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()