Let's train a K-Means model to cluster the MNIST handwritten digits to 10 clusters.

DeepClustering_1.py

from sklearn.cluster import KMeans
from keras.datasets import mnist
import numpy as np

def accu(y_true, y_pred):
    """
    Calculate clustering accuracy. Require scikit-learn installed
    # Arguments
        y: true labels, numpy.array with shape `(n_samples,)`
        y_pred: predicted labels, numpy.array with shape `(n_samples,)`
    # Return
        accuracy, in [0,1]
    """
    y_true = y_true.astype(np.int64)
    assert y_pred.size == y_true.size
    D = max(y_pred.max(), y_true.max()) + 1
    w = np.zeros((D, D), dtype=np.int64)
    for i in range(y_pred.size):
        w[y_pred[i], y_true[i]] += 1
    from sklearn.utils.linear_assignment_ import linear_assignment
    ind = linear_assignment(w.max() - w)
    return sum([w[i, j] for i, j in ind]) * 1.0 / y_pred.size


(x_train, y_train), (x_test, y_test) = mnist.load_data()
x = np.concatenate((x_train, x_test))
y = np.concatenate((y_train, y_test))
x = x.reshape((x.shape[0], -1))
x = np.divide(x, 255.)
# 10 clusters
n_clusters = len(np.unique(y))
# Runs in parallel 4 CPUs
kmeans = KMeans(n_clusters=n_clusters, n_init=20, n_jobs=4)
# Train K-Means.
y_pred_kmeans = kmeans.fit_predict(x)
# Evaluate the K-Means clustering accuracy.
acc = accu(y,y_pred_kmeans)