if (path.exists('training_progress.csv')):
progress = np.loadtxt('training_progress.csv', delimiter=',').tolist()
else:
progress = []
if (path.exists('models/min_model.h5')):
model = load_model(location='models/min_model.h5')
else:
model = load_model()
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
X_train, Y_train, X_train_gender = load_training_data()
X_dev, Y_dev, X_dev_gender = load_development_data()
if (path.exists('learner_params.txt')):
learner_params = np.loadtxt('learner_params.txt')
min_loss_dev = learner_params[0]
min_mae = learner_params[1]
prev_loss_dev = learner_params[4]
loss_dev = [learner_params[2], learner_params[3]]
increase_count = int(learner_params[5])
current_epoch_number = int(learner_params[6])
total_epoch_count = int(learner_params[7]) + 1
else:
min_loss_dev = 10000
min_mae = 10000
import os
from os import path
import random
#os.environ["CUDA_VISIBLE_DEVICES"]="3,4,5,6"
training_progress = []
development_progress = []
test_progress = []
model = load_model()
model.compile(optimizer='adagrad', loss='mse', metrics=['mae'])
X_train, Y_train = load_training_data()
X_dev, Y_dev = load_development_data()
X_test, Y_test = load_test_data()
min_mse_dev = 10000
min_mae_dev = 10000
min_mse_test = 10000
min_mae_test = 10000
current_epoch_number = 1
total_epoch_count = 100
m = X_train.shape[0]
batch_size_list = list(range(1, m))
print("\n\n")
import numpy as np
import sklearn.metrics
from load_data import load_development_data
from load_model import load_model
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
if __name__ == "__main__":
model = load_model()
model.load_weights('optimal_weights.h5')
dev_COVAREP_X_FORMANT, dev_facial_X_pose, dev_gaze_X_action, dev_transcript, dev_Y, dev_X_gender = load_development_data(
)
model.compile(loss='mse',
optimizer='adam',
metrics=['mean_absolute_error'])
dev_Y_hat = model.predict(
[dev_facial_X_pose, dev_gaze_X_action, dev_transcript, dev_X_gender])
dev_Y = np.array(dev_Y)
dev_Y_hat = dev_Y_hat.reshape((dev_Y.shape[0], ))
RMSE = np.sqrt(sklearn.metrics.mean_squared_error(dev_Y, dev_Y_hat))
MAE = sklearn.metrics.mean_absolute_error(dev_Y, dev_Y_hat)
EVS = sklearn.metrics.explained_variance_score(dev_Y, dev_Y_hat)