How to build a simple CNN based Image classifier using Keras
Milind Soorya / June 01, 2022
5 min read
- 1 Introduction
- 2 The Code
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Convolutional neural network (CNN), are a class of artificial neural networks that has become dominant in various computer vision tasks, it is attracting interest across a variety of domains.
A convolutional neural network is composed of multiple building blocks, such as convolution layers, pooling layers, and fully connected layers, and is designed to automatically and adaptively learn spatial hierarchies of features through a back propagation algorithm.
CNN work well on computer vision tasks like image classification, object detection, image recognition, etc. Other Neural networks used for similar tasks includes recurrent neural networks (RNN), long short term memory (LSTM), artificial neural networks (ANN), etc.,
In this article, I will be trying to solve the HP Unlocked challenge. It is challenge number four.
You can get the data from the above website, or you could also fork the files from my GitHub repository - Unlocked_Challenge_4 and start working on it directly.
This is a binary classification task. The challenge is to build a machine learning model to classify images of "La Eterna", a kind of flower.
I will use a CNN model to get a baseline score. On initial analysis, the dataset is quite small for a deep learning task. I will perform some image augmentations to increase the dataset.
I will also explore hyperparameter tuning and transfer learning using VGG19 in the upcoming articles.
I used a local Jupyter lab instance for running the code. The final code can be found here.
import pandas as pd import numpy as np import os import cv2 import matplotlib.pyplot as plt import warnings
import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers from tensorflow.keras.preprocessing.image import ImageDataGenerator import keras_tuner as kt
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
# Set the seed value for experiment reproducibility. seed = 1842 tf.random.set_seed(seed) np.random.seed(seed) # Turn off warnings for cleaner looking notebook warnings.simplefilter('ignore')
#define image dataset # Data Augmentation image_generator = ImageDataGenerator( rescale=1/255, rotation_range=10, # rotation width_shift_range=0.2, # horizontal shift height_shift_range=0.2, # vertical shift zoom_range=0.2, # zoom horizontal_flip=True, # horizontal flip brightness_range=[0.2,1.2],# brightness validation_split=0.2,) #Train & Validation Split train_dataset = image_generator.flow_from_directory(batch_size=32, directory='data_cleaned/Train', shuffle=True, target_size=(224, 224), subset="training", class_mode='categorical') validation_dataset = image_generator.flow_from_directory(batch_size=32, directory='data_cleaned/Train', shuffle=True, target_size=(224, 224), subset="validation", class_mode='categorical') #Organize data for our predictions image_generator_submission = ImageDataGenerator(rescale=1/255) submission = image_generator_submission.flow_from_directory( directory='data_cleaned/scraped_images', shuffle=False, target_size=(224, 224), class_mode=None)
Don't worry about how the network is created. We can use hyperparameter tuning to better tune the layers and get a stable network. I will discuss it in the next article. If you would like to learn more about it, check out Keras documentation.
Make sure you don't mess up the input and output shapes. Here the input shape is (224, 224, 3). Meaning the height, width and the channels of the image are respectively 224, 224 and 3. (3 is the red, green and blue channels of a colour image).
model = keras.models.Sequential([ keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape = [224, 224,3]), keras.layers.MaxPooling2D(), keras.layers.Conv2D(64, (2, 2), activation='relu'), keras.layers.MaxPooling2D(), keras.layers.Conv2D(64, (2, 2), activation='relu'), keras.layers.Flatten(), keras.layers.Dense(100, activation='relu'), keras.layers.Dense(2, activation ='softmax') ])
Now we can compile the prepared model. Note that we are also using a call back to stop the training early. In this case, the callback will be triggered if the validation loss is the same or increasing for more than 3 epochs.
model.compile(optimizer='adam', loss = 'binary_crossentropy', metrics=['accuracy']) callback = keras.callbacks.EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
model.fit(train_dataset, epochs=20, validation_data=validation_dataset, callbacks=callback)
loss, accuracy = model.evaluate(validation_dataset) print("Loss: ", loss) print("Accuracy: ", accuracy)
model = keras.models.load_model('cnn-model')
onlyfiles = [f.split('.') for f in os.listdir(os.path.join('data_cleaned/scraped_images/image_files')) if os.path.isfile(os.path.join(os.path.join('data_cleaned/scraped_images/image_files'), f))] submission_df = pd.DataFrame(onlyfiles, columns =['images']) submission_df[['la_eterna', 'other_flower']] = model.predict(submission) submission_df.head()
submission_df.to_csv('submission_file.csv', index = False)
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