def do_training(self, epochs=100, test_interval=10, show_interval=50):
errors = []
if test_interval: self.avg_vector_distances = []
self.current_session = sess = TFT.gen_initialized_session()
step = 0
for i in range(epochs):
error = 0
grabvars = [self.error]
for c in self.cases:
feeder = {self.input: [c[0]], self.target: [c[1]]}
_, grabvals, _ = self.run_one_step([self.trainer],
grabvars,
step=step,
show_interval=show_interval,
session=sess,
feed_dict=feeder)
error += grabvals[0]
step += 1
errors.append(error)
if (test_interval and i % test_interval == 0):
self.avg_vector_distances.append(
calc_avg_vect_dist(self.do_testing(sess, scatter=False)))
PLT.figure()
TFT.simple_plot(errors, xtitle="Epoch", ytitle="Error", title="")
if test_interval:
PLT.figure()
TFT.simple_plot(self.avg_vector_distances,
xtitle='Epoch',
ytitle='Avg Hidden-Node Vector Distance',
title='')
def training_session(self,
epochs,
sess=None,
dir="probeview",
continued=False):
self.roundup_probes()
session = sess if sess else TFT.gen_initialized_session(dir=dir)
self.current_session = session
self.do_training(session,
self.caseman.get_training_cases(),
epochs,
continued=continued)
def quickrun4(operators,
grabbed_vars=None,
dir='probeview',
session=None,
feed_dict=None,
step=1,
show_interval=1):
sess = session if session else TFT.gen_initialized_session(dir=dir)
results = sess.run([operators, grabbed_vars], feed_dict=feed_dict)
if (show_interval and step % show_interval) == 0:
TFT.show_results(results[1], grabbed_vars, dir)
return results[0], results[1], sess
def run_one_step(self,
operators,
grabbed_vars=None,
probed_vars=None,
dir='probeview',
session=None,
feed_dict=None,
step=1,
show_interval=1):
sess = session if session else TFT.gen_initialized_session(dir=dir)
if probed_vars is not None:
results = sess.run([operators, grabbed_vars, probed_vars],
feed_dict=feed_dict)
sess.probe_stream.add_summary(results[2], global_step=step)
else:
results = sess.run([operators, grabbed_vars], feed_dict=feed_dict)
if show_interval and (step % show_interval == 0):
self.display_grabvars(results[1], grabbed_vars, step=step)
return results[0], results[1], sess
def tfex8(size=5, steps=50, tvect=None, learning_rate=0.5, showint=10):
target = tvect if tvect else np.ones((1, size))
w = tf.Variable(np.random.uniform(-.1, .1, size=(size, size)),
name='weights') # weights applied to x.
b = tf.Variable(np.zeros((1, size)), name='bias') # bias terms
x = tf.placeholder(tf.float64, shape=(1, size), name='input')
y = tf.sigmoid(
tf.matmul(x, w) + b, name='out-sigmoid'
) # Gather all weighted inputs, then apply activation function
error = tf.reduce_mean(tf.square(target - y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
training_operator = optimizer.minimize(error)
feeder = {x: np.random.uniform(-1, 1, size=(1, size))}
sess = TFT.gen_initialized_session()
for step in range(steps):
quickrun4([training_operator], [w, b, y],
session=sess,
feed_dict=feeder,
step=step,
show_interval=showint)
TFT.close_session(sess)