def quickrun3(operators, grabbed_vars=None, dir='probeview', feed_dict=None):
sess = tf.Session()
probe_stream = TFT.viewprep(sess, dir=dir)
sess.run(tf.global_variables_initializer())
results = sess.run([operators, grabbed_vars], feed_dict=feed_dict)
sess.close()
TFT.show_results(results[1], grabbed_vars, dir)
return results
def quickrun2(operators, grabbed_vars=None, dir='probeview'):
sess = tf.Session()
probe_stream = TFT.viewprep(sess, dir=dir)
sess.run(tf.global_variables_initializer())
results = sess.run([
operators, grabbed_vars
]) # result = a list of output values, one from each operator.
sess.close()
TFT.show_results(results[1], grabbed_vars, dir)
return results
print("Validation Error: ", self.validation_error_history[-1][1])
# Consider validation testing here or something
# TFT.fireup_tensorboard(logdir='probeview')
# Plots.line([errors, self.validation_error_history])
print("\nFinished Training")
print("Training Cost: " + str(self.training_error_history[-1][1]))
print("Training Error %: " + str(self.training_error_history[-1][1] * 100) + " %")
print("Validation Error: " + str(self.validation_error_history[-1][1]))
print("Validation Error %: " + str(self.validation_error_history[-1][1] * 100) + "%")
# Plots.scatter([self.training_error_history, self.validation_error_history],
# ["Training Error", "Validation Error"])
# Plots.plotWeights([self.grabbed_weigths_history])
TFT.viewprep(sess)
Plots.line([self.training_error_history, self.validation_error_history], ["Training Cost", "Validation Error"])
print("\nResults for Training Set")
self.do_testing(self.case_manager.get_training_cases())
print("\nResults for Testing Set")
self.do_testing(self.case_manager.get_testing_cases())
if self.config.mbsize > 0: # Should run map test
print("\nRunning Map Tests")
map_batch_size = self.config.mbsize
np.random.shuffle(case_list) # Select random cases for this minibatch
cases = case_list[:map_batch_size]
self.do_testing(cases, grabvars=self.grabvars, scenario="mapping")