py19-loaddataset.py

import matplotlib.pyplot as plt
import numpy as np
import cv2
import os
import nnfs
nnfs.init()

# Loads a MNIST dataset
def load_mnist_dataset(dataset, path):
    
    # Scan all the directories and create a list of labels
    labels = os.listdir(os.path.join(path, dataset))
    
    # Create lists for samples and labels
    X = []
    y = []
    
    # For each label folder
    for label in labels:
        # And for each image in given folder
        for file in os.listdir(os.path.join(path, dataset, label)):
            # Read the image
            image = cv2.imread(os.path.join(path, dataset, label, file), cv2.IMREAD_UNCHANGED)
            
            # And append it and a label to the lists
            X.append(image)
            y.append(label)
    
    # Convert the data to proper numpy arrays and return
    return np.array(X), np.array(y).astype('uint8')

# MNIST dataset (train + test)
def create_data_mnist(path):
    
    # Load both sets separately
    X, y = load_mnist_dataset('train', path)
    X_test, y_test = load_mnist_dataset('test', path)
    
    # And return all the data
    return X, y, X_test, y_test

# Create dataset
X, y, X_test, y_test = create_data_mnist('fashion_mnist_images')

# Scale features
X = (X.astype(np.float32) - 127.5) / 127.5
X_test = (X_test.astype(np.float32) - 127.5) / 127.5

print(X.min(), X.max())
print(X.shape)

# Reshape to vectors
X = X.reshape(X.shape[0], -1)
X_test = X_test.reshape(X_test.shape[0], -1)

# Randomize order
keys = np.array(range(X.shape[0]))
print(keys[:10])

np.random.shuffle(keys)
print(keys[:10])

# Apply randomized order
X = X[keys]
y = y[keys]

plt.imshow((X[8].reshape(28, 28)))
plt.show()

print(y[8])