def main():
y_trues = []
y_preds = []
for i in xrange(0, len(model_types)):
model_type = model_types[i]
# test dataset
model_test_dataset = 'dataset_' + model_type
# path to saved model files
saved_model_weights_path = './trained_for_pred/' + \
model_type + '/model/Best-weights.h5'
saved_model_arch_path = './trained_for_pred/' + \
model_type + '/model/scratch_model.json'
test_data_dir = './datasets/' + model_test_dataset + '/test'
# init DataManager class
dataManager = DataManager(img_height, img_width)
# load model
print("===================== load model =========================")
model = load_model(saved_model_arch_path, saved_model_weights_path)
# get test data
print("===================== load data =========================")
test_data = dataManager.get_test_data(test_data_dir)
# start the eval process
print("===================== start eval =========================")
y_true = test_data.classes
# Confution Matrix and Classification Report
Y_pred = model.predict_generator(test_data,
num_of_test_samples // batch_size)
y_pred = np.argmax(Y_pred, axis=1)
y_trues.append(y_true)
y_preds.append(y_pred)
# init PlotData class
plotData = PlotData()
# Compute ROC curve and ROC area for each class
plotData.plot_roc(y_trues, y_preds, colors, linestyles, legends,
save_plt_roc)
def main():
# init DataManager class
dataManager = DataManager(img_height, img_width)
# init PlotData class
plotData = PlotData()
# load model
print("===================== load model =========================")
model = load_model()
# get test data
print("===================== load data =========================")
test_data = dataManager.get_test_data(test_data_dir)
# start the eval process
print("===================== start eval =========================")
y_true = test_data.classes
# Confution Matrix and Classification Report
Y_pred = model.predict_generator(test_data, num_of_test_samples // batch_size)
y_pred = np.argmax(Y_pred, axis=1)
# plot confusion matrix
cm = confusion_matrix(y_true, y_pred)
plotData.plot_confusion_matrix(
cm, cm_plot_labels, save_plt_cm, title='Confusion Matrix')
plotData.plot_confusion_matrix(
cm, cm_plot_labels, save_plt_normalized_cm, normalize=True, title='Normalized Confusion Matrix')
# Compute ROC curve and ROC area for each class
roc_auc = plotData.plot_roc(y_true, y_pred, save_plt_roc)
mae = mean_absolute_error(y_true, y_pred)
mse = mean_squared_error(y_true, y_pred)
accuracy = accuracy_score(y_true, y_pred)
print('mean absolute error: ' + str(mae))
print('mean squared error: ' + str(mse))
print('Area Under the Curve (AUC): ' + str(roc_auc))
c_report = classification_report(
y_true, y_pred, target_names=cm_plot_labels)
print(c_report)
delete_file(save_eval_report)
with open(save_eval_report, 'a') as f:
f.write('\n\n')
f.write('******************************************************\n')
f.write('************** Evalaluation Report ***************\n')
f.write('******************************************************\n')
f.write('\n\n')
f.write('- Accuracy Score: ' + str(accuracy))
f.write('\n\n')
f.write('- Mean Absolute Error (MAE): ' + str(mae))
f.write('\n\n')
f.write('- Mean Squared Error (MSE): ' + str(mse))
f.write('\n\n')
f.write('- Area Under the Curve (AUC): ' + str(roc_auc))
f.write('\n\n')
f.write('- Confusion Matrix:\n')
f.write(str(cm))
f.write('\n\n')
f.write('- Normalized Confusion Matrix:\n')
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
f.write(str(cm))
f.write('\n\n')
f.write('- Classification report:\n')
f.write(str(c_report))
f.close()
train_validation = ['train', 'validation']
data = pd.read_csv(train_log_data_path)
acc = data['acc'].values
val_acc = data['val_acc'].values
loss = data['loss'].values
val_loss = data['val_loss'].values
# plot metrics to the stats dir
plotData.plot_2d(acc, val_acc, 'epoch', 'accuracy',
'Model Accuracy', train_validation, save_plt_accuracy)
plotData.plot_2d(loss, val_loss, 'epoch', 'loss',
'Model Loss', train_validation, save_plt_loss)
plotData.plot_model_bis(data, save_plt_learning)
'''
# Load configuration
with open('{}/config.pkl'.format(FLAGS.checkpoints_dir), 'rb') as f:
config = pickle.load(f)
# Load data
dm = DataManager(data_dir=config['data_dir'],
stopwords_file=config['stopwords_file'],
sequence_len=config['sequence_len'],
n_samples=config['n_samples'],
test_size=config['test_size'],
val_samples=config['batch_size'],
random_state=config['random_state'],
ensure_preprocessed=True)
# Import graph and evaluate the model using test data
original_text, x_test, y_test, test_seq_len = dm.get_test_data(original_text=True)
graph = tf.Graph()
with graph.as_default():
sess = tf.Session()
# Import graph and restore its weights
print('Restoring graph ...')
saver = tf.train.import_meta_graph("{}/model.ckpt.meta".format(FLAGS.checkpoints_dir))
saver.restore(sess, ("{}/model.ckpt".format(FLAGS.checkpoints_dir)))
# Recover input/output tensors
input = graph.get_operation_by_name('input').outputs[0]
target = graph.get_operation_by_name('target').outputs[0]
seq_len = graph.get_operation_by_name('lengths').outputs[0]
dropout_keep_prob = graph.get_operation_by_name('dropout_keep_prob').outputs[0]
predict = graph.get_operation_by_name('final_layer/softmax/predictions').outputs[0]