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May 26, 2017 04:00
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| import math | |
| import scipy.io | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from mpl_toolkits.mplot3d import Axes3D | |
| from matplotlib.ticker import LinearLocator, FormatStrFormatter | |
| import tensorflow as tf | |
| # PLOT3 IN PYTHON | |
| def plot3(a, b, c, mark=".", col="b", title=""): | |
| fig = plt.figure(figsize=(8, 5)) | |
| plt.hold(True) | |
| ax = Axes3D(fig) | |
| ax.scatter(a, b, c, marker=mark,color=col) | |
| ax.set_xlabel('X_1'), ax.set_ylabel('X_2'), ax.set_zlabel('Y') | |
| ax.set_xlim(np.min(a), np.max(a)), ax.set_ylim(np.min(b), np.max(b)) | |
| ax.set_title(title, fontsize=25) | |
| plt.show() | |
| def plot3_2(a1, b1, c1, a2, b2, c2, l1="", l2="", mark=".", col1="k", col2="r", title=""): | |
| fig = plt.figure(figsize=(8, 5)) | |
| plt.hold(True) | |
| ax = Axes3D(fig) | |
| s1 = ax.scatter(a1, b1, c1, marker=mark, color=col1) | |
| s2 = ax.scatter(a2, b2, c2, marker=mark, color=col2) | |
| plt.legend((s1, s2), (l1, l2), loc='lower left', scatterpoints=1) | |
| ax.set_xlabel('X_1'), ax.set_ylabel('X_2'), ax.set_zlabel('Y') | |
| ax.set_xlim(np.min(a1), np.max(a1)), ax.set_ylim(np.min(b1), np.max(b1)) | |
| ax.set_title(title, fontsize=25) | |
| plt.show() | |
| # HDMN CLASS DEFINE | |
| tf_var = tf.Variable | |
| tf_rn = tf.random_normal | |
| tf_ru = tf.random_uniform | |
| tf_relu = tf.nn.relu | |
| tf_tanh = tf.nn.tanh | |
| class hmdn_class(object): | |
| # CONSTRUCTOR | |
| def __init__(self, _name, opt, _sess=None): | |
| # INIT STUFFS | |
| self.name = _name | |
| self.sess = opt['sess'] | |
| self.xdata = opt['xdata'] | |
| self.ydata = opt['ydata'] # TRAINING DATA | |
| self.ndata = self.xdata.shape[0] | |
| self.dimx = self.xdata.shape[1] | |
| self.dimy = self.ydata.shape[1] | |
| self.kmix = opt['kmix'] | |
| self.nhid1 = opt['nhid1'] | |
| self.nhid2 = opt['nhid2'] | |
| self.hid1actv = opt['hid1actv'] | |
| self.hid2actv = opt['hid2actv'] | |
| self.var_actv = opt['var_actv'] | |
| self.gain_p = opt['gain_p'] | |
| self.gain_s = opt['gain_s'] | |
| self.gain_e = opt['gain_e'] | |
| # PARAMETERS RELATED TO LEARNING | |
| self.epoch = 0 | |
| self.nepoch = opt['nepoch'] | |
| self.nbatch = opt['nbatch'] | |
| self.lrs = opt['lrs'] | |
| self.wd_rate = opt['wd_rate'] | |
| self.poltype = opt['poltype'] | |
| self.min_testmse = 1e10 | |
| # SAVE PATH | |
| self.savename = ("./weights_%s.mat" % (self.name)) | |
| # GET RANGE OF THE DATA | |
| self.ymin, self.ymax = np.min(self.ydata), np.max(self.ydata) | |
| # BUILD MODEL | |
| self.build_model() | |
| # OPTIMIZER | |
| wd_rate = self.wd_rate | |
| self.loss = self.hgmm_nll_out \ | |
| + wd_rate*tf.nn.l2_loss(self.W['x_h1']) \ | |
| + wd_rate*tf.nn.l2_loss(self.W['h1_h2']) \ | |
| + wd_rate*tf.nn.l2_loss(self.W['h2_p']) \ | |
| + wd_rate*tf.nn.l2_loss(self.W['h2_m']) \ | |
| + wd_rate*tf.nn.l2_loss(self.W['h2_s']) \ | |
| + wd_rate*tf.nn.l2_loss(self.W['h2_e']) \ | |
| + wd_rate*tf.nn.l2_loss(self.b['x_h1']) \ | |
| + wd_rate*tf.nn.l2_loss(self.b['h1_h2']) \ | |
| + wd_rate*tf.nn.l2_loss(self.b['h2_p']) \ | |
| + wd_rate*tf.nn.l2_loss(self.b['h2_m']) \ | |
| + wd_rate*tf.nn.l2_loss(self.b['h2_s']) \ | |
| + wd_rate*tf.nn.l2_loss(self.b['h2_e']) | |
| # OPTIMIZER | |
| self.optm = tf.train.AdamOptimizer(learning_rate=self.lr | |
| , beta1=0.9, beta2=0.999, epsilon=0.01).minimize(self.loss) | |
| # , beta1=0.9, beta2=0.999, epsilon=0.01).minimize(self.loss) | |
| # PRINT SOME | |
| print ("Initializing HDMN object:") | |
| print ("[%s] ndata: [%d], dimx: [%d], dimy: [%d], ymin: [%.2f], ymax: [%.2f], kmix: [%d]" | |
| % (self.name, self.ndata, self.dimx, self.dimy, self.ymin, self.ymax, self.kmix)) | |
| # BUILD MODEL | |
| def build_model(self): | |
| # INITIALIZE WEIGHTS | |
| STD = 0.1 | |
| self.W = { | |
| 'x_h1': tf_var(tf_rn([self.dimx, self.nhid1], stddev=STD), name='W/x_h1'), | |
| 'h1_h2': tf_var(tf_rn([self.nhid1, self.nhid2], stddev=STD), name='W/h1_h2'), | |
| 'h2_p': tf_var(tf_rn([self.nhid2, self.kmix], stddev=STD), name='W/h2_p'), | |
| 'h2_m': tf_var(tf_rn([self.nhid2, self.kmix*self.dimy], stddev=STD), name='W/h2_m'), | |
| 'h2_s': tf_var(tf_rn([self.nhid2, self.kmix*self.dimy], stddev=STD), name='W/h2_s'), | |
| 'h2_e': tf_var(tf_rn([self.nhid2, self.dimy], stddev=STD), name='W/h2_e') | |
| } | |
| self.b = { | |
| 'x_h1': tf_var(tf_rn([self.nhid1], stddev=STD), name='b/x_h1'), | |
| 'h1_h2': tf_var(tf_rn([self.nhid2], stddev=STD), name='b/h1_h2'), | |
| 'h2_p': tf_var(tf_rn([self.kmix], stddev=STD), name='b/h2_p'), | |
| # THIS SIMPLE TRICK IS IMPORTANT | |
| 'h2_m': tf_var(tf_rn([self.kmix*self.dimy], stddev=STD), name='b/h2_m'), | |
| # 'h2_m': tf_var(tf_ru([self.kmix*self.dimy], minval=self.ymin, maxval=self.ymax), name='b/h2_m'), | |
| 'h2_s': tf_var(tf_rn([self.kmix*self.dimy], stddev=STD), name='b/h2_s'), | |
| 'h2_e': tf_var(tf_rn([self.dimy], stddev=STD), name='b/h2_e') | |
| } | |
| # DEFINE PLACEHOLDERS | |
| self.x = tf.placeholder(dtype=tf.float32, shape=[None, self.dimx], name="X") | |
| self.y = tf.placeholder(dtype=tf.float32, shape=[None, self.dimy], name="Y") | |
| self.lr = tf.placeholder(dtype=tf.float32) | |
| self.kp = tf.placeholder(dtype=tf.float32) | |
| # CONSTRUCT MAIN GRAPH | |
| self.hmdn_out = self.hmdn(self.x) | |
| # OBJECTIVE FUNCTION | |
| self.hgmm_nll_out = self.hgmm_nll(self.y, self.hmdn_out) | |
| # HMDN FUNCTION | |
| def hmdn(self, _x): | |
| if self.hid1actv is 'relu': | |
| _h1 = tf_relu(tf.matmul(_x, self.W['x_h1'])+self.b['x_h1']) | |
| elif self.hid1actv is 'tanh': | |
| _h1 = tf_tanh(tf.matmul(_x, self.W['x_h1'])+self.b['x_h1']) | |
| elif self.hid1actv is 'elu': | |
| _h1 = tf.nn.elu(tf.matmul(_x, self.W['x_h1'])+self.b['x_h1']) | |
| if self.hid2actv is 'relu': | |
| _h2 = tf_relu(tf.matmul(_h1, self.W['h1_h2'])+self.b['h1_h2']) | |
| elif self.hid2actv is 'tanh': | |
| _h2 = tf_tanh(tf.matmul(_h1, self.W['h1_h2'])+self.b['h1_h2']) | |
| elif self.hid2actv is 'elu': | |
| _h2 = tf.nn.elu(tf.matmul(_h1, self.W['h1_h2'])+self.b['h1_h2']) | |
| _h2 = tf.nn.dropout(_h2, self.kp) # DROPOUT | |
| # MIXTURE WEIGHTS (PI) | |
| _pi_hat = tf.matmul(_h2, self.W['h2_p']) + self.b['h2_p'] | |
| _pi_hat_a = tf.exp(self.gain_p*(_pi_hat - tf.reduce_max(_pi_hat, 1, keep_dims=True))) | |
| _pi_hat_invsum = tf.reciprocal(tf.reduce_sum(_pi_hat_a, 1, keep_dims=True)) | |
| _pi = tf.multiply(_pi_hat_invsum, _pi_hat_a) | |
| # MITURE MU (MU) | |
| _mu = tf.matmul(_h2, self.W['h2_m']) + self.b['h2_m'] | |
| # MIXTURE SIGMA (SIGMA) | |
| _sigma_hat = tf.matmul(_h2, self.W['h2_s']) + self.b['h2_s'] | |
| if self.var_actv is 'sigmoid': | |
| _sigma = self.gain_s*tf.sigmoid(_sigma_hat) | |
| elif self.var_actv is 'exp': | |
| _sigma = self.gain_s*tf.exp(_sigma_hat) | |
| else: | |
| print ("SOMETHING IS WRONG IN [HMDN]") | |
| # HETEROSCADESTIC NOISE (ERROR) | |
| _err_hat = tf.matmul(_h2, self.W['h2_e']) + self.b['h2_e'] | |
| if self.var_actv is 'sigmoid': | |
| _err = self.gain_e *tf.sigmoid(_err_hat) | |
| elif self.var_actv is 'exp': | |
| _err = self.gain_e *tf.exp(_err_hat) | |
| else: | |
| print ("SOMETHING IS WRONG IN [HMDN]") | |
| # OUTS | |
| _out = { | |
| 'h1': _h1, 'h2': _h2, 'pi': _pi, 'mu': _mu, 'sigma': _sigma, 'err': _err | |
| } | |
| return _out | |
| # UTILITY FUNCTIONS | |
| def kron(self, _x, _k): | |
| _h = tf.shape(_x)[0] | |
| _w = tf.shape(_x)[1] | |
| return tf.reshape(tf.tile(tf.expand_dims(_x, axis=2), [1, 1, _k]), [_h, _w*_k]) | |
| def tf_normal(self, _y, _mu, _sigma): | |
| _result = (_y-_mu)/_sigma | |
| _result = -tf.square(_result)/2 | |
| _result = tf.exp(_result)/(math.sqrt(2*math.pi)*_sigma) | |
| return _result | |
| # HETEROSCADESTIC GAUSSIAN MIXTURE MODEL NEGATIVE LOG LIKELIHOOD | |
| def hgmm_nll(self, _y, _hmdn_out): | |
| _pi = _hmdn_out['pi'] | |
| _mu = _hmdn_out['mu'] | |
| _sigma = _hmdn_out['sigma'] | |
| _err = _hmdn_out['err'] | |
| _probs = self.tf_normal(tf.tile(_y, [1, self.kmix]), _mu | |
| , _sigma+tf.tile(_err, [1, self.kmix])) | |
| # KRON TO ALL Y-DIM | |
| _temp = tf.multiply(self.kron(_pi, self.dimy), _probs) | |
| _res = tf.reduce_sum(_temp, 1, keep_dims=True) | |
| _eps = 1e-9 | |
| _temp = _res +_eps | |
| return tf.reduce_mean(-tf.log( _temp )) # NLL | |
| # GET MOST PROBABLE OUTPUT OF HMDN | |
| def hmdn_sample(self, _hmdn_out): | |
| _sample_pi = _hmdn_out['pi'] | |
| _sample_mu = _hmdn_out['mu'] | |
| _sample_sigma = _hmdn_out['sigma'] | |
| _sample_err = _hmdn_out['err'] | |
| _nsample = _sample_pi.shape[0] | |
| _dimsample = self.dimy | |
| _outval = np.zeros((_nsample, _dimsample)) | |
| for i in range(_nsample): | |
| _currpi = _sample_pi[i] | |
| _maxidx = np.argmax(_currpi) | |
| _val = _sample_mu[i, self.dimy*_maxidx:self.dimy*(_maxidx+1)] | |
| _outval[i, :] = _val | |
| return _outval | |
| # TRAIN | |
| def train_hmdn(self): | |
| # FIRST, SHUFFLE DATA | |
| np.random.seed(0) | |
| randpermlist = np.random.permutation(self.ndata) | |
| self.xdata = self.xdata[randpermlist, :] | |
| self.ydata = self.ydata[randpermlist, :] | |
| # THEN, SEPARATE | |
| self.ntrain = np.int(self.ndata*0.95) | |
| self.trainx = self.xdata[:self.ntrain, :] | |
| self.trainy = self.ydata[:self.ntrain, :] | |
| self.testx = self.xdata[self.ntrain:, :] | |
| self.testy = self.ydata[self.ntrain:, :] | |
| self.ntest = self.testx.shape[0] | |
| # TRAIN CONFIGURATION | |
| NEPOCH = self.nepoch | |
| NBATCH = self.nbatch | |
| NITER = int(self.ntrain/NBATCH) | |
| PRINTEVERY = NEPOCH // 40 | |
| PLOTEVERY = NEPOCH // 5 | |
| # TRAIN | |
| mses_train = np.zeros(NEPOCH) | |
| mses_test = np.zeros(NEPOCH) | |
| losses = np.zeros(NEPOCH) | |
| for epoch in range(NEPOCH): | |
| self.epoch = epoch | |
| randpermlist = np.random.permutation(self.ntrain) # PERMUTE INDICES | |
| sumloss = 0 | |
| for i in range(NITER): | |
| randidx = randpermlist[i*NBATCH:(i+1)*NBATCH] | |
| batchx = self.trainx[randidx, :] | |
| batchy = self.trainy[randidx, :] | |
| # FEED WHILE TRAINING | |
| if epoch < NEPOCH/2: | |
| feeds = {self.x: batchx, self.y: batchy, self.lr:self.lrs[0], self.kp: 1.0} | |
| elif epoch < 3*NEPOCH/4: | |
| feeds = {self.x: batchx, self.y: batchy, self.lr:self.lrs[1], self.kp: 1.0} | |
| else: | |
| feeds = {self.x: batchx, self.y: batchy, self.lr:self.lrs[2], self.kp: 1.0} | |
| # OPIMIZE | |
| self.sess.run(self.optm, feed_dict=feeds) | |
| curloss = self.sess.run(self.loss, feed_dict=feeds) | |
| sumloss += curloss | |
| # AVERAGE LOSS | |
| avgloss = sumloss / NITER | |
| # COMPUTE AVERAGE PREDICTION LOSS (TEST DATA) | |
| feeds = {self.x: self.testx, self.kp: 1.0} | |
| hmdn_out_val = self.sess.run(self.hmdn_out, feed_dict=feeds) | |
| hmdn_sample_val = self.hmdn_sample(hmdn_out_val) | |
| mse_test = ((self.testy - hmdn_sample_val) ** 2).mean(axis=None) | |
| feeds = {self.x: self.trainx, self.kp: 1.0} | |
| hmdn_out_val = self.sess.run(self.hmdn_out, feed_dict=feeds) | |
| hmdn_sample_val = self.hmdn_sample(hmdn_out_val) | |
| mse_train = ((self.trainy - hmdn_sample_val) ** 2).mean(axis=None) | |
| # SAVE | |
| mses_train[epoch] = mse_train | |
| mses_test[epoch] = mse_test | |
| losses[epoch] = avgloss | |
| if (epoch%PRINTEVERY) == 0 or (epoch+1) == NEPOCH: | |
| print ("[%4d/%d] avgloss: %.4f, mse_train: %.4f, mse_test: %.4f" | |
| % (epoch, NEPOCH, avgloss, mse_train, mse_test)) | |
| # SAVE WEIGHT | |
| self.save_weights() | |
| # SAVE CURRENT BEST WEIGHT | |
| if mse_test < self.min_testmse: | |
| self.min_testmse = mse_test | |
| self.save_good_weights() | |
| if (epoch%PLOTEVERY) == 0 or (epoch+1) == NEPOCH: | |
| # PLOT | |
| self.plot_recon_hmdn() | |
| print ("OPTIMIZATION FINISHED.") | |
| # FINAL PLOT LOSS CURVE | |
| plt.figure(figsize=(8, 3)) | |
| plt.plot(losses) | |
| plt.title('LOSS CURVE') | |
| plt.figure(figsize=(8, 3)) | |
| plt.plot(mses_train) | |
| plt.plot(mses_test) | |
| plt.legend(['Train', 'Test']) | |
| plt.title('MSE') | |
| # PLOT RECON | |
| def plot_recon_hmdn(self): | |
| feeds = {self.x: self.xdata, self.kp: 1.0} | |
| hmdn_out_val = self.sess.run(self.hmdn_out, feed_dict=feeds) | |
| pred_ydata = self.hmdn_sample(hmdn_out_val) | |
| mse_data = ((self.ydata - pred_ydata) ** 2).mean(axis=None) | |
| # PLOT IN TWO DIMENSIONAL INPUT | |
| dim = self.xdata.shape[1] | |
| if dim > 2: | |
| X = self.xdata | |
| U, S, V = np.linalg.svd(X.T, full_matrices=False) | |
| proj_X = np.dot(X, U[:, :2]) | |
| else: | |
| proj_X = self.xdata | |
| # PLOT EACH Y DIM | |
| ndim = self.dimy | |
| if ndim > 2: | |
| ndim = 2 | |
| for plot_dim in range(ndim): | |
| plot3_2(proj_X[:,0], proj_X[:,1], self.ydata[:,plot_dim] | |
| , proj_X[:,0], proj_X[:,1], pred_ydata[:,plot_dim] | |
| , l1="Training Data", l2="Predicted Data" | |
| , title=("[%s] kmix: [%d], dim: [%d], mse_data: [%.4f]" | |
| % (self.name, self.kmix, plot_dim, mse_data))) | |
| # SAVE WEIGHTS | |
| def save_weights(self): | |
| # GET WEIGHTS | |
| W_x_h1 = self.sess.run(self.W['x_h1']) | |
| W_h1_h2 = self.sess.run(self.W['h1_h2']) | |
| W_h2_p = self.sess.run(self.W['h2_p']) | |
| W_h2_m = self.sess.run(self.W['h2_m']) | |
| W_h2_s = self.sess.run(self.W['h2_s']) | |
| W_h2_e = self.sess.run(self.W['h2_e']) | |
| b_x_h1 = self.sess.run(self.b['x_h1']) | |
| b_h1_h2 = self.sess.run(self.b['h1_h2']) | |
| b_h2_p = self.sess.run(self.b['h2_p']) | |
| b_h2_m = self.sess.run(self.b['h2_m']) | |
| b_h2_s = self.sess.run(self.b['h2_s']) | |
| b_h2_e = self.sess.run(self.b['h2_e']) | |
| # SAMPLE TEST (XTEST->YPRED) | |
| test_in = self.xdata[:10, :] | |
| feeds = {self.x: test_in, self.kp: 1.0} | |
| test_out = self.hmdn_sample(self.sess.run(self.hmdn_out, feed_dict=feeds)) | |
| # OTHERS TO SAVE | |
| xdata, ydata = self.xdata, self.ydata | |
| nhid1, nhid2 = self.nhid1, self.nhid2 | |
| kmix = self.kmix | |
| dimx, dimy = self.dimx, self.dimy | |
| epoch = self.epoch | |
| gain_p, gain_s, gain_e = self.gain_p, self.gain_s, self.gain_e | |
| hid1actv, hid2actv = self.hid1actv, self.hid2actv | |
| var_actv = self.var_actv | |
| # SAVE | |
| scipy.io.savemat(self.savename | |
| , mdict={'W_x_h1': W_x_h1, 'W_h1_h2': W_h1_h2, 'W_h2_p': W_h2_p, | |
| 'W_h2_m': W_h2_m, 'W_h2_s': W_h2_s, 'W_h2_e': W_h2_e, | |
| 'b_x_h1': b_x_h1, 'b_h1_h2': b_h1_h2, 'b_h2_p': b_h2_p, | |
| 'b_h2_m': b_h2_m, 'b_h2_s': b_h2_s, 'b_h2_e': b_h2_e, | |
| 'test_in': test_in, 'test_out': test_out, | |
| 'xdata': xdata, 'ydata': ydata, | |
| 'nhid1': nhid1, 'nhid2': nhid2, 'kmix': kmix, | |
| 'gain_p': gain_p, 'gain_s': gain_s, 'gain_e': gain_e, | |
| 'hid1actv': hid1actv, 'hid2actv': hid2actv, | |
| 'var_actv': var_actv, | |
| 'epoch': epoch | |
| }) | |
| print ("[%s] SAVED." % (self.savename)) | |
| # SAVE WEIGHTS | |
| def save_good_weights(self): | |
| # GET WEIGHTS | |
| W_x_h1 = self.sess.run(self.W['x_h1']) | |
| W_h1_h2 = self.sess.run(self.W['h1_h2']) | |
| W_h2_p = self.sess.run(self.W['h2_p']) | |
| W_h2_m = self.sess.run(self.W['h2_m']) | |
| W_h2_s = self.sess.run(self.W['h2_s']) | |
| W_h2_e = self.sess.run(self.W['h2_e']) | |
| b_x_h1 = self.sess.run(self.b['x_h1']) | |
| b_h1_h2 = self.sess.run(self.b['h1_h2']) | |
| b_h2_p = self.sess.run(self.b['h2_p']) | |
| b_h2_m = self.sess.run(self.b['h2_m']) | |
| b_h2_s = self.sess.run(self.b['h2_s']) | |
| b_h2_e = self.sess.run(self.b['h2_e']) | |
| # SAMPLE TEST (XTEST->YPRED) | |
| test_in = self.xdata[:10, :] | |
| feeds = {self.x: test_in, self.kp: 1.0} | |
| test_out = self.hmdn_sample(self.sess.run(self.hmdn_out, feed_dict=feeds)) | |
| # OTHERS TO SAVE | |
| xdata, ydata = self.xdata, self.ydata | |
| nhid1, nhid2 = self.nhid1, self.nhid2 | |
| kmix = self.kmix | |
| dimx, dimy = self.dimx, self.dimy | |
| epoch = self.epoch | |
| gain_p, gain_s, gain_e = self.gain_p, self.gain_s, self.gain_e | |
| hid1actv, hid2actv = self.hid1actv, self.hid2actv | |
| var_actv = self.var_actv | |
| # SAVE | |
| savename = ("./weights_%s_good.mat" % (self.name)) | |
| scipy.io.savemat(savename | |
| , mdict={'W_x_h1': W_x_h1, 'W_h1_h2': W_h1_h2, 'W_h2_p': W_h2_p, | |
| 'W_h2_m': W_h2_m, 'W_h2_s': W_h2_s, 'W_h2_e': W_h2_e, | |
| 'b_x_h1': b_x_h1, 'b_h1_h2': b_h1_h2, 'b_h2_p': b_h2_p, | |
| 'b_h2_m': b_h2_m, 'b_h2_s': b_h2_s, 'b_h2_e': b_h2_e, | |
| 'test_in': test_in, 'test_out': test_out, | |
| 'xdata': xdata, 'ydata': ydata, | |
| 'nhid1': nhid1, 'nhid2': nhid2, 'kmix': kmix, | |
| 'gain_p': gain_p, 'gain_s': gain_s, 'gain_e': gain_e, | |
| 'hid1actv': hid1actv, 'hid2actv': hid2actv, | |
| 'var_actv': var_actv, | |
| 'epoch': epoch | |
| }) | |
| print ("GOOD [%s] SAVED." % (self.savename)) | |
| # | |
| # | |
| # | |
| print ("HMDN CLASS READY") |
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