def test_dnn_eval(): banner("dnn cv") mnist_conf = MNIST_CONF.copy() mnist_conf["train_dnn"]["max_iters"] = MAX_ITERS run_DNN(mnist_conf) mnist_conf["init_dnn"] = { "filename": "temp/dnn/final.nnet", "num_hidden_layers": -1, "with_final": 1 } # per-part eval_DNN(mnist_conf) mnist_conf["eval_dnn"] = {"mode": "cv", "batch_size": 1024} eval_DNN(mnist_conf) mnist_conf["eval_dnn"] = {"mode": "per-feat", "batch_size": 1024} eval_DNN(mnist_conf)
def test_sda_dnn(): banner("sda dnn") mnist_conf = MNIST_CONF.copy() mnist_conf["train_sda"]["max_iters"] = MAX_ITERS run_SDA(mnist_conf) mnist_conf["train_dnn"]["max_iters"] = MAX_ITERS mnist_conf["init_dnn"] = { "filename": "temp/sda/final.nnet", "num_hidden_layers": -1, "with_final": 1 } run_DNN(mnist_conf) mnist_conf["init_sda"] = { "filename": "temp/dnn/final.nnet", "num_hidden_layers": -1, "with_final": 1 } mnist_conf["train_sda"]["max_iters"] = 1 run_SDA(mnist_conf)
def test_rbm_dnn(): banner("rbm dnn") mnist_conf = MNIST_CONF.copy() mnist_conf["train_rbm"]["max_iters"] = MAX_ITERS run_RBM(mnist_conf) mnist_conf["train_dnn"]["max_iters"] = MAX_ITERS mnist_conf["init_dnn"] = { "filename": "temp/rbm/final.nnet", "num_hidden_layers": -1, "with_final": 1 } run_DNN(mnist_conf) mnist_conf["init_rbm"] = { "filename": "temp/dnn/final.nnet", "num_hidden_layers": -1, "with_final": 1 } mnist_conf["train_rbm"]["max_iters"] = 0 run_RBM(mnist_conf)
def test_dropout(): banner("dropout") mnist_conf = MNIST_CONF.copy() mnist_conf["train_dnn"]["max_iters"] = MAX_ITERS mnist_conf["model"]["dropout_factor"] = "0.4" run_DNN(mnist_conf)