def run(self, n_samples, steps_per_sample, n_layers=3, layer_width=50, n_interior=32,\
n_terminal=32, saved_name=None, use_fd_hessian=False, use_L2_err=True):
if not saved_name:
pickle_dir = DIR_LOC + "/saved_models/{}_Euro".format(
time.strftime("%Y%m%d"))
saved_name = "{}_Euro.ckpt".format(time.strftime("%Y%m%d"))
else:
pickle_dir = DIR_LOC + "/saved_models/{}_{}".format(
time.strftime("%Y%m%d"), saved_name)
saved_name = time.strftime("%Y%m%d") + "_" + saved_name + ".ckpt"
model = DGMNet(n_layers, layer_width, input_dim=self.dim)
self.model = model
S_interior_tnsr = tf.placeholder(tf.float64, [None, self.dim])
t_interior_tnsr = tf.placeholder(tf.float64, [None, 1])
S_terminal_tnsr = tf.placeholder(tf.float64, [None, self.dim])
t_terminal_tnsr = tf.placeholder(tf.float64, [None, 1])
L1_tnsr, L3_tnsr = self.loss_func(model, S_interior_tnsr, t_interior_tnsr,\
S_terminal_tnsr, t_terminal_tnsr, use_fd_hessian=use_fd_hessian, use_L2_err=use_L2_err)
loss_tnsr = L1_tnsr + L3_tnsr
global_step = tf.Variable(0, trainable=False)
boundaries = [5000, 10000, 20000, 30000, 40000, 45000]
values = [1e-4, 5e-5, 1e-5, 5e-6, 1e-6, 5e-7, 1e-7]
learning_rate = tf.train.piecewise_constant(global_step, boundaries,
values)
optimizer = tf.train.AdamOptimizer(
learning_rate=learning_rate).minimize(loss_tnsr)
model_saver = tf.train.Saver()
self.loss_vec, self.L1_vec, self.L3_vec = [], [], []
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(n_samples):
S_interior, t_interior, S_terminal, t_terminal = self.sampler.run(
n_interior, n_terminal)
for _ in range(steps_per_sample):
loss, L1, L3, _ = sess.run([loss_tnsr, L1_tnsr, L3_tnsr, optimizer],\
feed_dict={S_interior_tnsr: S_interior, t_interior_tnsr: t_interior,\
S_terminal_tnsr: S_terminal, t_terminal_tnsr: t_terminal})
self.loss_vec.append(loss)
self.L1_vec.append(L1)
self.L3_vec.append(L3)
print("Iteration {}: Loss: {}; L1: {}; L3: {}".format(
i, loss, L1, L3))
model_saver.save(sess, DIR_LOC + "/saved_models/" + saved_name)
pickle.dump(self.loss_vec, pickle_dir + "_lossvec.pickle")
pickle.dump(self.L1_vec, pickle_dir + "_l1vec.pickle")
# pickle.dump(self.L2_vec, pickle_dir+"_l2vec.pickle")
pickle.dump(self.L3_vec, pickle_dir + "_l3vec.pickle")
def restore(self, S, t, saved_name, n_layers=3, layer_width=50):
self.model = DGMNet(n_layers, layer_width, input_dim=1)
S_interior_tnsr = tf.placeholder(tf.float32, [None, 1])
t_interior_tnsr = tf.placeholder(tf.float32, [None, 1])
V = self.model(S_interior_tnsr, t_interior_tnsr)
model_saver = tf.train.Saver()
with tf.Session() as sess:
model_saver.restore(
sess, DIR_LOC + '/saved_models/{}.ckpt'.format(saved_name))
fitted_optionValue = sess.run(V,
feed_dict={
S_interior_tnsr: S,
t_interior_tnsr: t
})
print("Model {}: {}".format(saved_name, fitted_optionValue.T))
return fitted_optionValue.T