def train_model():
"""
This function will train model
Tips: Load test,validation data first
Then, seperately load training data, since training data is really huge.
:return:
"""
path = '/home/jht00622/wiki_new.pkl'
data = load_data(path)
## extract different type data
train_dataset = data['train_dataset'] / 255
train_age_labels = data['train_age_labels']
#train_gender_labels = data['train_gender_labels']
valid_dataset = data['valid_dataset'] / 255
valid_age_labels = data['valid_age_labels']
#valid_gender_labels = data['valid_gender_labels']
test_dataset = data['test_dataset'] / 255
test_age_labels = data['test_age_labels']
#test_gender_labels = data['test_gender_labels']
hight = 128
channel = 1
batch_size = 50
learn_rate = 0.001
n_output = 4
total_size = train_dataset.shape[0]
net = Network(n_output=n_output,
n_length=hight,
learning_rate=learn_rate,
batch_size=batch_size,
channel=channel,
output_graph=False,
use_ckpt=False)
num_steps = 50000
for i in range(num_steps):
# randomly sample batch memory from all memory
indices = np.random.permutation(total_size)[:batch_size]
batch_x = train_dataset[indices, :, :, :]
batch_y = train_age_labels[indices, :]
net.learn(batch_x, batch_y)
if i % 20 == 0:
cost, accu_rate = net.get_accuracy_rate(batch_x, batch_y)
print("Iteration: %i. Train loss %.5f, Minibatch accuracy:"
" %.1f%%" % (i, cost, accu_rate))
if i % 100 == 0:
cost, accu_rate = net.get_accuracy_rate(valid_dataset,
valid_age_labels)
print("Iteration: %i. Validation loss %.5f, Validation accuracy:"
" %.1f%%" % (i, cost, accu_rate))
cost, accu_rate = net.get_accuracy_rate(test_dataset,
test_age_labels)
print("Iteration: %i. Test loss %.5f, Test accuracy:"
" %.1f%%" % (i, cost, accu_rate))
def train_model():
"""
This function will train model
Tips: Load test,validation data first
Then, seperately load training data, since training data is really huge.
:return:
"""
path = '/home/hengtong/project/age_gender/data/small/wiki_new.pkl'
data = load_data(path)
## extract different type data
train_dataset = data['train_dataset']/255
train_age_labels = data['train_age_labels']
#train_gender_labels = data['train_gender_labels']
valid_dataset = data['valid_dataset']/255
valid_age_labels = data['valid_age_labels']
#valid_gender_labels = data['valid_gender_labels']
test_dataset = data['test_dataset']/255
test_age_labels = data['test_age_labels']
#test_gender_labels = data['test_gender_labels']
hight = 128
channel = 1
batch_size = 128
learn_rate = 0.01
n_output = 4 # age mode
total_size = train_dataset.shape[0]
net = Network(
n_output = n_output,
n_length=hight,
learning_rate=learn_rate,
batch_size=batch_size,
channel=channel,
output_graph=False,
use_ckpt=False
)
epoch = 400 # epoch
iteration = int(total_size / batch_size)
print iteration
i = 1 # total training time
accu_train_age = []
accu_valid_age = []
accu_test_age = []
early_stop =0 # early stopping flag
train_rate_age = 0
for e in range(epoch):
print("-------------------------------")
print("epoch %d" % (e + 1))
# randomly sample batch memory from all memory
indices = np.random.permutation(total_size)
for ite in range(iteration):
mini_indices = indices[ite * batch_size:(ite + 1) * batch_size]
batch_x = train_dataset[mini_indices, :, :, :]
batch_y_age = train_age_labels[mini_indices, :]
net.learn(batch_x, batch_y_age)
if i % 50 == 0:
cost, train_rate_age= net.get_accuracy_rate(batch_x, batch_y_age)
print("Iteration: %i. Train loss %.5f, Minibatch gen accuracy:"" %.1f%%"% (i, cost, train_rate_age))
accu_train_age.append(train_rate_age)
if i % 50 == 0:
cost, valid_rate_age = net.get_accuracy_rate(valid_dataset, valid_age_labels)
print("Iteration: %i. Validation loss %.5f, Validation gen accuracy:" " %.1f%%" % (i, cost, valid_rate_age))
accu_valid_age.append(valid_rate_age)
cost, test_rate_age= net.get_accuracy_rate(test_dataset, test_age_labels)
print("Iteration: %i. Test loss %.5f, Test gen accuracy:"" %.1f%%" % (i, cost, test_rate_age))
accu_test_age.append(test_rate_age)
if i % 500 == 0:
net.save_parameters()
i = i + 1
# early stopping
if train_rate_age == 100:
if early_stop == 10:
print("Early Stopping!")
break
else:
early_stop = early_stop + 1
net.plot_cost() # plot trainingi cost
plt.figure() # plot accuracy
plt.plot(np.arange(len(accu_train_age)), accu_train_age, label='train age', linestyle='--')
plt.plot(np.arange(len(accu_valid_age)), accu_valid_age, label='valid age', linestyle='-')
plt.plot(np.arange(len(accu_test_age)), accu_test_age, label='test age', linestyle=':')
plt.ylabel('age accuracy')
plt.xlabel('epoch')
plt.legend(loc='lower right')
plt.grid()
plt.savefig('age.png')
