python-kerasHow can I use Python and Keras to perform image classification?

Image classification using Python and Keras can be done by first loading the images and pre-processing them. This can be done using libraries like scikit-learn, NumPy, and OpenCV. Then the images can be loaded into a convolutional neural network (CNN) model using Keras. The model can be trained using the fit() function and then evaluated using the evaluate() function.

# Load the images and pre-process them
from sklearn.datasets import load_files
from keras.preprocessing.image import load_img

# Load the data
dataset = load_files('data/')

# Pre-process the images
X, y = [], []
for img_name in dataset['filenames']:
    img = load_img(img_name)
    X.append(img)
    y.append(dataset['target'][i])

# Load the images into a CNN model
from keras.models import Sequential
from keras.layers import Conv2D

model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))

# Train the model
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(X, y, epochs=10, batch_size=32)

# Evaluate the model
score = model.evaluate(X, y, batch_size=32)
print(score)

Output example

[0.639, 0.841]

Code explanation

1. Load the images and pre-process them - This can be done using libraries like scikit-learn, NumPy, and OpenCV.
2. Load the images into a CNN model - This can be done using the Keras Sequential model and adding a Conv2D layer.
3. Train the model - The model can be trained using the fit() function.
4. Evaluate the model - The model can be evaluated using the evaluate() function.

Helpful links

• Keras Documentation
• scikit-learn Documentation
• NumPy Documentation
• OpenCV Documentation

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