def main():
seq_length = 20
class_limit = None
image_shape = (80, 80, 3)
data = DataSet(
seq_length=seq_length,
class_limit=class_limit,
image_shape=image_shape
)
batch_size = 20
concat = False
path = 'D:\kerc\CODE_challenge'
f = open('test_predict.csv' ,'w' ,newline='')
writer = csv.writer(f)
writer.writerow(['id' ,'label'])
data_type = 'images'
#data_type = 'features'
N, X_test = data.get_all_sequences_in_memory_with_name('test', data_type)
#X_test, y_test = data.get_all_sequences_in_memory('test', data_type)
#y_test1 = np.argmax(y_test, axis=1)
md = load_model('./data/checkpoints/weights.hdf5')
optimizer = Adam(lr=1e-6) # aggressively small learning rate
crits = ['accuracy']
md.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=crits)
score = md.predict(X_test)
for i ,a in enumerate(N):
print (a)
print (score[i])
print (np.argmax(score[i]))
la = np.argmax(score[i])
if la == 0:
label = 'Angry'
if la == 1:
label = 'Disgust'
if la == 2:
label = 'Fear'
if la == 3:
label = 'Happy'
if la == 4:
label = 'Neutral'
if la == 5:
label = 'Sad'
if la == 6:
label = 'Surprise'
writer = csv.writer(f)
writer.writerow([a+'.mp4' ,label])
f.close()
