def train(batch_size=4, nb_epoch=10):
checkpointer = ModelCheckpoint(
filepath=os.path.join('data', 'checkpoints', 'lstm' + '-' + 'features' + \
'.{epoch:03d}-{val_loss:.3f}.hdf5'),
verbose=1,
save_best_only=True)
tb = TensorBoard(log_dir=os.path.join('data', 'logs', 'lstm'))
early_stopper = EarlyStopping(patience=5)
timestamp = time.time()
csv_logger = CSVLogger(os.path.join('data', 'logs', 'lstm' + '-' + 'training-' + \
str(timestamp) + '.log'))
data = Dataset(
seq_length=26,
class_limit=2,
)
steps_per_epoch = 4
X, y = data.get_all_sequences_in_memory('training')
X_test, y_test = data.get_all_sequences_in_memory('testing')
rm = ResearchModels(len(data.classes), 'lstm', data.seq_length, None)
print "##################################################"
#X=X[2:]
#X_test=X_test[2:]
X = np.ravel(X)
X = X.reshape(16, 26, -1)
X_test = np.ravel(X_test)
X_test = X_test.reshape(3, 26, -1)
#print "X", X[0:10]
print "X.shape", X.shape
print "y.shape", y.shape
print "X_test.shape", X_test.shape
print "y_test.shape", y_test.shape
print "##################################################"
rm.model.fit(X,
y,
batch_size=batch_size,
validation_data=(X_test, y_test),
verbose=1,
callbacks=[tb, early_stopper, csv_logger],
epochs=nb_epoch)
predictions = rm.model.predict(X_test)
loss, accuracy = rm.model.evaluate(X_test, y_test)
#print 'Loss:',loss*100,'%'
for j in predictions:
if j[0] > j[1]:
print "Driver is alert with the confidence of", (j[0] * 100), "%"
else:
print "Driver is drowsy with the confidence of", (j[1] * 100), "%"
print "Sounding the alarm now...."
duration = 10 # second
freq = 440 # Hz
os.system(
'play --no-show-progress --null --channels 1 synth %s sine %f'
% (duration, freq))
tb = TensorBoard(log_dir=os.path.join('data', 'logs', 'lstm'))
early_stopper = EarlyStopping(patience=5)
timestamp = time.time()
csv_logger = CSVLogger(os.path.join('data', 'logs', 'lstm' + '-' + 'training-' + str(timestamp) + '.log'))
data = Dataset(
seq_length=seq,
class_limit=2,
)
steps_per_epoch = 4
X, y = data.get_all_sequences_in_memory('training', hyper, seq)
X_test, y_test = data.get_all_sequences_in_memory('testing', hyper, seq)
#X_test, y_test = data.get_all_sequences_in_memory('testing', cnt, seq)
rm = ResearchModels(len(data.classes),'lstm',data.seq_length, None)
print("##################################################")
#X=X[2:]
#X_test=X_test[2:]
print(X.shape)
X=np.ravel(X)
# First argument is number of training videos, second is number of images within it
print(X.shape)
#print(train_no)
#print(train_no * seq)
X=X.reshape((160, seq,-1))
