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Update to py2/3 print compatible, progress prints on single line. #3

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Update to py2/3 print compatible, progress prints on single line. #3

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20 changes: 12 additions & 8 deletions djinn/djinn_fns.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,8 @@
# For details about use and distribution, please read DJINN/LICENSE .
###############################################################################

from __future__ import print_function

import tensorflow as tf
import numpy as np
from sklearn.tree import _tree
Expand Down Expand Up @@ -351,14 +353,15 @@ def multilayer_perceptron(x, weights, biases):
train_accur[pp][epoch] = avg_cost
valid_accur[pp][epoch] = cost.eval({x: xtest, y: ytest, keep_prob:dropout_keep_prob})
# display training progresss
if epoch % display_step == 0:
if epoch % display_step == 0 or epoch == (training_epochs-1):
if regression == True:
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
print("Epoch:", '%04d' % (epoch+1),'/ %d' %(training_epochs), "cost=", "{:.9f}".format(avg_cost), end='\r')
#for classification, print cost and accuracy:
else:
avg_accuracy = accuracy.eval({x: xtrain, y: ytrain, keep_prob:dropout_keep_prob})
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost),
"accuracy=", "{:.3f}".format(avg_accuracy))
print("Epoch:", '%04d' % (epoch+1),'/ %d'%(training_epochs), "cost=", "{:.9f}".format(avg_cost),
"accuracy=", "{:.3f}".format(avg_accuracy),end='\r')
print("")
print("Optimization Finished!")

#save final weights/biases
Expand Down Expand Up @@ -646,15 +649,16 @@ def tf_continue_training(regression, xscale, yscale, x1, y1, ntrees,
keep_prob:dropout_keep_prob})
avg_cost += c / total_batch
# display training progresss
if epoch % display_step == 0:
if epoch % display_step == 0 or epoch == (training_epochs-1):
if regression == True:
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
print("Epoch:", '%04d' % (epoch+1),'/ %d' %(training_epochs), "cost=", "{:.9f}".format(avg_cost), end='\r')
#for classification, print cost and accuracy:
else:
avg_accuracy = sess[pp].run(accuracy,
feed_dict = {x: xtrain, y: ytrain, keep_prob:dropout_keep_prob})
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost),
"accuracy=", "{:.3f}".format(avg_accuracy))
print("Epoch:", '%04d' % (epoch+1), '/ %d' %(training_epochs), "cost=", "{:.9f}".format(avg_cost),
"accuracy=", "{:.3f}".format(avg_accuracy), end='\r')
print("")
print("Optimization Finished!")

#save model and nn info
Expand Down