In [1]:

    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile

cifar10_dataset_folder_path = 'cifar-10-batches-py'

# Use Floyd's cifar-10 dataset if present
floyd_cifar10_location = '/input/cifar-10/python.tar.gz'
if isfile(floyd_cifar10_location):
    tar_gz_path = floyd_cifar10_location
else:
    tar_gz_path = 'cifar-10-python.tar.gz'

class DLProgress(tqdm):
    last_block = 0

    def hook(self, block_num=1, block_size=1, total_size=None):
        self.total = total_size
        self.update((block_num - self.last_block) * block_size)
        self.last_block = block_num

if not isfile(tar_gz_path):
    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:
        urlretrieve(
            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',
            tar_gz_path,
            pbar.hook)

if not isdir(cifar10_dataset_folder_path):
    with tarfile.open(tar_gz_path) as tar:
        tar.extractall()
        tar.close()


tests.test_folder_path(cifar10_dataset_folder_path)


    

    




CIFAR-10 Dataset: 171MB [01:27, 1.96MB/s]                              






    




All files found!

In [6]:

    

%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import helper
import numpy as np

# Explore the dataset
batch_id = 3
sample_id = 1016
helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)


    

    




Stats of batch 3:
Samples: 10000
Label Counts: {0: 994, 1: 1042, 2: 965, 3: 997, 4: 990, 5: 1029, 6: 978, 7: 1015, 8: 961, 9: 1029}
First 20 Labels: [8, 5, 0, 6, 9, 2, 8, 3, 6, 2, 7, 4, 6, 9, 0, 0, 7, 3, 7, 2]

Example of Image 1016:
Image - Min Value: 0 Max Value: 241
Image - Shape: (32, 32, 3)
Label - Label Id: 4 Name: deer






    

In [7]:

    

def normalize(x):
    """
    Normalize a list of sample image data in the range of 0 to 1
    : x: List of image data.  The image shape is (32, 32, 3)
    : return: Numpy array of normalize data
    """
    # TODO: Implement Function
    return (x / np.max(x))


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_normalize(normalize)


    

    




Tests Passed

In [9]:

    

def one_hot_encode(x):
    """
    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.
    : x: List of sample Labels
    : return: Numpy array of one-hot encoded labels
    """
    # TODO: Implement Function
    return np.eye(10)[x]


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_one_hot_encode(one_hot_encode)


    

    




Tests Passed

In [10]:

    

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)


    

In [3]:

    

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import pickle
import problem_unittests as tests
import helper

# Load the Preprocessed Validation data
valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))


    

In [4]:

    

import tensorflow as tf

def neural_net_image_input(image_shape):
    """
    Return a Tensor for a batch of image input
    : image_shape: Shape of the images
    : return: Tensor for image input.
    """
    return tf.placeholder(tf.float32, shape=(None, image_shape[0], image_shape[1], image_shape[2]), name="x")


def neural_net_label_input(n_classes):
    """
    Return a Tensor for a batch of label input
    : n_classes: Number of classes
    : return: Tensor for label input.
    """
    return tf.placeholder(tf.float32, shape=(None, n_classes), name="y")


def neural_net_keep_prob_input():
    """
    Return a Tensor for keep probability
    : return: Tensor for keep probability.
    """
    return tf.placeholder(tf.float32, name="keep_prob")


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tf.reset_default_graph()
tests.test_nn_image_inputs(neural_net_image_input)
tests.test_nn_label_inputs(neural_net_label_input)
tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)


    

    




Image Input Tests Passed.
Label Input Tests Passed.
Keep Prob Tests Passed.

In [5]:

    

def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):
    """
    Apply convolution then max pooling to x_tensor
    :param x_tensor: TensorFlow Tensor
    :param conv_num_outputs: Number of outputs for the convolutional layer
    :param conv_ksize: kernal size 2-D Tuple for the convolutional layer
    :param conv_strides: Stride 2-D Tuple for convolution
    :param pool_ksize: kernal size 2-D Tuple for pool
    :param pool_strides: Stride 2-D Tuple for pool
    : return: A tensor that represents convolution and max pooling of x_tensor
    """

    weights = tf.Variable(tf.truncated_normal([conv_ksize[0], conv_ksize[1], 
                                              int(x_tensor.get_shape()[3]), 
                                              conv_num_outputs], stddev=0.1))
    bias = tf.Variable(tf.truncated_normal([conv_num_outputs], stddev=0.1))
    
    pool_strides = [1, pool_strides[0], pool_strides[1], 1]
    pool_ksize = [1, pool_ksize[0], pool_ksize[1], 1]
    conv_strides = [1, conv_strides[0], conv_strides[1], 1]
    
    conv_layer = tf.nn.conv2d(x_tensor, weights, strides=conv_strides, padding='SAME')
    conv_layer = tf.nn.bias_add(conv_layer, bias)
    conv_layer = tf.nn.relu(conv_layer)
    conv_layer = tf.nn.max_pool(
        conv_layer,
        ksize=pool_ksize,
        strides=pool_strides,
        padding='SAME')
    
    return conv_layer




"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_con_pool(conv2d_maxpool)


    

    




Tests Passed

In [6]:

    

def flatten(x_tensor):
    """
    Flatten x_tensor to (Batch Size, Flattened Image Size)
    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.
    : return: A tensor of size (Batch Size, Flattened Image Size).
    """
    return tf.reshape(x_tensor, shape=[-1, int(x_tensor.shape[1])*int(x_tensor.shape[2])*int(x_tensor.shape[3])])


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_flatten(flatten)


    

    




Tests Passed

In [7]:

    

def fully_conn(x_tensor, num_outputs):
    """
    Apply a fully connected layer to x_tensor using weight and bias
    : x_tensor: A 2-D tensor where the first dimension is batch size.
    : num_outputs: The number of output that the new tensor should be.
    : return: A 2-D tensor where the second dimension is num_outputs.
    """
    weights = tf.Variable(tf.truncated_normal([int(x_tensor.shape[1]), num_outputs], stddev=0.1))
    bias = tf.Variable(tf.truncated_normal([num_outputs], stddev=0.1))
    
    return tf.nn.relu(tf.matmul(x_tensor, weights) + bias)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_fully_conn(fully_conn)


    

    




Tests Passed

In [8]:

    

def output(x_tensor, num_outputs):
    """
    Apply a output layer to x_tensor using weight and bias
    : x_tensor: A 2-D tensor where the first dimension is batch size.
    : num_outputs: The number of output that the new tensor should be.
    : return: A 2-D tensor where the second dimension is num_outputs.
    """
    weights = tf.Variable(tf.truncated_normal([int(x_tensor.shape[1]), num_outputs], stddev=0.1))
    bias = tf.Variable(tf.truncated_normal([num_outputs], stddev=0.1))
    
    return tf.add(tf.matmul(x_tensor, weights), bias)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_output(output)


    

    




Tests Passed

In [31]:

    

def conv_net(x, keep_prob):
    """
    Create a convolutional neural network model
    : x: Placeholder tensor that holds image data.
    : keep_prob: Placeholder tensor that hold dropout keep probability.
    : return: Tensor that represents logits
    """
    # TODO: Apply 1, 2, or 3 Convolution and Max Pool layers
    #    Play around with different number of outputs, kernel size and stride
    # Function Definition from Above:
    #conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)
    layer = conv2d_maxpool(x, 32, (4, 4), (2, 2), (4, 4), (2, 2))
    #layer = tf.nn.dropout(layer, keep_prob)
    layer = conv2d_maxpool(layer, 64, (3, 3), (2, 2), (3, 3), (2, 2))
    layer = tf.nn.dropout(layer, keep_prob)
    #layer = conv2d_maxpool(layer, 128, (4, 4), (2, 2), (2, 2), (2, 2))
    #layer = tf.nn.dropout(layer, keep_prob)

    # TODO: Apply a Flatten Layer
    # Function Definition from Above:
    layer = flatten(layer)

    # TODO: Apply 1, 2, or 3 Fully Connected Layers
    #    Play around with different number of outputs
    # Function Definition from Above:
    layer = fully_conn(layer, 40)
    layer = fully_conn(layer, 40)
    
    # TODO: Apply an Output Layer
    #    Set this to the number of classes
    # Function Definition from Above:
    #output(layer, num_outputs)
    
    
    # TODO: return output
    return output(layer, 10)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""

##############################
## Build the Neural Network ##
##############################

# Remove previous weights, bias, inputs, etc..
tf.reset_default_graph()

# Inputs
x = neural_net_image_input((32, 32, 3))
y = neural_net_label_input(10)
keep_prob = neural_net_keep_prob_input()

# Model
logits = conv_net(x, keep_prob)

# Name logits Tensor, so that is can be loaded from disk after training
logits = tf.identity(logits, name='logits')

# Loss and Optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)

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

tests.test_conv_net(conv_net)


    

    




Neural Network Built!

In [16]:

    

def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):
    """
    Optimize the session on a batch of images and labels
    : session: Current TensorFlow session
    : optimizer: TensorFlow optimizer function
    : keep_probability: keep probability
    : feature_batch: Batch of Numpy image data
    : label_batch: Batch of Numpy label data
    """
    session.run(optimizer, feed_dict={x: feature_batch, y: label_batch, keep_prob: keep_probability})

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_train_nn(train_neural_network)


    

    




Tests Passed

In [17]:

    

def print_stats(session, feature_batch, label_batch, cost, accuracy):
    """
    Print information about loss and validation accuracy
    : session: Current TensorFlow session
    : feature_batch: Batch of Numpy image data
    : label_batch: Batch of Numpy label data
    : cost: TensorFlow cost function
    : accuracy: TensorFlow accuracy function
    """
    loss = session.run(cost, feed_dict = {x: feature_batch, y: label_batch, keep_prob: 1.0})
    accuracy = session.run(accuracy, feed_dict = {x: valid_features, y: valid_labels, keep_prob: 1.0})
    
    print("Loss: {}".format(loss))
    print("Validation Accuracy: {}".format(accuracy))


    

In [36]:

    

# TODO: Tune Parameters
epochs = 100
batch_size = 256
keep_probability = 0.8


    

In [37]:

    

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
print('Checking the Training on a Single Batch...')
with tf.Session() as sess:
    # Initializing the variables
    sess.run(tf.global_variables_initializer())
    
    # Training cycle
    for epoch in range(epochs):
        batch_i = 1
        for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):
            train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
        print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')
        print_stats(sess, batch_features, batch_labels, cost, accuracy)


    

    




Checking the Training on a Single Batch...
Epoch  1, CIFAR-10 Batch 1:  Loss: 2.1746606826782227
Validation Accuracy: 0.18400000035762787
Epoch  2, CIFAR-10 Batch 1:  Loss: 2.0287394523620605
Validation Accuracy: 0.2662000060081482
Epoch  3, CIFAR-10 Batch 1:  Loss: 1.9457350969314575
Validation Accuracy: 0.29840001463890076
Epoch  4, CIFAR-10 Batch 1:  Loss: 1.8301498889923096
Validation Accuracy: 0.3463999927043915
Epoch  5, CIFAR-10 Batch 1:  Loss: 1.760606288909912
Validation Accuracy: 0.3734000027179718
Epoch  6, CIFAR-10 Batch 1:  Loss: 1.6915462017059326
Validation Accuracy: 0.38419997692108154
Epoch  7, CIFAR-10 Batch 1:  Loss: 1.6186200380325317
Validation Accuracy: 0.39819997549057007
Epoch  8, CIFAR-10 Batch 1:  Loss: 1.5882208347320557
Validation Accuracy: 0.41159993410110474
Epoch  9, CIFAR-10 Batch 1:  Loss: 1.5262558460235596
Validation Accuracy: 0.41899996995925903
Epoch 10, CIFAR-10 Batch 1:  Loss: 1.4671363830566406
Validation Accuracy: 0.4283999800682068
Epoch 11, CIFAR-10 Batch 1:  Loss: 1.429485559463501
Validation Accuracy: 0.44439995288848877
Epoch 12, CIFAR-10 Batch 1:  Loss: 1.393850564956665
Validation Accuracy: 0.444599986076355
Epoch 13, CIFAR-10 Batch 1:  Loss: 1.3592623472213745
Validation Accuracy: 0.454399973154068
Epoch 14, CIFAR-10 Batch 1:  Loss: 1.3215725421905518
Validation Accuracy: 0.4605999290943146
Epoch 15, CIFAR-10 Batch 1:  Loss: 1.2827091217041016
Validation Accuracy: 0.4633999466896057
Epoch 16, CIFAR-10 Batch 1:  Loss: 1.2331130504608154
Validation Accuracy: 0.4773999750614166
Epoch 17, CIFAR-10 Batch 1:  Loss: 1.1900405883789062
Validation Accuracy: 0.4809999465942383
Epoch 18, CIFAR-10 Batch 1:  Loss: 1.1592490673065186
Validation Accuracy: 0.4793999493122101
Epoch 19, CIFAR-10 Batch 1:  Loss: 1.1357645988464355
Validation Accuracy: 0.4941999316215515
Epoch 20, CIFAR-10 Batch 1:  Loss: 1.1122007369995117
Validation Accuracy: 0.4923999607563019
Epoch 21, CIFAR-10 Batch 1:  Loss: 1.0796908140182495
Validation Accuracy: 0.49939998984336853
Epoch 22, CIFAR-10 Batch 1:  Loss: 1.0454235076904297
Validation Accuracy: 0.5063999891281128
Epoch 23, CIFAR-10 Batch 1:  Loss: 1.0266528129577637
Validation Accuracy: 0.5091999769210815
Epoch 24, CIFAR-10 Batch 1:  Loss: 0.971301794052124
Validation Accuracy: 0.514799952507019
Epoch 25, CIFAR-10 Batch 1:  Loss: 0.940711498260498
Validation Accuracy: 0.514799952507019
Epoch 26, CIFAR-10 Batch 1:  Loss: 0.9319143891334534
Validation Accuracy: 0.5127999782562256
Epoch 27, CIFAR-10 Batch 1:  Loss: 0.8847864866256714
Validation Accuracy: 0.5201998949050903
Epoch 28, CIFAR-10 Batch 1:  Loss: 0.8622934818267822
Validation Accuracy: 0.5237999558448792
Epoch 29, CIFAR-10 Batch 1:  Loss: 0.8206983804702759
Validation Accuracy: 0.5283999443054199
Epoch 30, CIFAR-10 Batch 1:  Loss: 0.8045783042907715
Validation Accuracy: 0.5311999320983887
Epoch 31, CIFAR-10 Batch 1:  Loss: 0.7602722644805908
Validation Accuracy: 0.535599946975708
Epoch 32, CIFAR-10 Batch 1:  Loss: 0.7494924664497375
Validation Accuracy: 0.5423999428749084
Epoch 33, CIFAR-10 Batch 1:  Loss: 0.7416500449180603
Validation Accuracy: 0.5375999808311462
Epoch 34, CIFAR-10 Batch 1:  Loss: 0.7240997552871704
Validation Accuracy: 0.5371999740600586
Epoch 35, CIFAR-10 Batch 1:  Loss: 0.6896461248397827
Validation Accuracy: 0.5437999367713928
Epoch 36, CIFAR-10 Batch 1:  Loss: 0.6698452234268188
Validation Accuracy: 0.5405999422073364
Epoch 37, CIFAR-10 Batch 1:  Loss: 0.6589939594268799
Validation Accuracy: 0.5449999570846558
Epoch 38, CIFAR-10 Batch 1:  Loss: 0.6363341808319092
Validation Accuracy: 0.5459998846054077
Epoch 39, CIFAR-10 Batch 1:  Loss: 0.626771092414856
Validation Accuracy: 0.5441999435424805
Epoch 40, CIFAR-10 Batch 1:  Loss: 0.604494571685791
Validation Accuracy: 0.544999897480011
Epoch 41, CIFAR-10 Batch 1:  Loss: 0.5974850654602051
Validation Accuracy: 0.5501999855041504
Epoch 42, CIFAR-10 Batch 1:  Loss: 0.5834242105484009
Validation Accuracy: 0.5535999536514282
Epoch 43, CIFAR-10 Batch 1:  Loss: 0.5942342281341553
Validation Accuracy: 0.5543999671936035
Epoch 44, CIFAR-10 Batch 1:  Loss: 0.5883170366287231
Validation Accuracy: 0.556999921798706
Epoch 45, CIFAR-10 Batch 1:  Loss: 0.5361185073852539
Validation Accuracy: 0.5587999224662781
Epoch 46, CIFAR-10 Batch 1:  Loss: 0.5044657588005066
Validation Accuracy: 0.5667999386787415
Epoch 47, CIFAR-10 Batch 1:  Loss: 0.5097840428352356
Validation Accuracy: 0.5681999325752258
Epoch 48, CIFAR-10 Batch 1:  Loss: 0.48674461245536804
Validation Accuracy: 0.5641999244689941
Epoch 49, CIFAR-10 Batch 1:  Loss: 0.4710724651813507
Validation Accuracy: 0.558199942111969
Epoch 50, CIFAR-10 Batch 1:  Loss: 0.4694976210594177
Validation Accuracy: 0.5731999278068542
Epoch 51, CIFAR-10 Batch 1:  Loss: 0.4481922686100006
Validation Accuracy: 0.5699999332427979
Epoch 52, CIFAR-10 Batch 1:  Loss: 0.4443246126174927
Validation Accuracy: 0.5727999210357666
Epoch 53, CIFAR-10 Batch 1:  Loss: 0.4463832378387451
Validation Accuracy: 0.5635998845100403
Epoch 54, CIFAR-10 Batch 1:  Loss: 0.44175657629966736
Validation Accuracy: 0.5759999752044678
Epoch 55, CIFAR-10 Batch 1:  Loss: 0.41942736506462097
Validation Accuracy: 0.5777999758720398
Epoch 56, CIFAR-10 Batch 1:  Loss: 0.4077526926994324
Validation Accuracy: 0.5757999420166016
Epoch 57, CIFAR-10 Batch 1:  Loss: 0.39177778363227844
Validation Accuracy: 0.5637999773025513
Epoch 58, CIFAR-10 Batch 1:  Loss: 0.38824179768562317
Validation Accuracy: 0.5763999223709106
Epoch 59, CIFAR-10 Batch 1:  Loss: 0.3773651123046875
Validation Accuracy: 0.5799999237060547
Epoch 60, CIFAR-10 Batch 1:  Loss: 0.3739699125289917
Validation Accuracy: 0.5701999068260193
Epoch 61, CIFAR-10 Batch 1:  Loss: 0.3562885522842407
Validation Accuracy: 0.5849999785423279
Epoch 62, CIFAR-10 Batch 1:  Loss: 0.3653099834918976
Validation Accuracy: 0.5753999352455139
Epoch 63, CIFAR-10 Batch 1:  Loss: 0.3213104009628296
Validation Accuracy: 0.5767999291419983
Epoch 64, CIFAR-10 Batch 1:  Loss: 0.3085307776927948
Validation Accuracy: 0.5735999345779419
Epoch 65, CIFAR-10 Batch 1:  Loss: 0.3124387264251709
Validation Accuracy: 0.5619999170303345
Epoch 66, CIFAR-10 Batch 1:  Loss: 0.3073600232601166
Validation Accuracy: 0.5763999223709106
Epoch 67, CIFAR-10 Batch 1:  Loss: 0.30320054292678833
Validation Accuracy: 0.5817998647689819
Epoch 68, CIFAR-10 Batch 1:  Loss: 0.309445858001709
Validation Accuracy: 0.5717998743057251
Epoch 69, CIFAR-10 Batch 1:  Loss: 0.30452796816825867
Validation Accuracy: 0.5683999061584473
Epoch 70, CIFAR-10 Batch 1:  Loss: 0.29527944326400757
Validation Accuracy: 0.5785999298095703
Epoch 71, CIFAR-10 Batch 1:  Loss: 0.28981733322143555
Validation Accuracy: 0.5739999413490295
Epoch 72, CIFAR-10 Batch 1:  Loss: 0.29755088686943054
Validation Accuracy: 0.5761998891830444
Epoch 73, CIFAR-10 Batch 1:  Loss: 0.30996572971343994
Validation Accuracy: 0.5701998472213745
Epoch 74, CIFAR-10 Batch 1:  Loss: 0.30247440934181213
Validation Accuracy: 0.5505999326705933
Epoch 75, CIFAR-10 Batch 1:  Loss: 0.2648955285549164
Validation Accuracy: 0.5755998492240906
Epoch 76, CIFAR-10 Batch 1:  Loss: 0.2419528365135193
Validation Accuracy: 0.5773999691009521
Epoch 77, CIFAR-10 Batch 1:  Loss: 0.23336437344551086
Validation Accuracy: 0.5841999053955078
Epoch 78, CIFAR-10 Batch 1:  Loss: 0.23413966596126556
Validation Accuracy: 0.5797998905181885
Epoch 79, CIFAR-10 Batch 1:  Loss: 0.22126254439353943
Validation Accuracy: 0.5809999108314514
Epoch 80, CIFAR-10 Batch 1:  Loss: 0.2127663791179657
Validation Accuracy: 0.5765999555587769
Epoch 81, CIFAR-10 Batch 1:  Loss: 0.22599779069423676
Validation Accuracy: 0.5705999135971069
Epoch 82, CIFAR-10 Batch 1:  Loss: 0.22242745757102966
Validation Accuracy: 0.5627999901771545
Epoch 83, CIFAR-10 Batch 1:  Loss: 0.2043347954750061
Validation Accuracy: 0.586199939250946
Epoch 84, CIFAR-10 Batch 1:  Loss: 0.19840021431446075
Validation Accuracy: 0.5815998911857605
Epoch 85, CIFAR-10 Batch 1:  Loss: 0.18979302048683167
Validation Accuracy: 0.5801998972892761
Epoch 86, CIFAR-10 Batch 1:  Loss: 0.19136761128902435
Validation Accuracy: 0.5835999250411987
Epoch 87, CIFAR-10 Batch 1:  Loss: 0.1836610734462738
Validation Accuracy: 0.5719999074935913
Epoch 88, CIFAR-10 Batch 1:  Loss: 0.2077423632144928
Validation Accuracy: 0.5625998973846436
Epoch 89, CIFAR-10 Batch 1:  Loss: 0.18396669626235962
Validation Accuracy: 0.5679999589920044
Epoch 90, CIFAR-10 Batch 1:  Loss: 0.15346796810626984
Validation Accuracy: 0.5811998844146729
Epoch 91, CIFAR-10 Batch 1:  Loss: 0.15729911625385284
Validation Accuracy: 0.5747998952865601
Epoch 92, CIFAR-10 Batch 1:  Loss: 0.1647510826587677
Validation Accuracy: 0.5649999380111694
Epoch 93, CIFAR-10 Batch 1:  Loss: 0.19637228548526764
Validation Accuracy: 0.5691999197006226
Epoch 94, CIFAR-10 Batch 1:  Loss: 0.16514122486114502
Validation Accuracy: 0.5759999752044678
Epoch 95, CIFAR-10 Batch 1:  Loss: 0.16009865701198578
Validation Accuracy: 0.5773999094963074
Epoch 96, CIFAR-10 Batch 1:  Loss: 0.15932121872901917
Validation Accuracy: 0.5701999664306641
Epoch 97, CIFAR-10 Batch 1:  Loss: 0.1455449014902115
Validation Accuracy: 0.5703999400138855
Epoch 98, CIFAR-10 Batch 1:  Loss: 0.13883209228515625
Validation Accuracy: 0.5705999135971069
Epoch 99, CIFAR-10 Batch 1:  Loss: 0.13548415899276733
Validation Accuracy: 0.5699999332427979
Epoch 100, CIFAR-10 Batch 1:  Loss: 0.15523676574230194
Validation Accuracy: 0.5727999210357666

In [38]:

    

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
save_model_path = './image_classification'

print('Training...')
with tf.Session() as sess:
    # Initializing the variables
    sess.run(tf.global_variables_initializer())
    
    # Training cycle
    for epoch in range(epochs):
        # Loop over all batches
        n_batches = 5
        for batch_i in range(1, n_batches + 1):
            for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):
                train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
            print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')
            print_stats(sess, batch_features, batch_labels, cost, accuracy)
            
    # Save Model
    saver = tf.train.Saver()
    save_path = saver.save(sess, save_model_path)


    

    




Training...
Epoch  1, CIFAR-10 Batch 1:  Loss: 2.1977498531341553
Validation Accuracy: 0.20059999823570251
Epoch  1, CIFAR-10 Batch 2:  Loss: 1.988148808479309
Validation Accuracy: 0.2833999693393707
Epoch  1, CIFAR-10 Batch 3:  Loss: 1.6414859294891357
Validation Accuracy: 0.3081999719142914
Epoch  1, CIFAR-10 Batch 4:  Loss: 1.7286639213562012
Validation Accuracy: 0.34880000352859497
Epoch  1, CIFAR-10 Batch 5:  Loss: 1.688643217086792
Validation Accuracy: 0.36459997296333313
Epoch  2, CIFAR-10 Batch 1:  Loss: 1.7484030723571777
Validation Accuracy: 0.3937999904155731
Epoch  2, CIFAR-10 Batch 2:  Loss: 1.7505152225494385
Validation Accuracy: 0.399399995803833
Epoch  2, CIFAR-10 Batch 3:  Loss: 1.3875747919082642
Validation Accuracy: 0.39879995584487915
Epoch  2, CIFAR-10 Batch 4:  Loss: 1.6338930130004883
Validation Accuracy: 0.42879998683929443
Epoch  2, CIFAR-10 Batch 5:  Loss: 1.4947240352630615
Validation Accuracy: 0.437999963760376
Epoch  3, CIFAR-10 Batch 1:  Loss: 1.5879207849502563
Validation Accuracy: 0.44620001316070557
Epoch  3, CIFAR-10 Batch 2:  Loss: 1.611999750137329
Validation Accuracy: 0.45579993724823
Epoch  3, CIFAR-10 Batch 3:  Loss: 1.2323493957519531
Validation Accuracy: 0.44919997453689575
Epoch  3, CIFAR-10 Batch 4:  Loss: 1.5603946447372437
Validation Accuracy: 0.47200000286102295
Epoch  3, CIFAR-10 Batch 5:  Loss: 1.4046629667282104
Validation Accuracy: 0.46699994802474976
Epoch  4, CIFAR-10 Batch 1:  Loss: 1.4267324209213257
Validation Accuracy: 0.4829999506473541
Epoch  4, CIFAR-10 Batch 2:  Loss: 1.4663374423980713
Validation Accuracy: 0.49199995398521423
Epoch  4, CIFAR-10 Batch 3:  Loss: 1.132730484008789
Validation Accuracy: 0.4949999451637268
Epoch  4, CIFAR-10 Batch 4:  Loss: 1.4437487125396729
Validation Accuracy: 0.4997999668121338
Epoch  4, CIFAR-10 Batch 5:  Loss: 1.2886548042297363
Validation Accuracy: 0.4949999451637268
Epoch  5, CIFAR-10 Batch 1:  Loss: 1.2837653160095215
Validation Accuracy: 0.5127999782562256
Epoch  5, CIFAR-10 Batch 2:  Loss: 1.3707767724990845
Validation Accuracy: 0.5059999823570251
Epoch  5, CIFAR-10 Batch 3:  Loss: 1.0916495323181152
Validation Accuracy: 0.5121999979019165
Epoch  5, CIFAR-10 Batch 4:  Loss: 1.3771164417266846
Validation Accuracy: 0.5295999646186829
Epoch  5, CIFAR-10 Batch 5:  Loss: 1.2021005153656006
Validation Accuracy: 0.5179999470710754
Epoch  6, CIFAR-10 Batch 1:  Loss: 1.1907583475112915
Validation Accuracy: 0.5267999172210693
Epoch  6, CIFAR-10 Batch 2:  Loss: 1.3104201555252075
Validation Accuracy: 0.5252000093460083
Epoch  6, CIFAR-10 Batch 3:  Loss: 1.0367233753204346
Validation Accuracy: 0.5375999808311462
Epoch  6, CIFAR-10 Batch 4:  Loss: 1.2396347522735596
Validation Accuracy: 0.5433999300003052
Epoch  6, CIFAR-10 Batch 5:  Loss: 1.1205297708511353
Validation Accuracy: 0.5335999131202698
Epoch  7, CIFAR-10 Batch 1:  Loss: 1.1361535787582397
Validation Accuracy: 0.5363999605178833
Epoch  7, CIFAR-10 Batch 2:  Loss: 1.2286032438278198
Validation Accuracy: 0.5410000085830688
Epoch  7, CIFAR-10 Batch 3:  Loss: 0.980373740196228
Validation Accuracy: 0.5471998453140259
Epoch  7, CIFAR-10 Batch 4:  Loss: 1.1710336208343506
Validation Accuracy: 0.5625998973846436
Epoch  7, CIFAR-10 Batch 5:  Loss: 1.059490442276001
Validation Accuracy: 0.5569999814033508
Epoch  8, CIFAR-10 Batch 1:  Loss: 1.0728704929351807
Validation Accuracy: 0.5625998973846436
Epoch  8, CIFAR-10 Batch 2:  Loss: 1.148860216140747
Validation Accuracy: 0.5517998933792114
Epoch  8, CIFAR-10 Batch 3:  Loss: 0.9174534678459167
Validation Accuracy: 0.5633999109268188
Epoch  8, CIFAR-10 Batch 4:  Loss: 1.1140468120574951
Validation Accuracy: 0.573199987411499
Epoch  8, CIFAR-10 Batch 5:  Loss: 1.031442403793335
Validation Accuracy: 0.5679999589920044
Epoch  9, CIFAR-10 Batch 1:  Loss: 1.0553324222564697
Validation Accuracy: 0.5703999400138855
Epoch  9, CIFAR-10 Batch 2:  Loss: 1.1303911209106445
Validation Accuracy: 0.5661999583244324
Epoch  9, CIFAR-10 Batch 3:  Loss: 0.8652509450912476
Validation Accuracy: 0.5719999074935913
Epoch  9, CIFAR-10 Batch 4:  Loss: 1.046394944190979
Validation Accuracy: 0.5777999758720398
Epoch  9, CIFAR-10 Batch 5:  Loss: 0.9565870761871338
Validation Accuracy: 0.5851999521255493
Epoch 10, CIFAR-10 Batch 1:  Loss: 1.001705527305603
Validation Accuracy: 0.5735999345779419
Epoch 10, CIFAR-10 Batch 2:  Loss: 1.081337571144104
Validation Accuracy: 0.574199914932251
Epoch 10, CIFAR-10 Batch 3:  Loss: 0.802502453327179
Validation Accuracy: 0.5787999629974365
Epoch 10, CIFAR-10 Batch 4:  Loss: 1.01389479637146
Validation Accuracy: 0.590999960899353
Epoch 10, CIFAR-10 Batch 5:  Loss: 0.8609421253204346
Validation Accuracy: 0.5951998829841614
Epoch 11, CIFAR-10 Batch 1:  Loss: 0.9621260166168213
Validation Accuracy: 0.5883998870849609
Epoch 11, CIFAR-10 Batch 2:  Loss: 1.0428920984268188
Validation Accuracy: 0.5887999534606934
Epoch 11, CIFAR-10 Batch 3:  Loss: 0.7476211190223694
Validation Accuracy: 0.5831999182701111
Epoch 11, CIFAR-10 Batch 4:  Loss: 0.9659223556518555
Validation Accuracy: 0.5995998978614807
Epoch 11, CIFAR-10 Batch 5:  Loss: 0.8117021322250366
Validation Accuracy: 0.5893999338150024
Epoch 12, CIFAR-10 Batch 1:  Loss: 0.9700316786766052
Validation Accuracy: 0.5921999216079712
Epoch 12, CIFAR-10 Batch 2:  Loss: 1.0059361457824707
Validation Accuracy: 0.5955999493598938
Epoch 12, CIFAR-10 Batch 3:  Loss: 0.7056434750556946
Validation Accuracy: 0.5903998613357544
Epoch 12, CIFAR-10 Batch 4:  Loss: 0.9203470349311829
Validation Accuracy: 0.6017999649047852
Epoch 12, CIFAR-10 Batch 5:  Loss: 0.7557036280632019
Validation Accuracy: 0.6007999777793884
Epoch 13, CIFAR-10 Batch 1:  Loss: 0.9592165946960449
Validation Accuracy: 0.5991998910903931
Epoch 13, CIFAR-10 Batch 2:  Loss: 0.9515608549118042
Validation Accuracy: 0.6037998795509338
Epoch 13, CIFAR-10 Batch 3:  Loss: 0.6732942461967468
Validation Accuracy: 0.5929999351501465
Epoch 13, CIFAR-10 Batch 4:  Loss: 0.8724186420440674
Validation Accuracy: 0.6089999079704285
Epoch 13, CIFAR-10 Batch 5:  Loss: 0.7073040008544922
Validation Accuracy: 0.6115999221801758
Epoch 14, CIFAR-10 Batch 1:  Loss: 0.907864511013031
Validation Accuracy: 0.6123999357223511
Epoch 14, CIFAR-10 Batch 2:  Loss: 0.8996661901473999
Validation Accuracy: 0.6159999370574951
Epoch 14, CIFAR-10 Batch 3:  Loss: 0.6387280225753784
Validation Accuracy: 0.5951998829841614
Epoch 14, CIFAR-10 Batch 4:  Loss: 0.8259354829788208
Validation Accuracy: 0.6143999099731445
Epoch 14, CIFAR-10 Batch 5:  Loss: 0.6516329050064087
Validation Accuracy: 0.613399863243103
Epoch 15, CIFAR-10 Batch 1:  Loss: 0.8909046649932861
Validation Accuracy: 0.6139999032020569
Epoch 15, CIFAR-10 Batch 2:  Loss: 0.8882606029510498
Validation Accuracy: 0.6129999160766602
Epoch 15, CIFAR-10 Batch 3:  Loss: 0.6103866100311279
Validation Accuracy: 0.6001999378204346
Epoch 15, CIFAR-10 Batch 4:  Loss: 0.7851787805557251
Validation Accuracy: 0.6185998916625977
Epoch 15, CIFAR-10 Batch 5:  Loss: 0.6350667476654053
Validation Accuracy: 0.6129999160766602
Epoch 16, CIFAR-10 Batch 1:  Loss: 0.8888770937919617
Validation Accuracy: 0.6191999316215515
Epoch 16, CIFAR-10 Batch 2:  Loss: 0.8311955332756042
Validation Accuracy: 0.6213999390602112
Epoch 16, CIFAR-10 Batch 3:  Loss: 0.554959774017334
Validation Accuracy: 0.6107998490333557
Epoch 16, CIFAR-10 Batch 4:  Loss: 0.7638996839523315
Validation Accuracy: 0.6183999180793762
Epoch 16, CIFAR-10 Batch 5:  Loss: 0.6164000034332275
Validation Accuracy: 0.6213998794555664
Epoch 17, CIFAR-10 Batch 1:  Loss: 0.8789526224136353
Validation Accuracy: 0.6041998863220215
Epoch 17, CIFAR-10 Batch 2:  Loss: 0.787085235118866
Validation Accuracy: 0.6219998598098755
Epoch 17, CIFAR-10 Batch 3:  Loss: 0.54900062084198
Validation Accuracy: 0.6149998903274536
Epoch 17, CIFAR-10 Batch 4:  Loss: 0.7117475271224976
Validation Accuracy: 0.6271998882293701
Epoch 17, CIFAR-10 Batch 5:  Loss: 0.6058753728866577
Validation Accuracy: 0.6233998537063599
Epoch 18, CIFAR-10 Batch 1:  Loss: 0.823367714881897
Validation Accuracy: 0.622999906539917
Epoch 18, CIFAR-10 Batch 2:  Loss: 0.766086995601654
Validation Accuracy: 0.6347998976707458
Epoch 18, CIFAR-10 Batch 3:  Loss: 0.5125723481178284
Validation Accuracy: 0.6187998652458191
Epoch 18, CIFAR-10 Batch 4:  Loss: 0.7046117782592773
Validation Accuracy: 0.6239999532699585
Epoch 18, CIFAR-10 Batch 5:  Loss: 0.5821499824523926
Validation Accuracy: 0.6261999011039734
Epoch 19, CIFAR-10 Batch 1:  Loss: 0.8069278597831726
Validation Accuracy: 0.6255999207496643
Epoch 19, CIFAR-10 Batch 2:  Loss: 0.7208167314529419
Validation Accuracy: 0.627799928188324
Epoch 19, CIFAR-10 Batch 3:  Loss: 0.5067182183265686
Validation Accuracy: 0.6149998903274536
Epoch 19, CIFAR-10 Batch 4:  Loss: 0.6833785772323608
Validation Accuracy: 0.6361998915672302
Epoch 19, CIFAR-10 Batch 5:  Loss: 0.5660731792449951
Validation Accuracy: 0.6317999362945557
Epoch 20, CIFAR-10 Batch 1:  Loss: 0.8028745651245117
Validation Accuracy: 0.6333998441696167
Epoch 20, CIFAR-10 Batch 2:  Loss: 0.6952913999557495
Validation Accuracy: 0.6287999749183655
Epoch 20, CIFAR-10 Batch 3:  Loss: 0.48867490887641907
Validation Accuracy: 0.6281999349594116
Epoch 20, CIFAR-10 Batch 4:  Loss: 0.6547895669937134
Validation Accuracy: 0.627599835395813
Epoch 20, CIFAR-10 Batch 5:  Loss: 0.5440071821212769
Validation Accuracy: 0.6307998895645142
Epoch 21, CIFAR-10 Batch 1:  Loss: 0.7800212502479553
Validation Accuracy: 0.6297999024391174
Epoch 21, CIFAR-10 Batch 2:  Loss: 0.6863542795181274
Validation Accuracy: 0.6333999037742615
Epoch 21, CIFAR-10 Batch 3:  Loss: 0.4561624526977539
Validation Accuracy: 0.6241999268531799
Epoch 21, CIFAR-10 Batch 4:  Loss: 0.6119809746742249
Validation Accuracy: 0.6373999118804932
Epoch 21, CIFAR-10 Batch 5:  Loss: 0.5441064834594727
Validation Accuracy: 0.6341999173164368
Epoch 22, CIFAR-10 Batch 1:  Loss: 0.7433232069015503
Validation Accuracy: 0.640799880027771
Epoch 22, CIFAR-10 Batch 2:  Loss: 0.6705741286277771
Validation Accuracy: 0.6349999308586121
Epoch 22, CIFAR-10 Batch 3:  Loss: 0.4015306234359741
Validation Accuracy: 0.6369999051094055
Epoch 22, CIFAR-10 Batch 4:  Loss: 0.6076388359069824
Validation Accuracy: 0.640799880027771
Epoch 22, CIFAR-10 Batch 5:  Loss: 0.5024596452713013
Validation Accuracy: 0.645599901676178
Epoch 23, CIFAR-10 Batch 1:  Loss: 0.7181150913238525
Validation Accuracy: 0.6419999003410339
Epoch 23, CIFAR-10 Batch 2:  Loss: 0.6371383666992188
Validation Accuracy: 0.6381998658180237
Epoch 23, CIFAR-10 Batch 3:  Loss: 0.4023593068122864
Validation Accuracy: 0.631399929523468
Epoch 23, CIFAR-10 Batch 4:  Loss: 0.6169359683990479
Validation Accuracy: 0.6321998834609985
Epoch 23, CIFAR-10 Batch 5:  Loss: 0.5024625658988953
Validation Accuracy: 0.6435998678207397
Epoch 24, CIFAR-10 Batch 1:  Loss: 0.70493084192276
Validation Accuracy: 0.6393998861312866
Epoch 24, CIFAR-10 Batch 2:  Loss: 0.621566116809845
Validation Accuracy: 0.6377999186515808
Epoch 24, CIFAR-10 Batch 3:  Loss: 0.4277169108390808
Validation Accuracy: 0.6439999341964722
Epoch 24, CIFAR-10 Batch 4:  Loss: 0.5740879774093628
Validation Accuracy: 0.640999972820282
Epoch 24, CIFAR-10 Batch 5:  Loss: 0.4723868668079376
Validation Accuracy: 0.6444000005722046
Epoch 25, CIFAR-10 Batch 1:  Loss: 0.7005448937416077
Validation Accuracy: 0.6351999044418335
Epoch 25, CIFAR-10 Batch 2:  Loss: 0.5925984978675842
Validation Accuracy: 0.6395999193191528
Epoch 25, CIFAR-10 Batch 3:  Loss: 0.4026998281478882
Validation Accuracy: 0.6429998874664307
Epoch 25, CIFAR-10 Batch 4:  Loss: 0.5574508905410767
Validation Accuracy: 0.648999810218811
Epoch 25, CIFAR-10 Batch 5:  Loss: 0.43602555990219116
Validation Accuracy: 0.6451998949050903
Epoch 26, CIFAR-10 Batch 1:  Loss: 0.6487689018249512
Validation Accuracy: 0.6345998644828796
Epoch 26, CIFAR-10 Batch 2:  Loss: 0.573045015335083
Validation Accuracy: 0.6369998455047607
Epoch 26, CIFAR-10 Batch 3:  Loss: 0.38534870743751526
Validation Accuracy: 0.642599880695343
Epoch 26, CIFAR-10 Batch 4:  Loss: 0.5258550047874451
Validation Accuracy: 0.6463999152183533
Epoch 26, CIFAR-10 Batch 5:  Loss: 0.42165523767471313
Validation Accuracy: 0.64739990234375
Epoch 27, CIFAR-10 Batch 1:  Loss: 0.6617226004600525
Validation Accuracy: 0.6299999356269836
Epoch 27, CIFAR-10 Batch 2:  Loss: 0.5610063076019287
Validation Accuracy: 0.6465998888015747
Epoch 27, CIFAR-10 Batch 3:  Loss: 0.3872659206390381
Validation Accuracy: 0.6363998651504517
Epoch 27, CIFAR-10 Batch 4:  Loss: 0.5089085102081299
Validation Accuracy: 0.6479998826980591
Epoch 27, CIFAR-10 Batch 5:  Loss: 0.426917165517807
Validation Accuracy: 0.6481998562812805
Epoch 28, CIFAR-10 Batch 1:  Loss: 0.6445666551589966
Validation Accuracy: 0.627799928188324
Epoch 28, CIFAR-10 Batch 2:  Loss: 0.5478131175041199
Validation Accuracy: 0.6429998874664307
Epoch 28, CIFAR-10 Batch 3:  Loss: 0.3620033860206604
Validation Accuracy: 0.6415998935699463
Epoch 28, CIFAR-10 Batch 4:  Loss: 0.48613542318344116
Validation Accuracy: 0.6429999470710754
Epoch 28, CIFAR-10 Batch 5:  Loss: 0.4119555652141571
Validation Accuracy: 0.6519998908042908
Epoch 29, CIFAR-10 Batch 1:  Loss: 0.6333691477775574
Validation Accuracy: 0.6339999437332153
Epoch 29, CIFAR-10 Batch 2:  Loss: 0.5522593855857849
Validation Accuracy: 0.6425999402999878
Epoch 29, CIFAR-10 Batch 3:  Loss: 0.3478941321372986
Validation Accuracy: 0.6375998258590698
Epoch 29, CIFAR-10 Batch 4:  Loss: 0.49010148644447327
Validation Accuracy: 0.6447998881340027
Epoch 29, CIFAR-10 Batch 5:  Loss: 0.45046722888946533
Validation Accuracy: 0.6481999158859253
Epoch 30, CIFAR-10 Batch 1:  Loss: 0.6273802518844604
Validation Accuracy: 0.6393999457359314
Epoch 30, CIFAR-10 Batch 2:  Loss: 0.5654564499855042
Validation Accuracy: 0.6475998163223267
Epoch 30, CIFAR-10 Batch 3:  Loss: 0.328299880027771
Validation Accuracy: 0.6345999240875244
Epoch 30, CIFAR-10 Batch 4:  Loss: 0.46261247992515564
Validation Accuracy: 0.6511999368667603
Epoch 30, CIFAR-10 Batch 5:  Loss: 0.41128766536712646
Validation Accuracy: 0.6535999178886414
Epoch 31, CIFAR-10 Batch 1:  Loss: 0.644677460193634
Validation Accuracy: 0.6235998868942261
Epoch 31, CIFAR-10 Batch 2:  Loss: 0.4801250696182251
Validation Accuracy: 0.6431998610496521
Epoch 31, CIFAR-10 Batch 3:  Loss: 0.32946228981018066
Validation Accuracy: 0.6465998888015747
Epoch 31, CIFAR-10 Batch 4:  Loss: 0.46382737159729004
Validation Accuracy: 0.6463999152183533
Epoch 31, CIFAR-10 Batch 5:  Loss: 0.39182159304618835
Validation Accuracy: 0.6537998914718628
Epoch 32, CIFAR-10 Batch 1:  Loss: 0.588614284992218
Validation Accuracy: 0.6431998610496521
Epoch 32, CIFAR-10 Batch 2:  Loss: 0.5043936967849731
Validation Accuracy: 0.6475999355316162
Epoch 32, CIFAR-10 Batch 3:  Loss: 0.30502215027809143
Validation Accuracy: 0.6459999084472656
Epoch 32, CIFAR-10 Batch 4:  Loss: 0.4441353678703308
Validation Accuracy: 0.6575998663902283
Epoch 32, CIFAR-10 Batch 5:  Loss: 0.3586779832839966
Validation Accuracy: 0.653799831867218
Epoch 33, CIFAR-10 Batch 1:  Loss: 0.5669272541999817
Validation Accuracy: 0.6271999478340149
Epoch 33, CIFAR-10 Batch 2:  Loss: 0.4525797963142395
Validation Accuracy: 0.6493998765945435
Epoch 33, CIFAR-10 Batch 3:  Loss: 0.3151791989803314
Validation Accuracy: 0.6495999097824097
Epoch 33, CIFAR-10 Batch 4:  Loss: 0.45039233565330505
Validation Accuracy: 0.6573998928070068
Epoch 33, CIFAR-10 Batch 5:  Loss: 0.37638717889785767
Validation Accuracy: 0.6523998975753784
Epoch 34, CIFAR-10 Batch 1:  Loss: 0.6012490391731262
Validation Accuracy: 0.6333998441696167
Epoch 34, CIFAR-10 Batch 2:  Loss: 0.4306783974170685
Validation Accuracy: 0.6445999145507812
Epoch 34, CIFAR-10 Batch 3:  Loss: 0.2825191915035248
Validation Accuracy: 0.6471998691558838
Epoch 34, CIFAR-10 Batch 4:  Loss: 0.4442032277584076
Validation Accuracy: 0.658799946308136
Epoch 34, CIFAR-10 Batch 5:  Loss: 0.35091233253479004
Validation Accuracy: 0.6523998379707336
Epoch 35, CIFAR-10 Batch 1:  Loss: 0.5919427871704102
Validation Accuracy: 0.6351999044418335
Epoch 35, CIFAR-10 Batch 2:  Loss: 0.4563086926937103
Validation Accuracy: 0.6469998955726624
Epoch 35, CIFAR-10 Batch 3:  Loss: 0.2717805504798889
Validation Accuracy: 0.6465998888015747
Epoch 35, CIFAR-10 Batch 4:  Loss: 0.41754812002182007
Validation Accuracy: 0.654999852180481
Epoch 35, CIFAR-10 Batch 5:  Loss: 0.3495824933052063
Validation Accuracy: 0.6513999104499817
Epoch 36, CIFAR-10 Batch 1:  Loss: 0.5482213497161865
Validation Accuracy: 0.6425998210906982
Epoch 36, CIFAR-10 Batch 2:  Loss: 0.427451491355896
Validation Accuracy: 0.6437999606132507
Epoch 36, CIFAR-10 Batch 3:  Loss: 0.2652438282966614
Validation Accuracy: 0.6513998508453369
Epoch 36, CIFAR-10 Batch 4:  Loss: 0.4359656572341919
Validation Accuracy: 0.656799852848053
Epoch 36, CIFAR-10 Batch 5:  Loss: 0.3495517075061798
Validation Accuracy: 0.6551999449729919
Epoch 37, CIFAR-10 Batch 1:  Loss: 0.5460016131401062
Validation Accuracy: 0.6433998346328735
Epoch 37, CIFAR-10 Batch 2:  Loss: 0.4297662675380707
Validation Accuracy: 0.6453999280929565
Epoch 37, CIFAR-10 Batch 3:  Loss: 0.28954851627349854
Validation Accuracy: 0.6533999443054199
Epoch 37, CIFAR-10 Batch 4:  Loss: 0.404166579246521
Validation Accuracy: 0.6569998264312744
Epoch 37, CIFAR-10 Batch 5:  Loss: 0.3481847047805786
Validation Accuracy: 0.6531999111175537
Epoch 38, CIFAR-10 Batch 1:  Loss: 0.5439050793647766
Validation Accuracy: 0.6301999092102051
Epoch 38, CIFAR-10 Batch 2:  Loss: 0.40804582834243774
Validation Accuracy: 0.6369999051094055
Epoch 38, CIFAR-10 Batch 3:  Loss: 0.269390732049942
Validation Accuracy: 0.6477999091148376
Epoch 38, CIFAR-10 Batch 4:  Loss: 0.37944334745407104
Validation Accuracy: 0.6599999666213989
Epoch 38, CIFAR-10 Batch 5:  Loss: 0.3406701385974884
Validation Accuracy: 0.6511998176574707
Epoch 39, CIFAR-10 Batch 1:  Loss: 0.5181202292442322
Validation Accuracy: 0.6453998684883118
Epoch 39, CIFAR-10 Batch 2:  Loss: 0.39736053347587585
Validation Accuracy: 0.6403998732566833
Epoch 39, CIFAR-10 Batch 3:  Loss: 0.25187385082244873
Validation Accuracy: 0.6553998589515686
Epoch 39, CIFAR-10 Batch 4:  Loss: 0.4015665352344513
Validation Accuracy: 0.6539998650550842
Epoch 39, CIFAR-10 Batch 5:  Loss: 0.33895474672317505
Validation Accuracy: 0.6495999097824097
Epoch 40, CIFAR-10 Batch 1:  Loss: 0.49749863147735596
Validation Accuracy: 0.6503998637199402
Epoch 40, CIFAR-10 Batch 2:  Loss: 0.3643900156021118
Validation Accuracy: 0.6477999091148376
Epoch 40, CIFAR-10 Batch 3:  Loss: 0.24874678254127502
Validation Accuracy: 0.6581999063491821
Epoch 40, CIFAR-10 Batch 4:  Loss: 0.3867996633052826
Validation Accuracy: 0.6581998467445374
Epoch 40, CIFAR-10 Batch 5:  Loss: 0.31977537274360657
Validation Accuracy: 0.6609998941421509
Epoch 41, CIFAR-10 Batch 1:  Loss: 0.5021096467971802
Validation Accuracy: 0.6429998874664307
Epoch 41, CIFAR-10 Batch 2:  Loss: 0.37291523814201355
Validation Accuracy: 0.6533999443054199
Epoch 41, CIFAR-10 Batch 3:  Loss: 0.23333808779716492
Validation Accuracy: 0.6579998731613159
Epoch 41, CIFAR-10 Batch 4:  Loss: 0.39001187682151794
Validation Accuracy: 0.657599925994873
Epoch 41, CIFAR-10 Batch 5:  Loss: 0.3040219247341156
Validation Accuracy: 0.6553999185562134
Epoch 42, CIFAR-10 Batch 1:  Loss: 0.4874580502510071
Validation Accuracy: 0.6481999158859253
Epoch 42, CIFAR-10 Batch 2:  Loss: 0.37568768858909607
Validation Accuracy: 0.6519998908042908
Epoch 42, CIFAR-10 Batch 3:  Loss: 0.23452383279800415
Validation Accuracy: 0.6415998935699463
Epoch 42, CIFAR-10 Batch 4:  Loss: 0.38821613788604736
Validation Accuracy: 0.6577998995780945
Epoch 42, CIFAR-10 Batch 5:  Loss: 0.32819944620132446
Validation Accuracy: 0.6499998569488525
Epoch 43, CIFAR-10 Batch 1:  Loss: 0.5197715163230896
Validation Accuracy: 0.6391998529434204
Epoch 43, CIFAR-10 Batch 2:  Loss: 0.3618895411491394
Validation Accuracy: 0.6543999314308167
Epoch 43, CIFAR-10 Batch 3:  Loss: 0.22709479928016663
Validation Accuracy: 0.6595999002456665
Epoch 43, CIFAR-10 Batch 4:  Loss: 0.3705103099346161
Validation Accuracy: 0.6663998961448669
Epoch 43, CIFAR-10 Batch 5:  Loss: 0.2979734539985657
Validation Accuracy: 0.6535999178886414
Epoch 44, CIFAR-10 Batch 1:  Loss: 0.49991461634635925
Validation Accuracy: 0.6531999111175537
Epoch 44, CIFAR-10 Batch 2:  Loss: 0.33553946018218994
Validation Accuracy: 0.6573998928070068
Epoch 44, CIFAR-10 Batch 3:  Loss: 0.21202747523784637
Validation Accuracy: 0.6653998494148254
Epoch 44, CIFAR-10 Batch 4:  Loss: 0.3715304732322693
Validation Accuracy: 0.6615999341011047
Epoch 44, CIFAR-10 Batch 5:  Loss: 0.2981499433517456
Validation Accuracy: 0.6583998799324036
Epoch 45, CIFAR-10 Batch 1:  Loss: 0.4830361008644104
Validation Accuracy: 0.642599880695343
Epoch 45, CIFAR-10 Batch 2:  Loss: 0.3148803412914276
Validation Accuracy: 0.6543998718261719
Epoch 45, CIFAR-10 Batch 3:  Loss: 0.22578972578048706
Validation Accuracy: 0.6563999056816101
Epoch 45, CIFAR-10 Batch 4:  Loss: 0.40322011709213257
Validation Accuracy: 0.6515998840332031
Epoch 45, CIFAR-10 Batch 5:  Loss: 0.31900808215141296
Validation Accuracy: 0.6527999043464661
Epoch 46, CIFAR-10 Batch 1:  Loss: 0.4502914547920227
Validation Accuracy: 0.6539998650550842
Epoch 46, CIFAR-10 Batch 2:  Loss: 0.32677319645881653
Validation Accuracy: 0.6571998000144958
Epoch 46, CIFAR-10 Batch 3:  Loss: 0.22679394483566284
Validation Accuracy: 0.6575998663902283
Epoch 46, CIFAR-10 Batch 4:  Loss: 0.36127907037734985
Validation Accuracy: 0.6679998636245728
Epoch 46, CIFAR-10 Batch 5:  Loss: 0.30114516615867615
Validation Accuracy: 0.6617998480796814
Epoch 47, CIFAR-10 Batch 1:  Loss: 0.4360753297805786
Validation Accuracy: 0.6541999578475952
Epoch 47, CIFAR-10 Batch 2:  Loss: 0.3511624336242676
Validation Accuracy: 0.6607998609542847
Epoch 47, CIFAR-10 Batch 3:  Loss: 0.20950940251350403
Validation Accuracy: 0.6691999435424805
Epoch 47, CIFAR-10 Batch 4:  Loss: 0.35590827465057373
Validation Accuracy: 0.6635998487472534
Epoch 47, CIFAR-10 Batch 5:  Loss: 0.27611738443374634
Validation Accuracy: 0.6583998799324036
Epoch 48, CIFAR-10 Batch 1:  Loss: 0.45979201793670654
Validation Accuracy: 0.6523998975753784
Epoch 48, CIFAR-10 Batch 2:  Loss: 0.3038058876991272
Validation Accuracy: 0.6607999205589294
Epoch 48, CIFAR-10 Batch 3:  Loss: 0.21346788108348846
Validation Accuracy: 0.6635999083518982
Epoch 48, CIFAR-10 Batch 4:  Loss: 0.35509008169174194
Validation Accuracy: 0.6645998954772949
Epoch 48, CIFAR-10 Batch 5:  Loss: 0.2953076958656311
Validation Accuracy: 0.6559998989105225
Epoch 49, CIFAR-10 Batch 1:  Loss: 0.41801658272743225
Validation Accuracy: 0.6601998805999756
Epoch 49, CIFAR-10 Batch 2:  Loss: 0.3135547637939453
Validation Accuracy: 0.6591999530792236
Epoch 49, CIFAR-10 Batch 3:  Loss: 0.21626152098178864
Validation Accuracy: 0.6643999218940735
Epoch 49, CIFAR-10 Batch 4:  Loss: 0.3135557770729065
Validation Accuracy: 0.668799877166748
Epoch 49, CIFAR-10 Batch 5:  Loss: 0.28541791439056396
Validation Accuracy: 0.6635998487472534
Epoch 50, CIFAR-10 Batch 1:  Loss: 0.404996782541275
Validation Accuracy: 0.6667999029159546
Epoch 50, CIFAR-10 Batch 2:  Loss: 0.3203143775463104
Validation Accuracy: 0.6587998867034912
Epoch 50, CIFAR-10 Batch 3:  Loss: 0.21111318469047546
Validation Accuracy: 0.6625999212265015
Epoch 50, CIFAR-10 Batch 4:  Loss: 0.34025391936302185
Validation Accuracy: 0.6629998683929443
Epoch 50, CIFAR-10 Batch 5:  Loss: 0.2742578983306885
Validation Accuracy: 0.665199875831604
Epoch 51, CIFAR-10 Batch 1:  Loss: 0.41268450021743774
Validation Accuracy: 0.6677998900413513
Epoch 51, CIFAR-10 Batch 2:  Loss: 0.3148064613342285
Validation Accuracy: 0.6601998805999756
Epoch 51, CIFAR-10 Batch 3:  Loss: 0.19486704468727112
Validation Accuracy: 0.6661999225616455
Epoch 51, CIFAR-10 Batch 4:  Loss: 0.30908215045928955
Validation Accuracy: 0.6697999238967896
Epoch 51, CIFAR-10 Batch 5:  Loss: 0.2831408679485321
Validation Accuracy: 0.6601998805999756
Epoch 52, CIFAR-10 Batch 1:  Loss: 0.40715086460113525
Validation Accuracy: 0.6633998155593872
Epoch 52, CIFAR-10 Batch 2:  Loss: 0.2955167889595032
Validation Accuracy: 0.6621999144554138
Epoch 52, CIFAR-10 Batch 3:  Loss: 0.19782938063144684
Validation Accuracy: 0.6727998852729797
Epoch 52, CIFAR-10 Batch 4:  Loss: 0.3015593886375427
Validation Accuracy: 0.6689999103546143
Epoch 52, CIFAR-10 Batch 5:  Loss: 0.26455408334732056
Validation Accuracy: 0.6619998812675476
Epoch 53, CIFAR-10 Batch 1:  Loss: 0.4075208902359009
Validation Accuracy: 0.6679998636245728
Epoch 53, CIFAR-10 Batch 2:  Loss: 0.2854780852794647
Validation Accuracy: 0.6675999164581299
Epoch 53, CIFAR-10 Batch 3:  Loss: 0.208605095744133
Validation Accuracy: 0.6683999300003052
Epoch 53, CIFAR-10 Batch 4:  Loss: 0.3071604371070862
Validation Accuracy: 0.6661999225616455
Epoch 53, CIFAR-10 Batch 5:  Loss: 0.26867592334747314
Validation Accuracy: 0.6579999327659607
Epoch 54, CIFAR-10 Batch 1:  Loss: 0.38148701190948486
Validation Accuracy: 0.6547998785972595
Epoch 54, CIFAR-10 Batch 2:  Loss: 0.26000887155532837
Validation Accuracy: 0.6649999022483826
Epoch 54, CIFAR-10 Batch 3:  Loss: 0.20421721041202545
Validation Accuracy: 0.6709999442100525
Epoch 54, CIFAR-10 Batch 4:  Loss: 0.30626803636550903
Validation Accuracy: 0.6675999164581299
Epoch 54, CIFAR-10 Batch 5:  Loss: 0.2606838345527649
Validation Accuracy: 0.6553999185562134
Epoch 55, CIFAR-10 Batch 1:  Loss: 0.3851734697818756
Validation Accuracy: 0.6607998609542847
Epoch 55, CIFAR-10 Batch 2:  Loss: 0.2913861870765686
Validation Accuracy: 0.66159987449646
Epoch 55, CIFAR-10 Batch 3:  Loss: 0.20283013582229614
Validation Accuracy: 0.6659998893737793
Epoch 55, CIFAR-10 Batch 4:  Loss: 0.28033125400543213
Validation Accuracy: 0.6693999171257019
Epoch 55, CIFAR-10 Batch 5:  Loss: 0.2645723819732666
Validation Accuracy: 0.6489998698234558
Epoch 56, CIFAR-10 Batch 1:  Loss: 0.37492018938064575
Validation Accuracy: 0.6617998480796814
Epoch 56, CIFAR-10 Batch 2:  Loss: 0.2587917447090149
Validation Accuracy: 0.6703998446464539
Epoch 56, CIFAR-10 Batch 3:  Loss: 0.20381230115890503
Validation Accuracy: 0.6697999238967896
Epoch 56, CIFAR-10 Batch 4:  Loss: 0.27326658368110657
Validation Accuracy: 0.6761998534202576
Epoch 56, CIFAR-10 Batch 5:  Loss: 0.24407152831554413
Validation Accuracy: 0.6661998629570007
Epoch 57, CIFAR-10 Batch 1:  Loss: 0.3789929747581482
Validation Accuracy: 0.6649999022483826
Epoch 57, CIFAR-10 Batch 2:  Loss: 0.25149789452552795
Validation Accuracy: 0.6725998520851135
Epoch 57, CIFAR-10 Batch 3:  Loss: 0.1887582689523697
Validation Accuracy: 0.6725999116897583
Epoch 57, CIFAR-10 Batch 4:  Loss: 0.2656835913658142
Validation Accuracy: 0.6721998453140259
Epoch 57, CIFAR-10 Batch 5:  Loss: 0.23364564776420593
Validation Accuracy: 0.6625998616218567
Epoch 58, CIFAR-10 Batch 1:  Loss: 0.37848836183547974
Validation Accuracy: 0.6725999116897583
Epoch 58, CIFAR-10 Batch 2:  Loss: 0.2566867470741272
Validation Accuracy: 0.6635998487472534
Epoch 58, CIFAR-10 Batch 3:  Loss: 0.18623892962932587
Validation Accuracy: 0.6685998439788818
Epoch 58, CIFAR-10 Batch 4:  Loss: 0.2835032343864441
Validation Accuracy: 0.668799877166748
Epoch 58, CIFAR-10 Batch 5:  Loss: 0.22174669802188873
Validation Accuracy: 0.6653998494148254
Epoch 59, CIFAR-10 Batch 1:  Loss: 0.3839302659034729
Validation Accuracy: 0.6741998791694641
Epoch 59, CIFAR-10 Batch 2:  Loss: 0.25174593925476074
Validation Accuracy: 0.6631999015808105
Epoch 59, CIFAR-10 Batch 3:  Loss: 0.1853119134902954
Validation Accuracy: 0.6747998595237732
Epoch 59, CIFAR-10 Batch 4:  Loss: 0.2610483765602112
Validation Accuracy: 0.6799998879432678
Epoch 59, CIFAR-10 Batch 5:  Loss: 0.21884450316429138
Validation Accuracy: 0.6505998969078064
Epoch 60, CIFAR-10 Batch 1:  Loss: 0.35686933994293213
Validation Accuracy: 0.6739999055862427
Epoch 60, CIFAR-10 Batch 2:  Loss: 0.24940693378448486
Validation Accuracy: 0.6667999029159546
Epoch 60, CIFAR-10 Batch 3:  Loss: 0.1733761429786682
Validation Accuracy: 0.6725999116897583
Epoch 60, CIFAR-10 Batch 4:  Loss: 0.2735709249973297
Validation Accuracy: 0.6727998852729797
Epoch 60, CIFAR-10 Batch 5:  Loss: 0.21531061828136444
Validation Accuracy: 0.6811997890472412
Epoch 61, CIFAR-10 Batch 1:  Loss: 0.33016520738601685
Validation Accuracy: 0.6621999144554138
Epoch 61, CIFAR-10 Batch 2:  Loss: 0.24481189250946045
Validation Accuracy: 0.6721998453140259
Epoch 61, CIFAR-10 Batch 3:  Loss: 0.19064143300056458
Validation Accuracy: 0.6761998534202576
Epoch 61, CIFAR-10 Batch 4:  Loss: 0.29592180252075195
Validation Accuracy: 0.6751998662948608
Epoch 61, CIFAR-10 Batch 5:  Loss: 0.2379412055015564
Validation Accuracy: 0.6557998657226562
Epoch 62, CIFAR-10 Batch 1:  Loss: 0.3261808454990387
Validation Accuracy: 0.6667999029159546
Epoch 62, CIFAR-10 Batch 2:  Loss: 0.23822683095932007
Validation Accuracy: 0.6691998839378357
Epoch 62, CIFAR-10 Batch 3:  Loss: 0.1809990257024765
Validation Accuracy: 0.6729998588562012
Epoch 62, CIFAR-10 Batch 4:  Loss: 0.22976568341255188
Validation Accuracy: 0.6757999062538147
Epoch 62, CIFAR-10 Batch 5:  Loss: 0.21085895597934723
Validation Accuracy: 0.6601998805999756
Epoch 63, CIFAR-10 Batch 1:  Loss: 0.34260109066963196
Validation Accuracy: 0.6697998642921448
Epoch 63, CIFAR-10 Batch 2:  Loss: 0.24308007955551147
Validation Accuracy: 0.6761999130249023
Epoch 63, CIFAR-10 Batch 3:  Loss: 0.16163599491119385
Validation Accuracy: 0.6775998473167419
Epoch 63, CIFAR-10 Batch 4:  Loss: 0.24550211429595947
Validation Accuracy: 0.6769998669624329
Epoch 63, CIFAR-10 Batch 5:  Loss: 0.23147347569465637
Validation Accuracy: 0.6531999111175537
Epoch 64, CIFAR-10 Batch 1:  Loss: 0.3323710262775421
Validation Accuracy: 0.6659998893737793
Epoch 64, CIFAR-10 Batch 2:  Loss: 0.24048739671707153
Validation Accuracy: 0.6647999286651611
Epoch 64, CIFAR-10 Batch 3:  Loss: 0.1644536256790161
Validation Accuracy: 0.6755999326705933
Epoch 64, CIFAR-10 Batch 4:  Loss: 0.23613819479942322
Validation Accuracy: 0.6773999333381653
Epoch 64, CIFAR-10 Batch 5:  Loss: 0.23462675511837006
Validation Accuracy: 0.6647998690605164
Epoch 65, CIFAR-10 Batch 1:  Loss: 0.3080148696899414
Validation Accuracy: 0.6679998636245728
Epoch 65, CIFAR-10 Batch 2:  Loss: 0.2150614708662033
Validation Accuracy: 0.6657998561859131
Epoch 65, CIFAR-10 Batch 3:  Loss: 0.17388251423835754
Validation Accuracy: 0.6807999014854431
Epoch 65, CIFAR-10 Batch 4:  Loss: 0.27977919578552246
Validation Accuracy: 0.6737998723983765
Epoch 65, CIFAR-10 Batch 5:  Loss: 0.22085757553577423
Validation Accuracy: 0.6669999361038208
Epoch 66, CIFAR-10 Batch 1:  Loss: 0.2987882196903229
Validation Accuracy: 0.6659998893737793
Epoch 66, CIFAR-10 Batch 2:  Loss: 0.2284328043460846
Validation Accuracy: 0.6733998656272888
Epoch 66, CIFAR-10 Batch 3:  Loss: 0.14568637311458588
Validation Accuracy: 0.6767998933792114
Epoch 66, CIFAR-10 Batch 4:  Loss: 0.24028794467449188
Validation Accuracy: 0.6753999590873718
Epoch 66, CIFAR-10 Batch 5:  Loss: 0.20090705156326294
Validation Accuracy: 0.6671999096870422
Epoch 67, CIFAR-10 Batch 1:  Loss: 0.31957435607910156
Validation Accuracy: 0.6771999001502991
Epoch 67, CIFAR-10 Batch 2:  Loss: 0.23325882852077484
Validation Accuracy: 0.6761997938156128
Epoch 67, CIFAR-10 Batch 3:  Loss: 0.16996799409389496
Validation Accuracy: 0.6773998737335205
Epoch 67, CIFAR-10 Batch 4:  Loss: 0.229888454079628
Validation Accuracy: 0.6717998385429382
Epoch 67, CIFAR-10 Batch 5:  Loss: 0.18584579229354858
Validation Accuracy: 0.6641998887062073
Epoch 68, CIFAR-10 Batch 1:  Loss: 0.3120509088039398
Validation Accuracy: 0.6735998392105103
Epoch 68, CIFAR-10 Batch 2:  Loss: 0.24065367877483368
Validation Accuracy: 0.6751998662948608
Epoch 68, CIFAR-10 Batch 3:  Loss: 0.15568341314792633
Validation Accuracy: 0.6707999110221863
Epoch 68, CIFAR-10 Batch 4:  Loss: 0.26769059896469116
Validation Accuracy: 0.6717998385429382
Epoch 68, CIFAR-10 Batch 5:  Loss: 0.22120794653892517
Validation Accuracy: 0.6551998853683472
Epoch 69, CIFAR-10 Batch 1:  Loss: 0.3027576804161072
Validation Accuracy: 0.6635999083518982
Epoch 69, CIFAR-10 Batch 2:  Loss: 0.22596099972724915
Validation Accuracy: 0.6663999557495117
Epoch 69, CIFAR-10 Batch 3:  Loss: 0.16494974493980408
Validation Accuracy: 0.6765998601913452
Epoch 69, CIFAR-10 Batch 4:  Loss: 0.23053967952728271
Validation Accuracy: 0.6773998737335205
Epoch 69, CIFAR-10 Batch 5:  Loss: 0.1981363594532013
Validation Accuracy: 0.6713998317718506
Epoch 70, CIFAR-10 Batch 1:  Loss: 0.28627318143844604
Validation Accuracy: 0.6715999245643616
Epoch 70, CIFAR-10 Batch 2:  Loss: 0.22339582443237305
Validation Accuracy: 0.668799877166748
Epoch 70, CIFAR-10 Batch 3:  Loss: 0.14589321613311768
Validation Accuracy: 0.6801998615264893
Epoch 70, CIFAR-10 Batch 4:  Loss: 0.22388076782226562
Validation Accuracy: 0.6749999523162842
Epoch 70, CIFAR-10 Batch 5:  Loss: 0.20397309958934784
Validation Accuracy: 0.6511999368667603
Epoch 71, CIFAR-10 Batch 1:  Loss: 0.3025345206260681
Validation Accuracy: 0.6679998636245728
Epoch 71, CIFAR-10 Batch 2:  Loss: 0.21026787161827087
Validation Accuracy: 0.6663999557495117
Epoch 71, CIFAR-10 Batch 3:  Loss: 0.15017588436603546
Validation Accuracy: 0.6771998405456543
Epoch 71, CIFAR-10 Batch 4:  Loss: 0.20205625891685486
Validation Accuracy: 0.6835998892784119
Epoch 71, CIFAR-10 Batch 5:  Loss: 0.1758928745985031
Validation Accuracy: 0.6689999103546143
Epoch 72, CIFAR-10 Batch 1:  Loss: 0.284553587436676
Validation Accuracy: 0.674799919128418
Epoch 72, CIFAR-10 Batch 2:  Loss: 0.19996102154254913
Validation Accuracy: 0.6671999096870422
Epoch 72, CIFAR-10 Batch 3:  Loss: 0.14800158143043518
Validation Accuracy: 0.6715999841690063
Epoch 72, CIFAR-10 Batch 4:  Loss: 0.21123360097408295
Validation Accuracy: 0.6719998717308044
Epoch 72, CIFAR-10 Batch 5:  Loss: 0.19139054417610168
Validation Accuracy: 0.6705998778343201
Epoch 73, CIFAR-10 Batch 1:  Loss: 0.28435641527175903
Validation Accuracy: 0.6627998352050781
Epoch 73, CIFAR-10 Batch 2:  Loss: 0.221767857670784
Validation Accuracy: 0.6571998596191406
Epoch 73, CIFAR-10 Batch 3:  Loss: 0.13869063556194305
Validation Accuracy: 0.6761999130249023
Epoch 73, CIFAR-10 Batch 4:  Loss: 0.2218361645936966
Validation Accuracy: 0.6789998412132263
Epoch 73, CIFAR-10 Batch 5:  Loss: 0.20326675474643707
Validation Accuracy: 0.6625999212265015
Epoch 74, CIFAR-10 Batch 1:  Loss: 0.32103562355041504
Validation Accuracy: 0.6603997945785522
Epoch 74, CIFAR-10 Batch 2:  Loss: 0.2218325138092041
Validation Accuracy: 0.669999897480011
Epoch 74, CIFAR-10 Batch 3:  Loss: 0.13809895515441895
Validation Accuracy: 0.6791998744010925
Epoch 74, CIFAR-10 Batch 4:  Loss: 0.21742142736911774
Validation Accuracy: 0.6779999136924744
Epoch 74, CIFAR-10 Batch 5:  Loss: 0.1937088668346405
Validation Accuracy: 0.666999876499176
Epoch 75, CIFAR-10 Batch 1:  Loss: 0.3016940653324127
Validation Accuracy: 0.6709998846054077
Epoch 75, CIFAR-10 Batch 2:  Loss: 0.2074679136276245
Validation Accuracy: 0.6667999625205994
Epoch 75, CIFAR-10 Batch 3:  Loss: 0.1330987811088562
Validation Accuracy: 0.6703999042510986
Epoch 75, CIFAR-10 Batch 4:  Loss: 0.21405759453773499
Validation Accuracy: 0.6709998846054077
Epoch 75, CIFAR-10 Batch 5:  Loss: 0.19126063585281372
Validation Accuracy: 0.6693998575210571
Epoch 76, CIFAR-10 Batch 1:  Loss: 0.28848838806152344
Validation Accuracy: 0.6719998121261597
Epoch 76, CIFAR-10 Batch 2:  Loss: 0.22632180154323578
Validation Accuracy: 0.6603999137878418
Epoch 76, CIFAR-10 Batch 3:  Loss: 0.14441931247711182
Validation Accuracy: 0.6737998723983765
Epoch 76, CIFAR-10 Batch 4:  Loss: 0.22183352708816528
Validation Accuracy: 0.6721998453140259
Epoch 76, CIFAR-10 Batch 5:  Loss: 0.2045401632785797
Validation Accuracy: 0.6691998243331909
Epoch 77, CIFAR-10 Batch 1:  Loss: 0.2679436504840851
Validation Accuracy: 0.6695998311042786
Epoch 77, CIFAR-10 Batch 2:  Loss: 0.18259365856647491
Validation Accuracy: 0.6679998636245728
Epoch 77, CIFAR-10 Batch 3:  Loss: 0.1314261555671692
Validation Accuracy: 0.6763998866081238
Epoch 77, CIFAR-10 Batch 4:  Loss: 0.240557000041008
Validation Accuracy: 0.6745998859405518
Epoch 77, CIFAR-10 Batch 5:  Loss: 0.1709538847208023
Validation Accuracy: 0.671799898147583
Epoch 78, CIFAR-10 Batch 1:  Loss: 0.2760853171348572
Validation Accuracy: 0.6691998839378357
Epoch 78, CIFAR-10 Batch 2:  Loss: 0.25482094287872314
Validation Accuracy: 0.6649999022483826
Epoch 78, CIFAR-10 Batch 3:  Loss: 0.13727450370788574
Validation Accuracy: 0.6743998527526855
Epoch 78, CIFAR-10 Batch 4:  Loss: 0.24047884345054626
Validation Accuracy: 0.6785998940467834
Epoch 78, CIFAR-10 Batch 5:  Loss: 0.17267368733882904
Validation Accuracy: 0.6703999042510986
Epoch 79, CIFAR-10 Batch 1:  Loss: 0.2639995515346527
Validation Accuracy: 0.6651999354362488
Epoch 79, CIFAR-10 Batch 2:  Loss: 0.22027228772640228
Validation Accuracy: 0.6673998832702637
Epoch 79, CIFAR-10 Batch 3:  Loss: 0.1387423574924469
Validation Accuracy: 0.6821998357772827
Epoch 79, CIFAR-10 Batch 4:  Loss: 0.20747984945774078
Validation Accuracy: 0.678399920463562
Epoch 79, CIFAR-10 Batch 5:  Loss: 0.16920414566993713
Validation Accuracy: 0.6717998385429382
Epoch 80, CIFAR-10 Batch 1:  Loss: 0.26470592617988586
Validation Accuracy: 0.6705998778343201
Epoch 80, CIFAR-10 Batch 2:  Loss: 0.22701528668403625
Validation Accuracy: 0.6641998887062073
Epoch 80, CIFAR-10 Batch 3:  Loss: 0.14302952587604523
Validation Accuracy: 0.6817998886108398
Epoch 80, CIFAR-10 Batch 4:  Loss: 0.2327347695827484
Validation Accuracy: 0.6789998412132263
Epoch 80, CIFAR-10 Batch 5:  Loss: 0.1653161495923996
Validation Accuracy: 0.6775999069213867
Epoch 81, CIFAR-10 Batch 1:  Loss: 0.2613367736339569
Validation Accuracy: 0.6697999238967896
Epoch 81, CIFAR-10 Batch 2:  Loss: 0.23083637654781342
Validation Accuracy: 0.674599826335907
Epoch 81, CIFAR-10 Batch 3:  Loss: 0.14487935602664948
Validation Accuracy: 0.6761999130249023
Epoch 81, CIFAR-10 Batch 4:  Loss: 0.22825342416763306
Validation Accuracy: 0.6797998547554016
Epoch 81, CIFAR-10 Batch 5:  Loss: 0.17824842035770416
Validation Accuracy: 0.6709998846054077
Epoch 82, CIFAR-10 Batch 1:  Loss: 0.25048553943634033
Validation Accuracy: 0.6739999055862427
Epoch 82, CIFAR-10 Batch 2:  Loss: 0.25016656517982483
Validation Accuracy: 0.6715998649597168
Epoch 82, CIFAR-10 Batch 3:  Loss: 0.1294407844543457
Validation Accuracy: 0.6759999394416809
Epoch 82, CIFAR-10 Batch 4:  Loss: 0.21693947911262512
Validation Accuracy: 0.6771999001502991
Epoch 82, CIFAR-10 Batch 5:  Loss: 0.19002552330493927
Validation Accuracy: 0.6743999123573303
Epoch 83, CIFAR-10 Batch 1:  Loss: 0.25255438685417175
Validation Accuracy: 0.668799877166748
Epoch 83, CIFAR-10 Batch 2:  Loss: 0.25611957907676697
Validation Accuracy: 0.6623998880386353
Epoch 83, CIFAR-10 Batch 3:  Loss: 0.13255758583545685
Validation Accuracy: 0.6723998188972473
Epoch 83, CIFAR-10 Batch 4:  Loss: 0.22326655685901642
Validation Accuracy: 0.6749998927116394
Epoch 83, CIFAR-10 Batch 5:  Loss: 0.1937580704689026
Validation Accuracy: 0.6675999164581299
Epoch 84, CIFAR-10 Batch 1:  Loss: 0.25574201345443726
Validation Accuracy: 0.6669999361038208
Epoch 84, CIFAR-10 Batch 2:  Loss: 0.23119671642780304
Validation Accuracy: 0.671799898147583
Epoch 84, CIFAR-10 Batch 3:  Loss: 0.14914080500602722
Validation Accuracy: 0.6797998547554016
Epoch 84, CIFAR-10 Batch 4:  Loss: 0.21834909915924072
Validation Accuracy: 0.6751998662948608
Epoch 84, CIFAR-10 Batch 5:  Loss: 0.1855824589729309
Validation Accuracy: 0.6705999374389648
Epoch 85, CIFAR-10 Batch 1:  Loss: 0.2495529055595398
Validation Accuracy: 0.6705998778343201
Epoch 85, CIFAR-10 Batch 2:  Loss: 0.22854547202587128
Validation Accuracy: 0.665199875831604
Epoch 85, CIFAR-10 Batch 3:  Loss: 0.12460757791996002
Validation Accuracy: 0.6741998791694641
Epoch 85, CIFAR-10 Batch 4:  Loss: 0.20964163541793823
Validation Accuracy: 0.6779999136924744
Epoch 85, CIFAR-10 Batch 5:  Loss: 0.17292341589927673
Validation Accuracy: 0.6707998514175415
Epoch 86, CIFAR-10 Batch 1:  Loss: 0.25346821546554565
Validation Accuracy: 0.6735998392105103
Epoch 86, CIFAR-10 Batch 2:  Loss: 0.21385802328586578
Validation Accuracy: 0.6709998846054077
Epoch 86, CIFAR-10 Batch 3:  Loss: 0.1314273327589035
Validation Accuracy: 0.6797997951507568
Epoch 86, CIFAR-10 Batch 4:  Loss: 0.18834124505519867
Validation Accuracy: 0.6821998953819275
Epoch 86, CIFAR-10 Batch 5:  Loss: 0.17082226276397705
Validation Accuracy: 0.6687999367713928
Epoch 87, CIFAR-10 Batch 1:  Loss: 0.2428545206785202
Validation Accuracy: 0.6797997951507568
Epoch 87, CIFAR-10 Batch 2:  Loss: 0.23697468638420105
Validation Accuracy: 0.6779999136924744
Epoch 87, CIFAR-10 Batch 3:  Loss: 0.12194352596998215
Validation Accuracy: 0.6745998859405518
Epoch 87, CIFAR-10 Batch 4:  Loss: 0.1766243577003479
Validation Accuracy: 0.6783998012542725
Epoch 87, CIFAR-10 Batch 5:  Loss: 0.16270311176776886
Validation Accuracy: 0.6739999055862427
Epoch 88, CIFAR-10 Batch 1:  Loss: 0.2433518022298813
Validation Accuracy: 0.6767998933792114
Epoch 88, CIFAR-10 Batch 2:  Loss: 0.23571299016475677
Validation Accuracy: 0.6643998622894287
Epoch 88, CIFAR-10 Batch 3:  Loss: 0.12210287153720856
Validation Accuracy: 0.6767998337745667
Epoch 88, CIFAR-10 Batch 4:  Loss: 0.20213589072227478
Validation Accuracy: 0.6765998601913452
Epoch 88, CIFAR-10 Batch 5:  Loss: 0.159438818693161
Validation Accuracy: 0.6705998182296753
Epoch 89, CIFAR-10 Batch 1:  Loss: 0.2338835597038269
Validation Accuracy: 0.6719998717308044
Epoch 89, CIFAR-10 Batch 2:  Loss: 0.20881518721580505
Validation Accuracy: 0.6697999238967896
Epoch 89, CIFAR-10 Batch 3:  Loss: 0.1287546157836914
Validation Accuracy: 0.673399806022644
Epoch 89, CIFAR-10 Batch 4:  Loss: 0.18869724869728088
Validation Accuracy: 0.6787998676300049
Epoch 89, CIFAR-10 Batch 5:  Loss: 0.16560310125350952
Validation Accuracy: 0.6691999435424805
Epoch 90, CIFAR-10 Batch 1:  Loss: 0.24074576795101166
Validation Accuracy: 0.6673998832702637
Epoch 90, CIFAR-10 Batch 2:  Loss: 0.20704540610313416
Validation Accuracy: 0.6727999448776245
Epoch 90, CIFAR-10 Batch 3:  Loss: 0.12490062415599823
Validation Accuracy: 0.6845998167991638
Epoch 90, CIFAR-10 Batch 4:  Loss: 0.18117626011371613
Validation Accuracy: 0.672999918460846
Epoch 90, CIFAR-10 Batch 5:  Loss: 0.1505563110113144
Validation Accuracy: 0.6781998872756958
Epoch 91, CIFAR-10 Batch 1:  Loss: 0.25553444027900696
Validation Accuracy: 0.6691998839378357
Epoch 91, CIFAR-10 Batch 2:  Loss: 0.19858744740486145
Validation Accuracy: 0.6673998832702637
Epoch 91, CIFAR-10 Batch 3:  Loss: 0.1127004474401474
Validation Accuracy: 0.6749999523162842
Epoch 91, CIFAR-10 Batch 4:  Loss: 0.17541463673114777
Validation Accuracy: 0.6849998235702515
Epoch 91, CIFAR-10 Batch 5:  Loss: 0.15558406710624695
Validation Accuracy: 0.6723998785018921
Epoch 92, CIFAR-10 Batch 1:  Loss: 0.22733935713768005
Validation Accuracy: 0.6749998927116394
Epoch 92, CIFAR-10 Batch 2:  Loss: 0.21483199298381805
Validation Accuracy: 0.6597998738288879
Epoch 92, CIFAR-10 Batch 3:  Loss: 0.12437789887189865
Validation Accuracy: 0.6729997992515564
Epoch 92, CIFAR-10 Batch 4:  Loss: 0.20251315832138062
Validation Accuracy: 0.6751998662948608
Epoch 92, CIFAR-10 Batch 5:  Loss: 0.1676541417837143
Validation Accuracy: 0.6717998385429382
Epoch 93, CIFAR-10 Batch 1:  Loss: 0.21977156400680542
Validation Accuracy: 0.6727998852729797
Epoch 93, CIFAR-10 Batch 2:  Loss: 0.20385566353797913
Validation Accuracy: 0.6665998697280884
Epoch 93, CIFAR-10 Batch 3:  Loss: 0.13768760859966278
Validation Accuracy: 0.6741998791694641
Epoch 93, CIFAR-10 Batch 4:  Loss: 0.17293351888656616
Validation Accuracy: 0.6821998357772827
Epoch 93, CIFAR-10 Batch 5:  Loss: 0.15944495797157288
Validation Accuracy: 0.671799898147583
Epoch 94, CIFAR-10 Batch 1:  Loss: 0.23111872375011444
Validation Accuracy: 0.6745998859405518
Epoch 94, CIFAR-10 Batch 2:  Loss: 0.19350118935108185
Validation Accuracy: 0.6697998642921448
Epoch 94, CIFAR-10 Batch 3:  Loss: 0.13295923173427582
Validation Accuracy: 0.6669999361038208
Epoch 94, CIFAR-10 Batch 4:  Loss: 0.1845422238111496
Validation Accuracy: 0.678399920463562
Epoch 94, CIFAR-10 Batch 5:  Loss: 0.14043214917182922
Validation Accuracy: 0.6739998459815979
Epoch 95, CIFAR-10 Batch 1:  Loss: 0.19993340969085693
Validation Accuracy: 0.6749998927116394
Epoch 95, CIFAR-10 Batch 2:  Loss: 0.18956080079078674
Validation Accuracy: 0.675399899482727
Epoch 95, CIFAR-10 Batch 3:  Loss: 0.12863197922706604
Validation Accuracy: 0.6795998811721802
Epoch 95, CIFAR-10 Batch 4:  Loss: 0.17314055562019348
Validation Accuracy: 0.6763999462127686
Epoch 95, CIFAR-10 Batch 5:  Loss: 0.1426345556974411
Validation Accuracy: 0.6773998737335205
Epoch 96, CIFAR-10 Batch 1:  Loss: 0.21913793683052063
Validation Accuracy: 0.6709998846054077
Epoch 96, CIFAR-10 Batch 2:  Loss: 0.19099614024162292
Validation Accuracy: 0.6757998466491699
Epoch 96, CIFAR-10 Batch 3:  Loss: 0.12060028314590454
Validation Accuracy: 0.6767998933792114
Epoch 96, CIFAR-10 Batch 4:  Loss: 0.17891231179237366
Validation Accuracy: 0.6817998886108398
Epoch 96, CIFAR-10 Batch 5:  Loss: 0.14112752676010132
Validation Accuracy: 0.6697998642921448
Epoch 97, CIFAR-10 Batch 1:  Loss: 0.21975237131118774
Validation Accuracy: 0.668799877166748
Epoch 97, CIFAR-10 Batch 2:  Loss: 0.18662655353546143
Validation Accuracy: 0.6671998500823975
Epoch 97, CIFAR-10 Batch 3:  Loss: 0.1169939860701561
Validation Accuracy: 0.6739999651908875
Epoch 97, CIFAR-10 Batch 4:  Loss: 0.15079736709594727
Validation Accuracy: 0.6883999109268188
Epoch 97, CIFAR-10 Batch 5:  Loss: 0.1583212912082672
Validation Accuracy: 0.6767998933792114
Epoch 98, CIFAR-10 Batch 1:  Loss: 0.21347761154174805
Validation Accuracy: 0.6739999055862427
Epoch 98, CIFAR-10 Batch 2:  Loss: 0.2073219120502472
Validation Accuracy: 0.6663998365402222
Epoch 98, CIFAR-10 Batch 3:  Loss: 0.12760764360427856
Validation Accuracy: 0.6737998723983765
Epoch 98, CIFAR-10 Batch 4:  Loss: 0.17566341161727905
Validation Accuracy: 0.6795998811721802
Epoch 98, CIFAR-10 Batch 5:  Loss: 0.14664757251739502
Validation Accuracy: 0.6745998859405518
Epoch 99, CIFAR-10 Batch 1:  Loss: 0.2003284990787506
Validation Accuracy: 0.6657999157905579
Epoch 99, CIFAR-10 Batch 2:  Loss: 0.18858759105205536
Validation Accuracy: 0.674799919128418
Epoch 99, CIFAR-10 Batch 3:  Loss: 0.13152146339416504
Validation Accuracy: 0.6755998730659485
Epoch 99, CIFAR-10 Batch 4:  Loss: 0.1574142575263977
Validation Accuracy: 0.6801998615264893
Epoch 99, CIFAR-10 Batch 5:  Loss: 0.15182048082351685
Validation Accuracy: 0.6683998107910156
Epoch 100, CIFAR-10 Batch 1:  Loss: 0.22650930285453796
Validation Accuracy: 0.668799877166748
Epoch 100, CIFAR-10 Batch 2:  Loss: 0.19564953446388245
Validation Accuracy: 0.6701999306678772
Epoch 100, CIFAR-10 Batch 3:  Loss: 0.12198585271835327
Validation Accuracy: 0.6747998595237732
Epoch 100, CIFAR-10 Batch 4:  Loss: 0.14250633120536804
Validation Accuracy: 0.6819998025894165
Epoch 100, CIFAR-10 Batch 5:  Loss: 0.1378372311592102
Validation Accuracy: 0.6749998331069946

In [39]:

    

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import tensorflow as tf
import pickle
import helper
import random

# Set batch size if not already set
try:
    if batch_size:
        pass
except NameError:
    batch_size = 64

save_model_path = './image_classification'
n_samples = 4
top_n_predictions = 3

def test_model():
    """
    Test the saved model against the test dataset
    """

    test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))
    loaded_graph = tf.Graph()

    with tf.Session(graph=loaded_graph) as sess:
        # Load model
        loader = tf.train.import_meta_graph(save_model_path + '.meta')
        loader.restore(sess, save_model_path)

        # Get Tensors from loaded model
        loaded_x = loaded_graph.get_tensor_by_name('x:0')
        loaded_y = loaded_graph.get_tensor_by_name('y:0')
        loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
        loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
        loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')
        
        # Get accuracy in batches for memory limitations
        test_batch_acc_total = 0
        test_batch_count = 0
        
        for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):
            test_batch_acc_total += sess.run(
                loaded_acc,
                feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})
            test_batch_count += 1

        print('Testing Accuracy: {}\n'.format(test_batch_acc_total/test_batch_count))

        # Print Random Samples
        random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))
        random_test_predictions = sess.run(
            tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),
            feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})
        helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)


test_model()


    

    




Testing Accuracy: 0.668359375