Added some results

workbench.py

@@ -40,7 +40,7 @@ nn_output_dim = 10
 nn_hdim = 516
 batch_size = 16
 # actual_epoch = 1
-actual_epoch = 60
+actual_epoch = 40
 # num_fake_epoch = 2
 num_fake_epoch = 20
 
@@ -51,7 +51,7 @@ conv_input_shape=(23, 36)
 othr_input = (141, )
 
 conv_input = Input(shape=conv_input_shape, name='conv_input')
-x_conv = Conv1D(133, (3), padding='same', activation='relu')(conv_input)
+x_conv = Conv1D(115, (3), padding='same', activation='relu')(conv_input)
 x_conv = Conv1D(46, (3), padding='same', activation='relu')(x_conv)
 x_conv = MaxPooling1D(pool_size=2)(x_conv)
 x_conv = Flatten()(x_conv)
@@ -82,11 +82,10 @@ history = model.fit_generator(data.generator('train', batch_size, content_name='
                               epochs=actual_epoch*num_fake_epoch,
                               validation_data=data.generator('test', batch_size, content_name='SlovarIJS_BESEDE_utf8.lex', content_location=''),
                               validation_steps=data.x_test.shape[0]/(batch_size * num_fake_epoch),
-                              verbose=2,
-                              workers=4
+                              verbose=2
                               )
 
-name = '60_epoch'
+name = '40_epoch'
 model.save(name + '.h5')
 output = open(name + '_history.pkl', 'wb')
 pickle.dump(history.history, output)