class Main:
def __init__(self, loadModelGen=None):
os.makedirs('model/' + conf.PATH, exist_ok=True)
os.makedirs('data/' + conf.PATH, exist_ok=True)
self.Model = ResNet().to(conf.DEVICE)
if loadModelGen == None:
self.modelGen = 0
print("modelGen : ", self.modelGen)
data = self_play.randomData()
data = self_play.inflated(data)
self.Model.fit(data, policyVias=1, valueVias=1)
np.savez('data/' + conf.PATH + '/Gen' + str(self.modelGen),
data[0], data[1], data[2])
torch.save(self.Model.state_dict(),
'model/' + conf.PATH + '/Gen' + str(self.modelGen))
else:
self.modelGen = loadModelGen
self.Model.load_state_dict(
torch.load('model/' + conf.PATH + '/Gen' + str(self.modelGen)))
def train(self):
while True:
self.modelGen += 1
if self.modelGen == 11:
break
print("modelGen : ", self.modelGen)
data = self_play.DataGenerate(self.Model)
data = self_play.inflated(data)
self.Model.fit(data, policyVias=1, valueVias=1)
np.savez('data/' + conf.PATH + '/Gen' + str(self.modelGen),
data[0], data[1], data[2])
torch.save(self.Model.state_dict(),
'model/' + conf.PATH + '/Gen' + str(self.modelGen))
class ImageNetSequence(tf.keras.utils.Sequence):
def __init__(self, x_set, y_set, batch_size):
# '''
model = ResNet(input_shape=(None, 32, 32, 3), output_dim=10, config=C)
model.compile(optimizer='adam',
loss = tf.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'])
#loss='sparse_categorical_crossentropy',
# model.build(input_shape = (None, 28, 28, 1))
# model.summary()
# model.build(input_shape=(None, 256, 256, 3))
import ipdb; ipdb.set_trace()
'''
hist = model.fit_generator(generator=train_set.__iter__(),
steps_per_epoch=int(1281167/C.BATCH_SIZE),
validation_data=val_set.__iter__(),
validation_steps=int(50000/C.BATCH_SIZE),
epochs=60)
'''
# plot the model composition:
# """ cifar-10
model.fit(train_images, train_labels, batch_size=64, epochs=20)
# plot the model composition:
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print("\nTest Loss: ", test_loss)
print("\nTest Accuracy: ", test_acc)
#"""
import ipdb; ipdb.set_trace()
