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Added custom actual accuracy metric which shows how many cases are correctly classified + changed to binary_crossentropy from mse

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lkrsnik 2017-07-07 16:11:44 +02:00
parent 669aa6bbfd
commit 0cc949897f
2 changed files with 14 additions and 6 deletions
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11
.idea/workspace.xml
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@ -2,7 +2,8 @@
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@ -34,8 +35,8 @@
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@ -823,8 +824,8 @@
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9
prepare_data.py
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@ -6,6 +6,7 @@ import numpy as np
import h5py
import gc
import math
import keras.backend as K
# functions for saving, loading and shuffling whole arrays to ram
@ -319,7 +320,8 @@ def generate_X_and_y(dictionary, max_word, max_num_vowels, content, vowels, acce
if len(word_accetuations) > 0:
y_value = 1/len(word_accetuations)
for el in word_accetuations:
y[i][el] = y_value
# y[i][el] = y_value
y[i][el] = 1
else:
y[i][0] = 1
# y[i][generate_presentable_y(word_accetuations, list(el[3]), max_num_vowels)] = 1
@ -457,6 +459,11 @@ def generate_X_and_y_RAM_efficient(name, split_number):
h5f.close()
# metric for calculation of correct results
def actual_accuracy(y_true, y_pred):
return K.mean(K.equal(K.mean(K.equal(K.round(y_true), K.round(y_pred)), axis=-1), 1.0))
# generator for inputs for tracking of data fitting
def generate_fake_epoch(orig_X, orig_X_additional, orig_y, batch_size):
size = orig_X.shape[0]