RBMs_and_DBNs/run.py at master · Hackerman342/RBMs_and_DBNs · GitHub

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from util import *
from rbm import RestrictedBoltzmannMachine
from dbn import DeepBeliefNet
# for removing files automatically
import os
import glob

if __name__ == "__main__":
    # Automatically remove files so network trains
    files_remove = False
    if files_remove:
        print('Removing training files')
        # Do not remove trained_rbm files
        #files = glob.glob('C:/Users/kwc57/Github_repos/RBMs_and_DBNs/trained_rbm/*')
        #for f in files:
             #os.remove(f)
        files = glob.glob('C:/Users/kwc57/Github_repos/RBMs_and_DBNs/trained_dbn/*')
        for f in files:
             os.remove(f)

    print('Reading mnist train and test data ')
    image_size = [28,28]
    train_imgs,train_lbls,test_imgs,test_lbls = read_mnist(dim=image_size, n_train=60000, n_test=10000)

    ''' restricted boltzmann machine '''
    '''
    print ("\nStarting a Restricted Boltzmann Machine..")
    rbm = RestrictedBoltzmannMachine(ndim_visible=image_size[0]*image_size[1],
                                     #ndim_hidden=500,
                                     ndim_hidden=200,
                                     is_bottom=True,
                                     image_size=image_size,
                                     is_top=False,
                                     n_labels=10,
                                     batch_size=20
    )
    rbm.rf["period"] = 1
    err1 = rbm.cd1(visible_trainset=train_imgs, n_iterations=20)

    rbm2 = RestrictedBoltzmannMachine(ndim_visible=image_size[0]*image_size[1],
                                     ndim_hidden=300,
                                     is_bottom=True,
                                     image_size=image_size,
                                     is_top=False,
                                     n_labels=10,
                                     batch_size=20
    )

    err2 = rbm2.cd1(visible_trainset=train_imgs, n_iterations=20)
    rbm3 = RestrictedBoltzmannMachine(ndim_visible=image_size[0]*image_size[1],
                                     ndim_hidden=200,
                                     is_bottom=True,
                                     image_size=image_size,
                                     is_top=False,
                                     n_labels=10,
                                     batch_size=20
    )

    err3 = rbm3.cd1(visible_trainset=train_imgs, n_iterations=20)
    plt.figure()
    plt.plot(err1, label='500 nodes')
    plt.plot(err2, label='300 nodes')
    plt.plot(err3, label='200 nodes')
    plt.xlabel("Iterations")
    plt.ylabel("MSE")
    plt.title("Error over epochs")
    plt.legend()
    plt.show()
    '''
    #rbm.cd1(visible_trainset=train_imgs, n_iterations=15)
    #rbm.cd1(visible_trainset=train_imgs, n_iterations=20)

    ''' deep- belief net '''

    print ("\nStarting a Deep Belief Net..")

    #dbn = DeepBeliefNet(sizes={"vis":image_size[0]*image_size[1], "hid":500, "pen":500, "top":2000, "lbl":10},
    dbn = DeepBeliefNet(sizes={"vis":image_size[0]*image_size[1], "hid":500, "pen":500, "top":2000, "lbl":10},
                        image_size=image_size,
                        n_labels=10,
                        batch_size=20
    )

    ''' greedy layer-wise training '''

    #dbn.train_greedylayerwise(vis_trainset=train_imgs, lbl_trainset=train_lbls, n_iterations=2000)
    dbn.train_greedylayerwise(vis_trainset=train_imgs, lbl_trainset=train_lbls, n_iterations=20)

    '''
    # Plot MSE (like recon loss from first two DBN layers)
    # Only works when training
    plt.figure()
    plt.plot(dbn.MSE_v1, label='vis--hid')
    plt.plot(dbn.MSE_v2, label='hid--pen')
    plt.plot(dbn.MSE_v3, label='pen+lbl--top')
    plt.xlabel("Epochs [All minibatches]")
    plt.ylabel("MSE")
    plt.title("Error over epochs | Full Architecture")
    plt.legend()
    plt.show()
    '''
    '''
    ############### Recognition ###############
    print('Checking recognition of training data')
    dbn.recognize(train_imgs, train_lbls)

    print('Checking recognition of testing data')
    dbn.recognize(test_imgs, test_lbls)
    '''
    ############### Generation ###############
    '''
    for digit in range(10):
        print('Generating digit: %i' %digit)
        digit_1hot = np.zeros(shape=(1,10))
        digit_1hot[0,digit] = 1
        dbn.generate(digit_1hot, name="rbms")
    '''

    ''' fine-tune wake-sleep training '''

    dbn.train_wakesleep_finetune(vis_trainset=train_imgs, lbl_trainset=train_lbls, n_iterations=20)

    dbn.recognize(train_imgs, train_lbls)

    dbn.recognize(test_imgs, test_lbls)
    '''
    for digit in range(10):
        digit_1hot = np.zeros(shape=(1,10))
        digit_1hot[0,digit] = 1
        dbn.generate(digit_1hot, name="dbn")
    '''
    '''
    simpler_dbn = DeepBeliefNet(sizes={"vis":image_size[0]*image_size[1], "pen":500, "top":2000, "lbl":10},
                        image_size=image_size,
                        n_labels=10,
                        batch_size=20
    )

    '''
    '''greedy layer-wise training '''
    '''
    #dbn.train_greedylayerwise(vis_trainset=train_imgs, lbl_trainset=train_lbls, n_iterations=2000)
    dbn.train_greedylayerwise(vis_trainset=train_imgs, lbl_trainset=train_lbls, n_iterations=20)
    '''