def __init__(self, n_layer, lstm_unit, input_shape, feature, data_type):
DATA = load_var(data_type)
self.EMOTIONS = DATA['EMOTIONS']
self.model = Sequential()
if n_layer > 1:
self.model.add(
LSTM(lstm_unit,
return_sequences=True,
input_shape=input_shape,
dropout=0.2))
layer_count = 1
while layer_count < n_layer:
if layer_count == n_layer - 1:
self.model.add(
LSTM(lstm_unit, return_sequences=False, dropout=0.2))
else:
self.model.add(
LSTM(lstm_unit, return_sequences=True, dropout=0.2))
layer_count += 1
else:
self.model.add(
LSTM(lstm_unit,
return_sequences=False,
input_shape=input_shape,
dropout=0.2))
nb_class = len(self.EMOTIONS)
self.model.add(Dense(nb_class, activation='softmax'))
current_time = time.strftime("%Y%m%d-%H%M%S")
self.base_dir = '../LSTM/' + data_type + '/' + feature + '/'
self.model_dir = 'LSTM_' + str(n_layer) + '_' + str(
lstm_unit) + '_' + current_time + '/'
filename = 'LSTM.h5'
self.model_file = self.base_dir + self.model_dir + filename
def __init__(self, model, base_dir, scores=[]):
DATA = load_var(data_type)
self.EMOTIONS = DATA['EMOTIONS']
self.model = model
self.scores = scores
self.base_dir = '../SVM/' + data_type + '/' + feature + '/'
# plot normal confusion matrix
fig2, ax2 = plt.subplots()
plot_confusion_matrix(cm,
title='LSTM',
float_display='.0f',
class_names=EMOTIONS)
plt.savefig(base_dir + 'cm_test.png')
df = pd.DataFrame(cm_percent, index=EMOTIONS, columns=EMOTIONS)
df.index.name = 'Actual'
df.columns.name = 'Predicted'
df.to_csv(base_dir + 'confusion_matrix_test.csv', float_format='%.4f')
if __name__ == '__main__':
feature = 'VGG16'
data_type = 'Basic'
eval_mode = 'game'
DATA = load_var(data_type)
EMOTIONS = DATA['EMOTIONS']
base_dir = '../best model/' + data_type + '/' + feature + '/'
model_file = base_dir + 'LSTM.h5'
# evaluate the model with test set
X_val, y_val, X_test, y_test = load_test_data(data_type,
feature,
eval_mode=eval_mode)
model = load_model(model_file)
print(model.summary())
display_scores(model, X_val, y_val, X_test, y_test)
save_results(model, X_test, y_test, EMOTIONS, base_dir)