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

.idea/workspace.xml

@@ -2,7 +2,8 @@
 <project version="4">
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-      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/workspace.xml" afterPath="$PROJECT_DIR$/.idea/workspace.xml" />
+      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/cnn/word_accetuation/cnn_dictionary/1_epoch.h5" afterPath="$PROJECT_DIR$/cnn/word_accetuation/cnn_dictionary/1_epoch.h5" />
+      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/cnn/word_accetuation/cnn_dictionary/1_epoch_history.pkl" afterPath="$PROJECT_DIR$/cnn/word_accetuation/cnn_dictionary/1_epoch_history.pkl" />
       <change type="MODIFICATION" beforePath="$PROJECT_DIR$/cnn/word_accetuation/cnn_dictionary/cnn.ipynb" afterPath="$PROJECT_DIR$/cnn/word_accetuation/cnn_dictionary/cnn.ipynb" />
       <change type="MODIFICATION" beforePath="$PROJECT_DIR$/prepare_data.py" afterPath="$PROJECT_DIR$/prepare_data.py" />
     </list>
@@ -34,8 +35,8 @@
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         <entry file="file://$PROJECT_DIR$/prepare_data.py">
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-              <caret line="461" column="22" lean-forward="false" selection-start-line="461" selection-start-column="22" selection-end-line="461" selection-end-column="22" />
+            <state relative-caret-position="442">
+              <caret line="462" column="19" lean-forward="false" selection-start-line="462" selection-start-column="4" selection-end-line="462" selection-end-column="19" />
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                 <element signature="e#24#63#0" expanded="true" />
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@@ -823,8 +824,8 @@
     <entry file="file://$PROJECT_DIR$/cnn/word_accetuation/cnn_dictionary/cnn_test_on_other_attributes.ipynb" />
     <entry file="file://$PROJECT_DIR$/prepare_data.py">
       <provider selected="true" editor-type-id="text-editor">
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-          <caret line="461" column="22" lean-forward="false" selection-start-line="461" selection-start-column="22" selection-end-line="461" selection-end-column="22" />
+        <state relative-caret-position="442">
+          <caret line="462" column="19" lean-forward="false" selection-start-line="462" selection-start-column="4" selection-end-line="462" selection-end-column="19" />
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prepare_data.py

@@ -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]