In [1]:

    

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets(".", one_hot=True, reshape=False)


    

    




Extracting ./train-images-idx3-ubyte.gz
Extracting ./train-labels-idx1-ubyte.gz
Extracting ./t10k-images-idx3-ubyte.gz
Extracting ./t10k-labels-idx1-ubyte.gz

In [2]:

    

import tensorflow as tf


    

In [3]:

    

# Parameters
learning_rate = 0.00001
epochs = 10
batch_size = 128


    

In [4]:

    

# Number of samples to calculate validation and accuracy
# Decrease this if you're running out of memory to calculate accuracy
test_valid_size = 256


    

In [5]:

    

# Network Parameters
n_classes = 10  # MNIST total classes (0-9 digits)
dropout = 0.75  # Dropout, probability to keep units


    

In [6]:

    

# Store layers weight & bias
weights = {
    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
    'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
    'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),
    'out': tf.Variable(tf.random_normal([1024, n_classes]))}

biases = {
    'bc1': tf.Variable(tf.random_normal([32])),
    'bc2': tf.Variable(tf.random_normal([64])),
    'bd1': tf.Variable(tf.random_normal([1024])),
    'out': tf.Variable(tf.random_normal([n_classes]))}


    

In [7]:

    

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


    

In [8]:

    

def maxpool2d(x, k=2):
    return tf.nn.max_pool(
        x,
        ksize=[1, k, k, 1],
        strides=[1, k, k, 1],
        padding='SAME')


    

In [9]:

    

def conv_net(x, weights, biases, dropout):
    # Layer 1 - 28*28*1 to 14*14*32
    conv1 = conv2d(x, weights['wc1'], biases['bc1'])
    conv1 = maxpool2d(conv1, k=2)

    # Layer 2 - 14*14*32 to 7*7*64
    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
    conv2 = maxpool2d(conv2, k=2)

    # Fully connected layer - 7*7*64 to 1024
    fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
    fc1 = tf.nn.relu(fc1)
    fc1 = tf.nn.dropout(fc1, dropout)

    # Output Layer - class prediction - 1024 to 10
    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
    return out


    

In [10]:

    

# tf Graph input
x = tf.placeholder(tf.float32, [None, 28, 28, 1])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32)


    

In [11]:

    

# Model
logits = conv_net(x, weights, biases, keep_prob)


    

In [12]:

    

# Define loss and optimizer
cost = tf.reduce_mean(\
    tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\
    .minimize(cost)


    

In [13]:

    

# Accuracy
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))


    

In [14]:

    

# Initializing the variables
init = tf. global_variables_initializer()


    

In [15]:

    

# Launch the graph
with tf.Session() as sess:
    sess.run(init)

    for epoch in range(epochs):
        for batch in range(mnist.train.num_examples//batch_size):
            batch_x, batch_y = mnist.train.next_batch(batch_size)
            sess.run(optimizer, feed_dict={
                x: batch_x,
                y: batch_y,
                keep_prob: dropout})

            # Calculate batch loss and accuracy
            loss = sess.run(cost, feed_dict={
                x: batch_x,
                y: batch_y,
                keep_prob: 1.})
            valid_acc = sess.run(accuracy, feed_dict={
                x: mnist.validation.images[:test_valid_size],
                y: mnist.validation.labels[:test_valid_size],
                keep_prob: 1.})

            print('Epoch {:>2}, Batch {:>3} -'
                  'Loss: {:>10.4f} Validation Accuracy: {:.6f}'.format(
                epoch + 1,
                batch + 1,
                loss,
                valid_acc))

    # Calculate Test Accuracy
    test_acc = sess.run(accuracy, feed_dict={
        x: mnist.test.images[:test_valid_size],
        y: mnist.test.labels[:test_valid_size],
        keep_prob: 1.})
    print('Testing Accuracy: {}'.format(test_acc))


    

    




Epoch  1, Batch   1 -Loss: 82219.9375 Validation Accuracy: 0.062500
Epoch  1, Batch   2 -Loss: 62935.6992 Validation Accuracy: 0.070312
Epoch  1, Batch   3 -Loss: 59076.2812 Validation Accuracy: 0.082031
Epoch  1, Batch   4 -Loss: 52329.8672 Validation Accuracy: 0.082031
Epoch  1, Batch   5 -Loss: 44530.6406 Validation Accuracy: 0.097656
Epoch  1, Batch   6 -Loss: 44715.8789 Validation Accuracy: 0.093750
Epoch  1, Batch   7 -Loss: 40386.7734 Validation Accuracy: 0.097656
Epoch  1, Batch   8 -Loss: 40158.2656 Validation Accuracy: 0.109375
Epoch  1, Batch   9 -Loss: 36460.1328 Validation Accuracy: 0.117188
Epoch  1, Batch  10 -Loss: 34233.4375 Validation Accuracy: 0.117188
Epoch  1, Batch  11 -Loss: 32288.9785 Validation Accuracy: 0.121094
Epoch  1, Batch  12 -Loss: 28586.3086 Validation Accuracy: 0.105469
Epoch  1, Batch  13 -Loss: 26655.2578 Validation Accuracy: 0.109375
Epoch  1, Batch  14 -Loss: 24604.9766 Validation Accuracy: 0.109375
Epoch  1, Batch  15 -Loss: 22866.4727 Validation Accuracy: 0.121094
Epoch  1, Batch  16 -Loss: 22906.3711 Validation Accuracy: 0.128906
Epoch  1, Batch  17 -Loss: 21518.4473 Validation Accuracy: 0.128906
Epoch  1, Batch  18 -Loss: 21576.7520 Validation Accuracy: 0.125000
Epoch  1, Batch  19 -Loss: 20560.2812 Validation Accuracy: 0.125000
Epoch  1, Batch  20 -Loss: 21581.8262 Validation Accuracy: 0.128906
Epoch  1, Batch  21 -Loss: 17833.9844 Validation Accuracy: 0.128906
Epoch  1, Batch  22 -Loss: 20430.1367 Validation Accuracy: 0.144531
Epoch  1, Batch  23 -Loss: 20553.9688 Validation Accuracy: 0.140625
Epoch  1, Batch  24 -Loss: 17005.7773 Validation Accuracy: 0.152344
Epoch  1, Batch  25 -Loss: 16892.2227 Validation Accuracy: 0.156250
Epoch  1, Batch  26 -Loss: 15476.4473 Validation Accuracy: 0.156250
Epoch  1, Batch  27 -Loss: 15312.1299 Validation Accuracy: 0.183594
Epoch  1, Batch  28 -Loss: 17757.7891 Validation Accuracy: 0.187500
Epoch  1, Batch  29 -Loss: 17599.8945 Validation Accuracy: 0.195312
Epoch  1, Batch  30 -Loss: 15400.1562 Validation Accuracy: 0.191406
Epoch  1, Batch  31 -Loss: 14562.4150 Validation Accuracy: 0.187500
Epoch  1, Batch  32 -Loss: 15826.8438 Validation Accuracy: 0.191406
Epoch  1, Batch  33 -Loss: 15426.9492 Validation Accuracy: 0.214844
Epoch  1, Batch  34 -Loss: 13363.4404 Validation Accuracy: 0.218750
Epoch  1, Batch  35 -Loss: 14588.8789 Validation Accuracy: 0.203125
Epoch  1, Batch  36 -Loss: 11028.2324 Validation Accuracy: 0.226562
Epoch  1, Batch  37 -Loss: 11953.8096 Validation Accuracy: 0.234375
Epoch  1, Batch  38 -Loss: 12257.5527 Validation Accuracy: 0.246094
Epoch  1, Batch  39 -Loss: 12678.4492 Validation Accuracy: 0.253906
Epoch  1, Batch  40 -Loss: 12909.0977 Validation Accuracy: 0.246094
Epoch  1, Batch  41 -Loss: 13445.9805 Validation Accuracy: 0.277344
Epoch  1, Batch  42 -Loss: 11596.3896 Validation Accuracy: 0.285156
Epoch  1, Batch  43 -Loss: 10910.3711 Validation Accuracy: 0.292969
Epoch  1, Batch  44 -Loss: 10617.6768 Validation Accuracy: 0.292969
Epoch  1, Batch  45 -Loss: 10137.0684 Validation Accuracy: 0.308594
Epoch  1, Batch  46 -Loss:  9454.8936 Validation Accuracy: 0.300781
Epoch  1, Batch  47 -Loss: 11286.6445 Validation Accuracy: 0.296875
Epoch  1, Batch  48 -Loss: 10298.9531 Validation Accuracy: 0.316406
Epoch  1, Batch  49 -Loss:  9542.9980 Validation Accuracy: 0.328125
Epoch  1, Batch  50 -Loss: 10593.9541 Validation Accuracy: 0.339844
Epoch  1, Batch  51 -Loss:  9651.0566 Validation Accuracy: 0.347656
Epoch  1, Batch  52 -Loss: 10540.5244 Validation Accuracy: 0.363281
Epoch  1, Batch  53 -Loss: 10030.4600 Validation Accuracy: 0.351562
Epoch  1, Batch  54 -Loss: 10472.6123 Validation Accuracy: 0.359375
Epoch  1, Batch  55 -Loss:  8346.4062 Validation Accuracy: 0.363281
Epoch  1, Batch  56 -Loss:  8663.6504 Validation Accuracy: 0.355469
Epoch  1, Batch  57 -Loss:  9221.3906 Validation Accuracy: 0.363281
Epoch  1, Batch  58 -Loss:  7381.2603 Validation Accuracy: 0.359375
Epoch  1, Batch  59 -Loss:  7715.8057 Validation Accuracy: 0.359375
Epoch  1, Batch  60 -Loss:  9042.3311 Validation Accuracy: 0.367188
Epoch  1, Batch  61 -Loss:  7794.9653 Validation Accuracy: 0.367188
Epoch  1, Batch  62 -Loss:  7819.8467 Validation Accuracy: 0.367188
Epoch  1, Batch  63 -Loss:  8345.5283 Validation Accuracy: 0.378906
Epoch  1, Batch  64 -Loss:  9415.1914 Validation Accuracy: 0.375000
Epoch  1, Batch  65 -Loss:  5837.4995 Validation Accuracy: 0.394531
Epoch  1, Batch  66 -Loss:  7692.4316 Validation Accuracy: 0.394531
Epoch  1, Batch  67 -Loss:  7262.2188 Validation Accuracy: 0.402344
Epoch  1, Batch  68 -Loss:  6630.8232 Validation Accuracy: 0.414062
Epoch  1, Batch  69 -Loss:  7263.3486 Validation Accuracy: 0.414062
Epoch  1, Batch  70 -Loss:  6830.0415 Validation Accuracy: 0.394531
Epoch  1, Batch  71 -Loss:  7783.5879 Validation Accuracy: 0.417969
Epoch  1, Batch  72 -Loss:  7061.8301 Validation Accuracy: 0.414062
Epoch  1, Batch  73 -Loss:  7030.1069 Validation Accuracy: 0.414062
Epoch  1, Batch  74 -Loss:  6689.5986 Validation Accuracy: 0.414062
Epoch  1, Batch  75 -Loss:  6251.2539 Validation Accuracy: 0.410156
Epoch  1, Batch  76 -Loss:  6638.7070 Validation Accuracy: 0.421875
Epoch  1, Batch  77 -Loss:  8022.4023 Validation Accuracy: 0.425781
Epoch  1, Batch  78 -Loss:  7056.3164 Validation Accuracy: 0.433594
Epoch  1, Batch  79 -Loss:  6714.6104 Validation Accuracy: 0.425781
Epoch  1, Batch  80 -Loss:  6506.1553 Validation Accuracy: 0.453125
Epoch  1, Batch  81 -Loss:  6725.3564 Validation Accuracy: 0.445312
Epoch  1, Batch  82 -Loss:  6805.8423 Validation Accuracy: 0.445312
Epoch  1, Batch  83 -Loss:  5602.2031 Validation Accuracy: 0.453125
Epoch  1, Batch  84 -Loss:  5703.7812 Validation Accuracy: 0.468750
Epoch  1, Batch  85 -Loss:  7431.2041 Validation Accuracy: 0.476562
Epoch  1, Batch  86 -Loss:  5955.2246 Validation Accuracy: 0.449219
Epoch  1, Batch  87 -Loss:  5733.8438 Validation Accuracy: 0.468750
Epoch  1, Batch  88 -Loss:  6548.2114 Validation Accuracy: 0.472656
Epoch  1, Batch  89 -Loss:  7213.0913 Validation Accuracy: 0.488281
Epoch  1, Batch  90 -Loss:  6263.6255 Validation Accuracy: 0.476562
Epoch  1, Batch  91 -Loss:  5844.9736 Validation Accuracy: 0.488281
Epoch  1, Batch  92 -Loss:  5696.8071 Validation Accuracy: 0.488281
Epoch  1, Batch  93 -Loss:  5679.1494 Validation Accuracy: 0.476562
Epoch  1, Batch  94 -Loss:  5858.5400 Validation Accuracy: 0.480469
Epoch  1, Batch  95 -Loss:  5466.0845 Validation Accuracy: 0.472656
Epoch  1, Batch  96 -Loss:  5885.4766 Validation Accuracy: 0.507812
Epoch  1, Batch  97 -Loss:  4637.8477 Validation Accuracy: 0.515625
Epoch  1, Batch  98 -Loss:  5609.9346 Validation Accuracy: 0.523438
Epoch  1, Batch  99 -Loss:  5340.8604 Validation Accuracy: 0.523438
Epoch  1, Batch 100 -Loss:  5019.0562 Validation Accuracy: 0.531250
Epoch  1, Batch 101 -Loss:  4949.5146 Validation Accuracy: 0.519531
Epoch  1, Batch 102 -Loss:  4272.7695 Validation Accuracy: 0.542969
Epoch  1, Batch 103 -Loss:  4212.0703 Validation Accuracy: 0.542969
Epoch  1, Batch 104 -Loss:  3894.6680 Validation Accuracy: 0.523438
Epoch  1, Batch 105 -Loss:  4105.9141 Validation Accuracy: 0.515625
Epoch  1, Batch 106 -Loss:  5151.4336 Validation Accuracy: 0.519531
Epoch  1, Batch 107 -Loss:  5454.4863 Validation Accuracy: 0.515625
Epoch  1, Batch 108 -Loss:  4641.1943 Validation Accuracy: 0.519531
Epoch  1, Batch 109 -Loss:  4357.9092 Validation Accuracy: 0.500000
Epoch  1, Batch 110 -Loss:  4981.3516 Validation Accuracy: 0.519531
Epoch  1, Batch 111 -Loss:  5003.0537 Validation Accuracy: 0.535156
Epoch  1, Batch 112 -Loss:  4533.2188 Validation Accuracy: 0.546875
Epoch  1, Batch 113 -Loss:  5082.2847 Validation Accuracy: 0.539062
Epoch  1, Batch 114 -Loss:  4677.6660 Validation Accuracy: 0.531250
Epoch  1, Batch 115 -Loss:  4475.2339 Validation Accuracy: 0.539062
Epoch  1, Batch 116 -Loss:  5334.7373 Validation Accuracy: 0.535156
Epoch  1, Batch 117 -Loss:  4241.7695 Validation Accuracy: 0.566406
Epoch  1, Batch 118 -Loss:  5478.2285 Validation Accuracy: 0.554688
Epoch  1, Batch 119 -Loss:  5059.0068 Validation Accuracy: 0.546875
Epoch  1, Batch 120 -Loss:  4293.8066 Validation Accuracy: 0.546875
Epoch  1, Batch 121 -Loss:  3712.9585 Validation Accuracy: 0.566406
Epoch  1, Batch 122 -Loss:  3858.9297 Validation Accuracy: 0.566406
Epoch  1, Batch 123 -Loss:  4829.6475 Validation Accuracy: 0.570312
Epoch  1, Batch 124 -Loss:  4378.1450 Validation Accuracy: 0.578125
Epoch  1, Batch 125 -Loss:  4216.3730 Validation Accuracy: 0.574219
Epoch  1, Batch 126 -Loss:  3888.9497 Validation Accuracy: 0.582031
Epoch  1, Batch 127 -Loss:  4267.6074 Validation Accuracy: 0.574219
Epoch  1, Batch 128 -Loss:  3987.3833 Validation Accuracy: 0.589844
Epoch  1, Batch 129 -Loss:  4108.6875 Validation Accuracy: 0.578125
Epoch  1, Batch 130 -Loss:  4304.5840 Validation Accuracy: 0.582031
Epoch  1, Batch 131 -Loss:  4334.6074 Validation Accuracy: 0.582031
Epoch  1, Batch 132 -Loss:  3178.0552 Validation Accuracy: 0.585938
Epoch  1, Batch 133 -Loss:  3957.2566 Validation Accuracy: 0.585938
Epoch  1, Batch 134 -Loss:  3429.2800 Validation Accuracy: 0.593750
Epoch  1, Batch 135 -Loss:  3952.7764 Validation Accuracy: 0.597656
Epoch  1, Batch 136 -Loss:  4657.6445 Validation Accuracy: 0.593750
Epoch  1, Batch 137 -Loss:  2884.2446 Validation Accuracy: 0.601562
Epoch  1, Batch 138 -Loss:  4299.7964 Validation Accuracy: 0.597656
Epoch  1, Batch 139 -Loss:  4392.9380 Validation Accuracy: 0.605469
Epoch  1, Batch 140 -Loss:  2880.1089 Validation Accuracy: 0.601562
Epoch  1, Batch 141 -Loss:  3822.5757 Validation Accuracy: 0.601562
Epoch  1, Batch 142 -Loss:  4364.4321 Validation Accuracy: 0.613281
Epoch  1, Batch 143 -Loss:  3082.0203 Validation Accuracy: 0.617188
Epoch  1, Batch 144 -Loss:  4573.4771 Validation Accuracy: 0.613281
Epoch  1, Batch 145 -Loss:  3405.3655 Validation Accuracy: 0.617188
Epoch  1, Batch 146 -Loss:  3514.4829 Validation Accuracy: 0.613281
Epoch  1, Batch 147 -Loss:  2915.5596 Validation Accuracy: 0.609375
Epoch  1, Batch 148 -Loss:  3759.8826 Validation Accuracy: 0.609375
Epoch  1, Batch 149 -Loss:  3195.6626 Validation Accuracy: 0.617188
Epoch  1, Batch 150 -Loss:  3251.6680 Validation Accuracy: 0.609375
Epoch  1, Batch 151 -Loss:  3916.6760 Validation Accuracy: 0.621094
Epoch  1, Batch 152 -Loss:  3623.9932 Validation Accuracy: 0.625000
Epoch  1, Batch 153 -Loss:  2415.4163 Validation Accuracy: 0.617188
Epoch  1, Batch 154 -Loss:  3375.7568 Validation Accuracy: 0.625000
Epoch  1, Batch 155 -Loss:  2959.0408 Validation Accuracy: 0.617188
Epoch  1, Batch 156 -Loss:  3028.4111 Validation Accuracy: 0.625000
Epoch  1, Batch 157 -Loss:  3656.0334 Validation Accuracy: 0.625000
Epoch  1, Batch 158 -Loss:  3812.0505 Validation Accuracy: 0.625000
Epoch  1, Batch 159 -Loss:  3007.2351 Validation Accuracy: 0.625000
Epoch  1, Batch 160 -Loss:  3511.2263 Validation Accuracy: 0.617188
Epoch  1, Batch 161 -Loss:  3219.1064 Validation Accuracy: 0.613281
Epoch  1, Batch 162 -Loss:  3959.0115 Validation Accuracy: 0.621094
Epoch  1, Batch 163 -Loss:  3600.0298 Validation Accuracy: 0.621094
Epoch  1, Batch 164 -Loss:  3644.4512 Validation Accuracy: 0.621094
Epoch  1, Batch 165 -Loss:  2882.5752 Validation Accuracy: 0.625000
Epoch  1, Batch 166 -Loss:  2980.5830 Validation Accuracy: 0.617188
Epoch  1, Batch 167 -Loss:  2970.4556 Validation Accuracy: 0.621094
Epoch  1, Batch 168 -Loss:  3433.9937 Validation Accuracy: 0.621094
Epoch  1, Batch 169 -Loss:  2708.2939 Validation Accuracy: 0.625000
Epoch  1, Batch 170 -Loss:  3479.0276 Validation Accuracy: 0.628906
Epoch  1, Batch 171 -Loss:  2997.4319 Validation Accuracy: 0.628906
Epoch  1, Batch 172 -Loss:  3799.1692 Validation Accuracy: 0.636719
Epoch  1, Batch 173 -Loss:  2941.8406 Validation Accuracy: 0.628906
Epoch  1, Batch 174 -Loss:  3992.2761 Validation Accuracy: 0.632812
Epoch  1, Batch 175 -Loss:  2970.5532 Validation Accuracy: 0.636719
Epoch  1, Batch 176 -Loss:  3490.1702 Validation Accuracy: 0.636719
Epoch  1, Batch 177 -Loss:  3328.8501 Validation Accuracy: 0.640625
Epoch  1, Batch 178 -Loss:  3584.8486 Validation Accuracy: 0.644531
Epoch  1, Batch 179 -Loss:  2609.7222 Validation Accuracy: 0.636719
Epoch  1, Batch 180 -Loss:  3113.4373 Validation Accuracy: 0.636719
Epoch  1, Batch 181 -Loss:  3713.4080 Validation Accuracy: 0.644531
Epoch  1, Batch 182 -Loss:  3072.4326 Validation Accuracy: 0.644531
Epoch  1, Batch 183 -Loss:  2734.1235 Validation Accuracy: 0.648438
Epoch  1, Batch 184 -Loss:  2030.3574 Validation Accuracy: 0.644531
Epoch  1, Batch 185 -Loss:  2663.1350 Validation Accuracy: 0.644531
Epoch  1, Batch 186 -Loss:  3429.8018 Validation Accuracy: 0.656250
Epoch  1, Batch 187 -Loss:  2943.5359 Validation Accuracy: 0.652344
Epoch  1, Batch 188 -Loss:  2738.5730 Validation Accuracy: 0.652344
Epoch  1, Batch 189 -Loss:  2768.1897 Validation Accuracy: 0.652344
Epoch  1, Batch 190 -Loss:  2334.2346 Validation Accuracy: 0.648438
Epoch  1, Batch 191 -Loss:  2873.9038 Validation Accuracy: 0.656250
Epoch  1, Batch 192 -Loss:  2464.5923 Validation Accuracy: 0.656250
Epoch  1, Batch 193 -Loss:  2738.3101 Validation Accuracy: 0.652344
Epoch  1, Batch 194 -Loss:  3333.3132 Validation Accuracy: 0.648438
Epoch  1, Batch 195 -Loss:  3126.1704 Validation Accuracy: 0.656250
Epoch  1, Batch 196 -Loss:  2089.4434 Validation Accuracy: 0.648438
Epoch  1, Batch 197 -Loss:  3493.1790 Validation Accuracy: 0.648438
Epoch  1, Batch 198 -Loss:  3316.9229 Validation Accuracy: 0.652344
Epoch  1, Batch 199 -Loss:  2442.2583 Validation Accuracy: 0.652344
Epoch  1, Batch 200 -Loss:  2950.5820 Validation Accuracy: 0.664062
Epoch  1, Batch 201 -Loss:  3562.1611 Validation Accuracy: 0.656250
Epoch  1, Batch 202 -Loss:  2795.3730 Validation Accuracy: 0.660156
Epoch  1, Batch 203 -Loss:  3272.8042 Validation Accuracy: 0.660156
Epoch  1, Batch 204 -Loss:  2825.2078 Validation Accuracy: 0.660156
Epoch  1, Batch 205 -Loss:  3446.0146 Validation Accuracy: 0.656250
Epoch  1, Batch 206 -Loss:  3385.2834 Validation Accuracy: 0.656250
Epoch  1, Batch 207 -Loss:  3196.3843 Validation Accuracy: 0.652344
Epoch  1, Batch 208 -Loss:  3906.3750 Validation Accuracy: 0.656250
Epoch  1, Batch 209 -Loss:  2670.2515 Validation Accuracy: 0.660156
Epoch  1, Batch 210 -Loss:  3396.1904 Validation Accuracy: 0.660156
Epoch  1, Batch 211 -Loss:  3483.2859 Validation Accuracy: 0.656250
Epoch  1, Batch 212 -Loss:  3151.6077 Validation Accuracy: 0.664062
Epoch  1, Batch 213 -Loss:  2706.3896 Validation Accuracy: 0.660156
Epoch  1, Batch 214 -Loss:  2348.7993 Validation Accuracy: 0.660156
Epoch  1, Batch 215 -Loss:  3600.2749 Validation Accuracy: 0.664062
Epoch  1, Batch 216 -Loss:  2293.9888 Validation Accuracy: 0.667969
Epoch  1, Batch 217 -Loss:  2875.4368 Validation Accuracy: 0.664062
Epoch  1, Batch 218 -Loss:  2013.5355 Validation Accuracy: 0.671875
Epoch  1, Batch 219 -Loss:  3013.4807 Validation Accuracy: 0.667969
Epoch  1, Batch 220 -Loss:  3137.8889 Validation Accuracy: 0.660156
Epoch  1, Batch 221 -Loss:  2241.7302 Validation Accuracy: 0.667969
Epoch  1, Batch 222 -Loss:  1880.5161 Validation Accuracy: 0.660156
Epoch  1, Batch 223 -Loss:  3325.4009 Validation Accuracy: 0.652344
Epoch  1, Batch 224 -Loss:  3566.6885 Validation Accuracy: 0.656250
Epoch  1, Batch 225 -Loss:  2524.2725 Validation Accuracy: 0.664062
Epoch  1, Batch 226 -Loss:  2999.2959 Validation Accuracy: 0.660156
Epoch  1, Batch 227 -Loss:  2709.6919 Validation Accuracy: 0.667969
Epoch  1, Batch 228 -Loss:  2442.4578 Validation Accuracy: 0.660156
Epoch  1, Batch 229 -Loss:  3012.5859 Validation Accuracy: 0.667969
Epoch  1, Batch 230 -Loss:  2315.0586 Validation Accuracy: 0.671875
Epoch  1, Batch 231 -Loss:  3314.6970 Validation Accuracy: 0.664062
Epoch  1, Batch 232 -Loss:  2044.7413 Validation Accuracy: 0.656250
Epoch  1, Batch 233 -Loss:  2840.4097 Validation Accuracy: 0.660156
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Epoch 10, Batch 282 -Loss:   182.3717 Validation Accuracy: 0.800781
Epoch 10, Batch 283 -Loss:   197.9075 Validation Accuracy: 0.804688
Epoch 10, Batch 284 -Loss:   185.8369 Validation Accuracy: 0.800781
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Epoch 10, Batch 288 -Loss:   384.5043 Validation Accuracy: 0.808594
Epoch 10, Batch 289 -Loss:   261.8563 Validation Accuracy: 0.804688
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Epoch 10, Batch 297 -Loss:   284.0745 Validation Accuracy: 0.804688
Epoch 10, Batch 298 -Loss:   337.8279 Validation Accuracy: 0.800781
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Epoch 10, Batch 300 -Loss:   376.9219 Validation Accuracy: 0.808594
Epoch 10, Batch 301 -Loss:   264.0002 Validation Accuracy: 0.804688
Epoch 10, Batch 302 -Loss:   178.0444 Validation Accuracy: 0.808594
Epoch 10, Batch 303 -Loss:   168.7988 Validation Accuracy: 0.808594
Epoch 10, Batch 304 -Loss:   252.3356 Validation Accuracy: 0.812500
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Epoch 10, Batch 306 -Loss:   220.5645 Validation Accuracy: 0.804688
Epoch 10, Batch 307 -Loss:   317.3190 Validation Accuracy: 0.808594
Epoch 10, Batch 308 -Loss:   308.2164 Validation Accuracy: 0.804688
Epoch 10, Batch 309 -Loss:   288.4954 Validation Accuracy: 0.804688
Epoch 10, Batch 310 -Loss:   231.4652 Validation Accuracy: 0.796875
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Epoch 10, Batch 312 -Loss:   392.2288 Validation Accuracy: 0.804688
Epoch 10, Batch 313 -Loss:   267.9290 Validation Accuracy: 0.804688
Epoch 10, Batch 314 -Loss:   233.3397 Validation Accuracy: 0.800781
Epoch 10, Batch 315 -Loss:   255.3793 Validation Accuracy: 0.808594
Epoch 10, Batch 316 -Loss:   263.1919 Validation Accuracy: 0.804688
Epoch 10, Batch 317 -Loss:   144.8387 Validation Accuracy: 0.808594
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Epoch 10, Batch 319 -Loss:   267.8472 Validation Accuracy: 0.812500
Epoch 10, Batch 320 -Loss:   208.5347 Validation Accuracy: 0.816406
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Epoch 10, Batch 324 -Loss:   260.1551 Validation Accuracy: 0.816406
Epoch 10, Batch 325 -Loss:   274.7183 Validation Accuracy: 0.816406
Epoch 10, Batch 326 -Loss:   185.1582 Validation Accuracy: 0.820312
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Epoch 10, Batch 328 -Loss:   373.2432 Validation Accuracy: 0.816406
Epoch 10, Batch 329 -Loss:   155.7943 Validation Accuracy: 0.820312
Epoch 10, Batch 330 -Loss:   276.9376 Validation Accuracy: 0.820312
Epoch 10, Batch 331 -Loss:   284.1817 Validation Accuracy: 0.816406
Epoch 10, Batch 332 -Loss:   304.6053 Validation Accuracy: 0.816406
Epoch 10, Batch 333 -Loss:   285.0582 Validation Accuracy: 0.816406
Epoch 10, Batch 334 -Loss:   209.6783 Validation Accuracy: 0.812500
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Epoch 10, Batch 338 -Loss:   407.6760 Validation Accuracy: 0.812500
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Epoch 10, Batch 343 -Loss:   228.4102 Validation Accuracy: 0.812500
Epoch 10, Batch 344 -Loss:   240.2368 Validation Accuracy: 0.816406
Epoch 10, Batch 345 -Loss:   269.4096 Validation Accuracy: 0.808594
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Epoch 10, Batch 347 -Loss:   249.5504 Validation Accuracy: 0.816406
Epoch 10, Batch 348 -Loss:   297.7899 Validation Accuracy: 0.816406
Epoch 10, Batch 349 -Loss:   200.7790 Validation Accuracy: 0.816406
Epoch 10, Batch 350 -Loss:   237.7918 Validation Accuracy: 0.816406
Epoch 10, Batch 351 -Loss:   245.7619 Validation Accuracy: 0.812500
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Epoch 10, Batch 353 -Loss:   313.4632 Validation Accuracy: 0.812500
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Epoch 10, Batch 356 -Loss:   280.0686 Validation Accuracy: 0.808594
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Epoch 10, Batch 358 -Loss:   286.1483 Validation Accuracy: 0.816406
Epoch 10, Batch 359 -Loss:   348.9514 Validation Accuracy: 0.812500
Epoch 10, Batch 360 -Loss:   106.2976 Validation Accuracy: 0.812500
Epoch 10, Batch 361 -Loss:   174.9075 Validation Accuracy: 0.820312
Epoch 10, Batch 362 -Loss:   294.5188 Validation Accuracy: 0.812500
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Epoch 10, Batch 368 -Loss:   193.9459 Validation Accuracy: 0.816406
Epoch 10, Batch 369 -Loss:   211.8296 Validation Accuracy: 0.820312
Epoch 10, Batch 370 -Loss:   272.3282 Validation Accuracy: 0.820312
Epoch 10, Batch 371 -Loss:   302.4833 Validation Accuracy: 0.816406
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Epoch 10, Batch 374 -Loss:   329.8824 Validation Accuracy: 0.816406
Epoch 10, Batch 375 -Loss:   281.1390 Validation Accuracy: 0.816406
Epoch 10, Batch 376 -Loss:   207.4584 Validation Accuracy: 0.816406
Epoch 10, Batch 377 -Loss:   224.7826 Validation Accuracy: 0.816406
Epoch 10, Batch 378 -Loss:   313.8479 Validation Accuracy: 0.816406
Epoch 10, Batch 379 -Loss:   235.3392 Validation Accuracy: 0.820312
Epoch 10, Batch 380 -Loss:   296.0457 Validation Accuracy: 0.816406
Epoch 10, Batch 381 -Loss:   240.8499 Validation Accuracy: 0.812500
Epoch 10, Batch 382 -Loss:   215.8387 Validation Accuracy: 0.812500
Epoch 10, Batch 383 -Loss:   332.1722 Validation Accuracy: 0.812500
Epoch 10, Batch 384 -Loss:   182.7698 Validation Accuracy: 0.812500
Epoch 10, Batch 385 -Loss:   289.2596 Validation Accuracy: 0.812500
Epoch 10, Batch 386 -Loss:   315.3920 Validation Accuracy: 0.816406
Epoch 10, Batch 387 -Loss:   284.9305 Validation Accuracy: 0.816406
Epoch 10, Batch 388 -Loss:   197.8183 Validation Accuracy: 0.816406
Epoch 10, Batch 389 -Loss:   192.5607 Validation Accuracy: 0.816406
Epoch 10, Batch 390 -Loss:   212.1469 Validation Accuracy: 0.820312
Epoch 10, Batch 391 -Loss:   229.2794 Validation Accuracy: 0.820312
Epoch 10, Batch 392 -Loss:   188.8823 Validation Accuracy: 0.812500
Epoch 10, Batch 393 -Loss:   325.7650 Validation Accuracy: 0.812500
Epoch 10, Batch 394 -Loss:   277.6490 Validation Accuracy: 0.812500
Epoch 10, Batch 395 -Loss:   164.1648 Validation Accuracy: 0.808594
Epoch 10, Batch 396 -Loss:   222.8815 Validation Accuracy: 0.816406
Epoch 10, Batch 397 -Loss:   276.6872 Validation Accuracy: 0.812500
Epoch 10, Batch 398 -Loss:   239.6993 Validation Accuracy: 0.812500
Epoch 10, Batch 399 -Loss:   352.8018 Validation Accuracy: 0.812500
Epoch 10, Batch 400 -Loss:   167.0705 Validation Accuracy: 0.812500
Epoch 10, Batch 401 -Loss:   284.2626 Validation Accuracy: 0.812500
Epoch 10, Batch 402 -Loss:   200.1229 Validation Accuracy: 0.812500
Epoch 10, Batch 403 -Loss:   291.7751 Validation Accuracy: 0.812500
Epoch 10, Batch 404 -Loss:   305.8893 Validation Accuracy: 0.808594
Epoch 10, Batch 405 -Loss:   186.1707 Validation Accuracy: 0.812500
Epoch 10, Batch 406 -Loss:   224.7188 Validation Accuracy: 0.816406
Epoch 10, Batch 407 -Loss:   195.0201 Validation Accuracy: 0.812500
Epoch 10, Batch 408 -Loss:   366.4603 Validation Accuracy: 0.808594
Epoch 10, Batch 409 -Loss:   270.4200 Validation Accuracy: 0.812500
Epoch 10, Batch 410 -Loss:   207.2350 Validation Accuracy: 0.804688
Epoch 10, Batch 411 -Loss:   237.3178 Validation Accuracy: 0.800781
Epoch 10, Batch 412 -Loss:   339.5620 Validation Accuracy: 0.800781
Epoch 10, Batch 413 -Loss:   430.1067 Validation Accuracy: 0.800781
Epoch 10, Batch 414 -Loss:   313.0763 Validation Accuracy: 0.796875
Epoch 10, Batch 415 -Loss:   203.1254 Validation Accuracy: 0.796875
Epoch 10, Batch 416 -Loss:   341.1235 Validation Accuracy: 0.800781
Epoch 10, Batch 417 -Loss:   303.1134 Validation Accuracy: 0.796875
Epoch 10, Batch 418 -Loss:   429.1742 Validation Accuracy: 0.800781
Epoch 10, Batch 419 -Loss:   153.9593 Validation Accuracy: 0.800781
Epoch 10, Batch 420 -Loss:   270.1494 Validation Accuracy: 0.796875
Epoch 10, Batch 421 -Loss:   343.4836 Validation Accuracy: 0.796875
Epoch 10, Batch 422 -Loss:   282.6892 Validation Accuracy: 0.796875
Epoch 10, Batch 423 -Loss:   357.2126 Validation Accuracy: 0.796875
Epoch 10, Batch 424 -Loss:   253.1052 Validation Accuracy: 0.796875
Epoch 10, Batch 425 -Loss:   268.7802 Validation Accuracy: 0.796875
Epoch 10, Batch 426 -Loss:   429.7561 Validation Accuracy: 0.792969
Epoch 10, Batch 427 -Loss:   289.0232 Validation Accuracy: 0.796875
Epoch 10, Batch 428 -Loss:   358.8989 Validation Accuracy: 0.789062
Epoch 10, Batch 429 -Loss:   164.5413 Validation Accuracy: 0.792969
Testing Accuracy: 0.80078125

In [ ]: