val accuracy 0.845
step 49250, training accuracy 0.99
step 49250, val accuracy 0.84
step 49300, training accuracy 0.985
step 49300, val accuracy 0.79
step 49350, training accuracy 0.995
step 49350, val accuracy 0.765
step 49400, training accuracy 1
step 49400, val accuracy 0.835
step 49450, training accuracy 0.995
step 49450, val accuracy 0.81
step 49500, training accuracy 1
step 49500, val accuracy 0.845
step 49550, training accuracy 1
step 49550, val accuracy 0.845
step 49600, training accuracy 1
step 49600, val accuracy 0.835
step 49650, training accuracy 1
step 49650, val accuracy 0.795
step 49700, training accuracy 0.99
step 49700, val accuracy 0.845
step 49750, training accuracy 0.99
step 49750, val accuracy 0.805
step 49800, training accuracy 0.995
step 49800, val accuracy 0.785
step 49850, training accuracy 0.99
step 49850, val accuracy 0.85
step 49900, training accuracy 0.975
step 49900, val accuracy 0.825
step 49950, training accuracy 0.995
step 49950, val accuracy 0.81

In [22]:

    

fig, ax = plt.subplots()

ax.plot(range(0,len(tr_acc)*summary_frequency,summary_frequency),tr_acc, label='Training')
ax.plot(range(0,len(ts_acc)*summary_frequency,summary_frequency),ts_acc, label='Validation')
ax.set_xlabel('Training steps')
ax.set_ylabel('Accuracy')
ax.set_ylim([0,1])
ax.set_title('Batch Normalization Accuracy')
ax.legend(loc=4)
plt.show()


    

In [ ]: