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TensorFlow Image Classification : All you need to know about Building Classifiers

Last updated on Apr 18,2023 7.5K Views

Kislay Keshari
Kurt is a Big Data and Data Science Expert, working as a... Kurt is a Big Data and Data Science Expert, working as a Research Analyst at Edureka. He is keen to work with Machine Learning,...
6 / 7 Blog from Tensorflow

Image Classification a task which even a baby can do in seconds, but for a machine, it has been a tough task until the recent advancements in Artificial Intelligence and Deep Learning. Self-driving cars can detect objects and take required action in real-time and most of this is possible because of TensorFlow Image Classification. In this article, I’ll guide you through the following topics:


What is TensorFlow?

TensorFlow is Google’s Open Source Machine Learning Framework for dataflow programming across a range of tasks. Nodes in the graph represent mathematical operations, while the graph edges represent the multi-dimensional data arrays communicated between them.

Tensors are just multidimensional arrays, an extension of 2-dimensional tables to data with a higher dimension. There are many features of Tensorflow which makes it appropriate for Deep Learning and it’s core open source library helps you develop and train ML models.


What is Image Classification?

The intent of Image Classification is to categorize all pixels in a digital image into one of several land cover classes or themes.  This categorized data may then be used to produce thematic maps of the land cover present in an image.


Now Depending on the interaction between the analyst and the computer during classification, there are two types of classification:

  • Supervised &
  • Unsupervised

So, without wasting any time let’s jump into TensorFlow Image Classification. I have 2 examples: easy and difficult. Let’s proceed with the easy one.


TensorFlow Image Classification: Fashion MNIST


Fashion MNIST Dataset

Here we are going to use Fashion MNIST Dataset, which contains 70,000 grayscale images in 10 categories. We will use 60000 for training and the rest 10000 for testing purposes. You can access the Fashion MNIST directly from TensorFlow, just import and load the data.

  • Let’s import the libraries first
from __future__ import absolute_import, division, print_function
# TensorFlow and tf.keras

import tensorflow as tf
from tensorflow import keras

# Helper libraries

import numpy as np
import matplotlib.pyplot as plt


  • Let’s load the data
fashion_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()


  • Next, we are going to map the images into classes
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat','Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']


  • Exploring the data
#Each Label is between 0-9


  • Now, it’s time to pre-process the data.
#If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255.




  • We have to scale the images from 0-1 to feed it into the Neural Network
train_images = train_images / 255.0

test_images = test_images / 255.0


  • Let’s display some images.
for i in range(25):




  • Setup the layers
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation=tf.nn.relu),
    keras.layers.Dense(10, activation=tf.nn.softmax)



  • Compile the Model



  • Model Training, train_labels, epochs=10)





  • Evaluating Accuracy
test_loss, test_acc = model.evaluate(test_images, test_labels)

print('Test accuracy:', test_acc)



  • Making Predictions
predictions = model.predict(test_images)




A prediction is an array of 10 numbers. These describe the “confidence” of the model that the image corresponds to each of the 10 different articles of clothing. We can see which label has the highest confidence value.

#Model is most confident that it's an ankle boot. Let's see if it's correct

Output: 9



Output: 9


  • Now, it’s time to look at the full set of 10 channels
def plot_image(i, predictions_array, true_label, img):
  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]


  predicted_label = np.argmax(predictions_array)
  if predicted_label == true_label:
    color = 'green'
    color = 'red'

  plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],

def plot_value_array(i, predictions_array, true_label):
  predictions_array, true_label = predictions_array[i], true_label[i]
  thisplot =, predictions_array, color="#777777")
  plt.ylim([0, 1])
  predicted_label = np.argmax(predictions_array)



  • Let’s look at the 0th and 10th image first
i = 0
plot_image(i, predictions, test_labels, test_images)
plot_value_array(i, predictions,  test_labels)



i = 10
plot_image(i, predictions, test_labels, test_images)
plot_value_array(i, predictions,  test_labels)



  • Now, let’s plot several images and their predictions. Correct ones are green, while the incorrect ones are red.
num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
  plt.subplot(num_rows, 2*num_cols, 2*i+1)
  plot_image(i, predictions, test_labels, test_images)
  plt.subplot(num_rows, 2*num_cols, 2*i+2)
  plot_value_array(i, predictions, test_labels)




  • Finally, we will use the trained model to make a prediction about a single image.
# Grab an image from the test dataset
img = test_images[0]

# Add the image to a batch where it's the only member.
img = (np.expand_dims(img,0))

predictions_single = model.predict(img) 




plot_value_array(0, predictions_single, test_labels)
plt.xticks(range(10), class_names, rotation=45)



  • As you can see the prediction for our only image in batch.
prediction_result = np.argmax(predictions_single[0])

Output: 9




The CIFAR-10 dataset consists of airplanes, dogs, cats, and other objects. You’ll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded. This use-case will surely clear your doubts about TensorFlow Image Classification.

  • Downloading the Data
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm 
import tarfile

cifar10_dataset_folder_path = 'cifar-10-batches-py'

class DownloadProgress(tqdm):
    last_block = 0

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

    check if the data (zip) file is already downloaded
    if not, download it from "" and save as cifar-10-python.tar.gz
if not isfile('cifar-10-python.tar.gz'):
    with DownloadProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:

if not isdir(cifar10_dataset_folder_path):
    with'cifar-10-python.tar.gz') as tar:


  • Importing Necessary Libraries
import pickle
import numpy as np
import matplotlib.pyplot as plt


  • Understanding the Data

The original batch of Data is 10000×3072 tensor expressed in a numpy array, where 10000 is the number of sample data. The image is colored and of size 32×32. Feeding can be done either in a format of (width x height x num_channel) or (num_channel x width x height). Let’s define the labels.

def load_label_names():
    return ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']


  • Reshaping the Data

We are going to reshape the data in two stages

Firstly, divide the row vector (3072) into 3 pieces. Each piece corresponds to each channel. This results in (3 x 1024) dimension of a tensor. Then Divide the resulting tensor from the previous step with 32.  32 here means the width of an image. This results in (3x32x32).

Secondly, we have to transpose the data from (num_channel, width, height) to (width, height, num_channel).  For that, we are going to use the transpose function.

Reshape and Transpose


def load_cfar10_batch(cifar10_dataset_folder_path, batch_id):
    with open(cifar10_dataset_folder_path + '/data_batch_' + str(batch_id), mode='rb') as file:
        # note the encoding type is 'latin1'
        batch = pickle.load(file, encoding='latin1')
    features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)
    labels = batch['labels']
    return features, label


  • Exploring the Data
def display_stats(cifar10_dataset_folder_path, batch_id, sample_id):
    features, labels = load_cfar10_batch(cifar10_dataset_folder_path, batch_id)
    if not (0 <= sample_id < len(features)):
        print('{} samples in batch {}.  {} is out of range.'.format(len(features), batch_id, sample_id))
        return None

Stats of batch #{}:'.format(batch_id))
    print('# of Samples: {}
    label_names = load_label_names()
    label_counts = dict(zip(*np.unique(labels, return_counts=True)))
    for key, value in label_counts.items():
        print('Label Counts of [{}]({}) : {}'.format(key, label_names[key].upper(), value))
    sample_image = features[sample_id]
    sample_label = labels[sample_id]
Example of Image {}:'.format(sample_id))
    print('Image - Min Value: {} Max Value: {}'.format(sample_image.min(), sample_image.max()))
    print('Image - Shape: {}'.format(sample_image.shape))
    print('Label - Label Id: {} Name: {}'.format(sample_label, label_names[sample_label]))


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

import numpy as np

# Explore the dataset
batch_id = 3
sample_id = 7000
display_stats(cifar10_dataset_folder_path, batch_id, sample_id)




  • Implementing Preprocessing Functions

We are going to Normalize the data via Min-Max Normalization. This simply makes all x values to range between 0 and 1.
y = (x-min) / (max-min)

def normalize(x):
            - x: input image data in numpy array [32, 32, 3]
            - normalized x 
    min_val = np.min(x)
    max_val = np.max(x)
    x = (x-min_val) / (max_val-min_val)
    return x


  • One-Hot Encode
def one_hot_encode(x):
            - x: a list of labels
            - one hot encoding matrix (number of labels, number of class)
    encoded = np.zeros((len(x), 10))
    for idx, val in enumerate(x):
        encoded[idx][val] = 1
    return encoded


  • Preprocess and Save the Data
def _preprocess_and_save(normalize, one_hot_encode, features, labels, filename):
    features = normalize(features)
    labels = one_hot_encode(labels)

    pickle.dump((features, labels), open(filename, 'wb'))

def preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode):
    n_batches = 5
    valid_features = []
    valid_labels = []

    for batch_i in range(1, n_batches + 1):
        features, labels = load_cfar10_batch(cifar10_dataset_folder_path, batch_i)
        # find index to be the point as validation data in the whole dataset of the batch (10%)
        index_of_validation = int(len(features) * 0.1)

        # preprocess the 90% of the whole dataset of the batch
        # - normalize the features
        # - one_hot_encode the lables
        # - save in a new file named, "preprocess_batch_" + batch_number
        # - each file for each batch
        _preprocess_and_save(normalize, one_hot_encode,
                             features[:-index_of_validation], labels[:-index_of_validation], 
                             'preprocess_batch_' + str(batch_i) + '.p')

        # unlike the training dataset, validation dataset will be added through all batch dataset
        # - take 10% of the whold dataset of the batch
        # - add them into a list of
        #   - valid_features
        #   - valid_labels

    # preprocess the all stacked validation dataset
    _preprocess_and_save(normalize, one_hot_encode,
                         np.array(valid_features), np.array(valid_labels),

    # load the test dataset
    with open(cifar10_dataset_folder_path + '/test_batch', mode='rb') as file:
        batch = pickle.load(file, encoding='latin1')

    # preprocess the testing data
    test_features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)
    test_labels = batch['labels']

    # Preprocess and Save all testing data
    _preprocess_and_save(normalize, one_hot_encode,
                         np.array(test_features), np.array(test_labels),


preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)


  • Checkpoint
import pickle

valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))


  • Building the Network

The entire model consists of 14 layers in total.


import tensorflow as tf

def conv_net(x, keep_prob):
    conv1_filter = tf.Variable(tf.truncated_normal(shape=[3, 3, 3, 64], mean=0, stddev=0.08))
    conv2_filter = tf.Variable(tf.truncated_normal(shape=[3, 3, 64, 128], mean=0, stddev=0.08))
    conv3_filter = tf.Variable(tf.truncated_normal(shape=[5, 5, 128, 256], mean=0, stddev=0.08))
    conv4_filter = tf.Variable(tf.truncated_normal(shape=[5, 5, 256, 512], mean=0, stddev=0.08))

    # 1, 2
    conv1 = tf.nn.conv2d(x, conv1_filter, strides=[1,1,1,1], padding='SAME')
    conv1 = tf.nn.relu(conv1)
    conv1_pool = tf.nn.max_pool(conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
    conv1_bn = tf.layers.batch_normalization(conv1_pool)

    # 3, 4
    conv2 = tf.nn.conv2d(conv1_bn, conv2_filter, strides=[1,1,1,1], padding='SAME')
    conv2 = tf.nn.relu(conv2)
    conv2_pool = tf.nn.max_pool(conv2, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')    
    conv2_bn = tf.layers.batch_normalization(conv2_pool)
    # 5, 6
    conv3 = tf.nn.conv2d(conv2_bn, conv3_filter, strides=[1,1,1,1], padding='SAME')
    conv3 = tf.nn.relu(conv3)
    conv3_pool = tf.nn.max_pool(conv3, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')  
    conv3_bn = tf.layers.batch_normalization(conv3_pool)
    # 7, 8
    conv4 = tf.nn.conv2d(conv3_bn, conv4_filter, strides=[1,1,1,1], padding='SAME')
    conv4 = tf.nn.relu(conv4)
    conv4_pool = tf.nn.max_pool(conv4, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
    conv4_bn = tf.layers.batch_normalization(conv4_pool)
    # 9
    flat = tf.contrib.layers.flatten(conv4_bn)  

    # 10
    full1 = tf.contrib.layers.fully_connected(inputs=flat, num_outputs=128, activation_fn=tf.nn.relu)
    full1 = tf.nn.dropout(full1, keep_prob)
    full1 = tf.layers.batch_normalization(full1)
    # 11
    full2 = tf.contrib.layers.fully_connected(inputs=full1, num_outputs=256, activation_fn=tf.nn.relu)
    full2 = tf.nn.dropout(full2, keep_prob)
    full2 = tf.layers.batch_normalization(full2)
    # 12
    full3 = tf.contrib.layers.fully_connected(inputs=full2, num_outputs=512, activation_fn=tf.nn.relu)
    full3 = tf.nn.dropout(full3, keep_prob)
    full3 = tf.layers.batch_normalization(full3)    
    # 13
    full4 = tf.contrib.layers.fully_connected(inputs=full3, num_outputs=1024, activation_fn=tf.nn.relu)
    full4 = tf.nn.dropout(full4, keep_prob)
    full4 = tf.layers.batch_normalization(full4)        
    # 14
    out = tf.contrib.layers.fully_connected(inputs=full3, num_outputs=10, activation_fn=None)
    return out


  • Hyperparameters
epochs = 10
batch_size = 128
keep_probability = 0.7
learning_rate = 0.001


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

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


  • Train the Neural Network
#Single Optimization
train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):, feed_dict={ x: feature_batch, y: label_batch, keep_prob: keep_probability })


#Showing Stats
def print_stats(session, feature_batch, label_batch, cost, accuracy):
    loss =, 
                        x: feature_batch,
                        y: label_batch,
                        keep_prob: 1.
    valid_acc =, 
                             x: valid_features,
                             y: valid_labels,
                             keep_prob: 1.
    print('Loss: {:>10.4f} Validation Accuracy: {:.6f}'.format(loss, valid_acc))



  • Fully Training and Saving the Model
def batch_features_labels(features, labels, batch_size):
    Split features and labels into batches
    for start in range(0, len(features), batch_size):
        end = min(start + batch_size, len(features))
        yield features[start:end], labels[start:end]

def load_preprocess_training_batch(batch_id, batch_size):
    Load the Preprocessed Training data and return them in batches of <batch_size> or less
    filename = 'preprocess_batch_' + str(batch_id) + '.p'
    features, labels = pickle.load(open(filename, mode='rb'))

    # Return the training data in batches of size <batch_size> or less
    return batch_features_labels(features, labels, batch_size)




#Saving Model and Path
= './image_classification' print('Training...') with tf.Session() as sess: # Initializing the variables # 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 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 =, save_model_path)



Now, the important part of Tensorflow Image Classification is done. Now, it’s time to test the model.


  • Testing the Model
import pickle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelBinarizer

def batch_features_labels(features, labels, batch_size):
    Split features and labels into batches
    for start in range(0, len(features), batch_size):
        end = min(start + batch_size, len(features))
        yield features[start:end], labels[start:end]

def display_image_predictions(features, labels, predictions, top_n_predictions):
    n_classes = 10
    label_names = load_label_names()
    label_binarizer = LabelBinarizer()
    label_ids = label_binarizer.inverse_transform(np.array(labels))

    fig, axies = plt.subplots(nrows=top_n_predictions, ncols=2, figsize=(20, 10))
    fig.suptitle('Softmax Predictions', fontsize=20, y=1.1)

    n_predictions = 3
    margin = 0.05
    ind = np.arange(n_predictions)
    width = (1. - 2. * margin) / n_predictions
    for image_i, (feature, label_id, pred_indicies, pred_values) in enumerate(zip(features, label_ids, predictions.indices, predictions.values)):
        if (image_i < top_n_predictions):
            pred_names = [label_names[pred_i] for pred_i in pred_indicies]
            correct_name = label_names[label_id]
            axies[image_i][0].imshow((feature*255).astype(np.int32, copy=False))

            axies[image_i][1].barh(ind + margin, pred_values[:3], width)
            axies[image_i][1].set_yticks(ind + margin)
            axies[image_i][1].set_xticks([0, 0.5, 1.0])


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

import tensorflow as tf
import pickle
import random

save_model_path = './image_classification'
batch_size = 64
n_samples = 10
top_n_predictions = 5

def test_model():
    test_features, test_labels = pickle.load(open('preprocess_training.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('input_x:0')
        loaded_y = loaded_graph.get_tensor_by_name('output_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 train_feature_batch, train_label_batch in batch_features_labels(test_features, test_labels, batch_size):
            test_batch_acc_total +=
                feed_dict={loaded_x: train_feature_batch, loaded_y: train_label_batch, loaded_keep_prob: 1.0})
            test_batch_count += 1

        print('Testing Accuracy: {}

        # Print Random Samples
        random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))
        random_test_predictions =
            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})
        display_image_predictions(random_test_features, random_test_labels, random_test_predictions, top_n_predictions)



Output: Testing Accuracy: 0.5882762738853503



Now, if you train your neural network for more epochs or change the activation function, you might get a different result that might have better accuracy.

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