DeepClustering_4.py

model.compile(optimizer=SGD(0.01, 0.9), loss='kld')
#%%
kmeans = KMeans(n_clusters=n_clusters, n_init=20)
y_pred = kmeans.fit_predict(encoder.predict(x))
#%%
y_pred_last = np.copy(y_pred)
model.get_layer(name='clustering').set_weights([kmeans.cluster_centers_])
#%%
# computing an auxiliary target distribution
def target_distribution(q):
    weight = q ** 2 / q.sum(0)
    return (weight.T / weight.sum(1)).T
loss = 0
index = 0
maxiter = 8000
update_interval = 140
index_array = np.arange(x.shape[0])
tol = 0.001 # tolerance threshold to stop training
#%%
for ite in range(int(maxiter)):
    if ite % update_interval == 0:
        q = model.predict(x, verbose=0)
        p = target_distribution(q)  # update the auxiliary target distribution p

        # evaluate the clustering performance
        y_pred = q.argmax(1)
        if y is not None:
            acc = np.round(accu(y, y_pred), 5)
            nmi = np.round(nmis(y, y_pred), 5)
            ari = np.round(aris(y, y_pred), 5)
            loss = np.round(loss, 5)
            print('Iter %d: acc = %.5f, nmi = %.5f, ari = %.5f' % (ite, acc, nmi, ari), ' ; loss=', loss)

        # check stop criterion - model convergence
        delta_label = np.sum(y_pred != y_pred_last).astype(np.float32) / y_pred.shape[0]
        y_pred_last = np.copy(y_pred)
        if ite > 0 and delta_label < tol:
            print('delta_label ', delta_label, '< tol ', tol)
            print('Reached tolerance threshold. Stopping training.')
            break
    idx = index_array[index * batch_size: min((index+1) * batch_size, x.shape[0])]
    loss = model.train_on_batch(x=x[idx], y=p[idx])
    index = index + 1 if (index + 1) * batch_size <= x.shape[0] else 0

model.save_weights(save_dir + '/DEC_model_final.h5')

model.load_weights(save_dir + '/DEC_model_final.h5')