Added multiple results and created working grid settings and scripts

workbench.py

@@ -27,7 +27,11 @@ from prepare_data import *
 # save_inputs('../../internal_representations/inputs/shuffeled_matrix_validate_inputs_other_features_output_11.h5', X_validate, y_validate,  other_features = X_other_features_validate)
 # X_train, X_other_features_train, y_train = load_inputs('cnn/internal_representations/inputs/shuffeled_matrix_train_inputs_other_features_output_11.h5', other_features=True)
 # X_validate, X_other_features_validate, y_validate = load_inputs('cnn/internal_representations/inputs/shuffeled_matrix_validate_inputs_other_features_output_11.h5', other_features=True)
-data = Data('l', save_generated_data=False, number_of_syllables=True)
+# letters
+# data = Data('l', save_generated_data=False, number_of_syllables=True)
+
+# syllabled letters
+data = Data('l', save_generated_data=False, accent_classification=True)
 data.generate_data('letters_word_accetuation_train',
                    'letters_word_accetuation_test',
                    'letters_word_accetuation_validate', content_name='SlovarIJS_BESEDE_utf8.lex',
@@ -36,7 +40,7 @@ data.generate_data('letters_word_accetuation_train',
 
 
 num_examples = len(data.x_train)  # training set size
-nn_output_dim = 10
+nn_output_dim = 13
 nn_hdim = 516
 batch_size = 16
 # actual_epoch = 1
@@ -46,13 +50,23 @@ num_fake_epoch = 20
 
 
 
-
+# letters
 conv_input_shape=(23, 36)
-othr_input = (141, )
+
+# syllabled letters
+# conv_input_shape=(10, 5168)
+
+
+# othr_input = (140, )
+othr_input = (150, )
 
 conv_input = Input(shape=conv_input_shape, name='conv_input')
+# letters
 x_conv = Conv1D(115, (3), padding='same', activation='relu')(conv_input)
 x_conv = Conv1D(46, (3), padding='same', activation='relu')(x_conv)
+
+# syllabled letters
+# x_conv = Conv1D(200, (2), padding='same', activation='relu')(conv_input)
 x_conv = MaxPooling1D(pool_size=2)(x_conv)
 x_conv = Flatten()(x_conv)
 
@@ -62,10 +76,10 @@ x = concatenate([x_conv, othr_input])
 # x = Dense(1024, input_dim=(516 + 256), activation='relu')(x)
 x = Dense(256, activation='relu')(x)
 x = Dropout(0.3)(x)
-x = Dense(256, activation='relu')(x)
+x = Dense(512, activation='relu')(x)
+x = Dropout(0.3)(x)
+x = Dense(512, activation='relu')(x)
 x = Dropout(0.3)(x)
-x = Dense(256, activation='relu')(x)
-x = Dropout(0.2)(x)
 x = Dense(nn_output_dim, activation='sigmoid')(x)