with open(os.path.join(save_dir, "result.json"), 'w') as f:
json.dump(result_dict, f)
x = range(epoch_final)
plt.cla()
plt.plot(x, train_loss_list, label="train loss")
plt.plot(x, test_loss_list, label="test loss")
plt.legend()
plt.savefig(os.path.join(save_dir, "loss.png"))
plt.cla()
plt.plot(x, train_acc_list, label="train acc")
plt.plot(x, test_acc_list, label="test acc")
plt.legend()
plt.savefig(os.path.join(save_dir, "acc.png"))
train_data, test_data, train_label, test_label = load_mnist_2d('data')
with open("config.json", 'r') as f:
config_all = json.load(f)
while len(config_all['train_config']) != 0:
train_config = config_all['train_config'][0]
config = copy.copy(config_all['default_config'])
for key, value in train_config.items():
config[key] = value
LOG_INFO('Using config %s now' % (config['name']))
model, loss = build_model(config)
train_and_save(model, loss, train_data, test_data, train_label, test_label)
config_all['finish_config'].append(train_config)
del config_all['train_config'][0]
with open("config.json", 'w') as f:
loss /= times
acc /= times
return acc, loss
def inference(model, sess, X): # Test Process
return sess.run([model.pred], {model.x_: X, model.keep_prob: 1.0})[0]
with tf.Session() as sess:
if not os.path.exists(FLAGS.train_dir):
os.mkdir(FLAGS.train_dir)
if not os.path.exists(FLAGS.img_dir):
os.mkdir(FLAGS.img_dir)
if FLAGS.is_train:
X_train, X_test, y_train, y_test = load_mnist_2d(FLAGS.data_dir)
X_val, y_val = X_train[50000:], y_train[50000:]
X_train, y_train = X_train[:50000], y_train[:50000]
mlp_model = Model(True)
if tf.train.get_checkpoint_state(FLAGS.train_dir):
mlp_model.saver.restore(
sess, tf.train.latest_checkpoint(FLAGS.train_dir))
else:
tf.global_variables_initializer().run()
pre_losses = [1e18] * 3
best_val_acc = 0.0
loss_plot_list = []
acc_plot_list = []