| 131 | self.train_op = tf.train.AdamOptimizer(1e-2).minimize(self.cost_op) |
| 132 | |
| 133 | def set(self, preSoftmaxPi, preSoftmaxA, preSoftmaxR, mu, logSigma): |
| 134 | # assume build has already been called |
| 135 | # we just assign these new variables |
| 136 | op1 = self.preSoftmaxPi.assign(preSoftmaxPi) |
| 137 | op2 = self.preSoftmaxA.assign(preSoftmaxA) |
| 138 | op3 = self.preSoftmaxR.assign(preSoftmaxR) |
| 139 | op4 = self.mu.assign(mu) |
| 140 | op5 = self.logSigma.assign(logSigma) |
| 141 | self.session.run([op1, op2, op3, op4, op5]) |
| 142 | |
| 143 | def fit(self, X, max_iter=10): |
| 144 | # train the HMM model using stochastic gradient descent |