def write_predictions():
pred_list = []
prediction = tf.argmax(y_conv,1)
length = int(len(validation) / test_batch)
for i in range(length):
a = i*test_batch
pred_list = pred_list + prediction.eval(feed_dict={x: validation[a:a + test_batch],y_: v_labels[a:a + test_batch],keep_prob: 1.0}, session=sess).tolist()
pred_list += prediction.eval(feed_dict={x: validation[length * test_batch:],y_: v_labels[length * test_batch:],keep_prob: 1.0}, session=sess).tolist()
# print(len(validation))
# print(len(pred_list))
class_acc = {}
class_amount = {}
for i, pred in enumerate(pred_list):
class_amount[val_labels[i]] = 0
class_acc[val_labels[i]] = 0
for i, pred in enumerate(pred_list):
class_amount[val_labels[i]] += 1
if pred == val_labels[i]:
class_acc[val_labels[i]] += 1
write_file = str(save_location + run_number + '_predictions.txt')
with open(write_file, 'w') as f:
for i, pred in enumerate(pred_list):
string = str(str(pred) + ' ' + str(val_labels[i]) + ' ' +
str(class_acc[val_labels[i]]) + '/' + str(class_amount[val_labels[i]]) + '=' +
str(float(class_acc[val_labels[i]] / class_amount[val_labels[i]])) + '\n')
f.write(string)
error_gen.make_html(str(net_name + '_0'), write_file, 'kanji_dictionary_32_distort2.json', 'validation_32_distort2.json')
def write_predictions():
pred_list = []
prediction = tf.argmax(y_conv,1)
length = int(len(validation) / test_batch)
for i in range(length):
a = i*test_batch
pred_list = pred_list + prediction.eval(feed_dict={x: validation[a:a + test_batch],y_: v_labels[a:a + test_batch],keep_prob: 1.0}, session=sess).tolist()
write_file = str(save_location + run_number + '_predictions.txt')
with open(write_file, 'w') as f:
for i, pred in enumerate(pred_list):
string = str(str(pred) + ' ' + str(val_labels[i]) + '\n')
f.write(string)
error_gen.make_html(net_name, write_file, 'kanji_dictionary.json', 'validation.json')