# e.g. 1000_60sec.pkl
in_pkl = sys.argv[1]
out_pkl = sys.argv[2]
with open(in_pkl) as f:
dat = pickle.load(f)
X_train, X_valid, X_test = dat[0]
sys.stderr.write("X_train shape = %s\n" % str(X_train.shape))
sys.stderr.write("X_valid shape = %s\n" % str(X_valid.shape))
sys.stderr.write("X_test shape = %s\n" % str(X_test.shape))
args = dict()
args["seed"] = 0
args["batch_size"] = 16
args["learning_rate"] = 0.01
args["momentum"] = 0.9
args["num_epochs"] = 2000
args["X_train"] = X_train
args["X_valid"] = X_valid
args["X_test"] = X_test
args["update_method"] = rmsprop
args["out_pkl"] = out_pkl
args["in_model"] = "../models/16mar_minimalist2_use_mean.model"
args["config"] = "../configurations/19feb_testing_d_minimalist2.py"
experiment.train(args)
out_pkl = sys.argv[2]
with open(in_pkl) as f:
dat = pickle.load(f)
X_train, X_valid, X_test = dat[0]
sys.stderr.write("X_train shape = %s\n" % str(X_train.shape))
sys.stderr.write("X_valid shape = %s\n" % str(X_valid.shape))
sys.stderr.write("X_test shape = %s\n" % str(X_test.shape))
args = dict()
args["seed"] = 0
args["num_inputs"] = 1
args["batch_size"] = 64
args["learning_rate"] = 0.1
args["momentum"] = 0.9
args["num_epochs"] = 2000
args["X_train"] = X_train
args["X_valid"] = X_valid
args["X_test"] = X_test
#args["adagrad"] = True
#args["config"] = "basic_net.py"
args["config"] = "basic_net_d3.py"
model = experiment.train(args)
sys.stderr.write("writing to file: %s\n" % (out_pkl))
with open(out_pkl, "wb") as f:
pickle.dump(model, f, pickle.HIGHEST_PROTOCOL)
with open(in_pkl) as f:
dat = pickle.load(f)
X_train, X_valid, X_test = dat[0]
sys.stderr.write("X_train shape = %s\n" % str(X_train.shape))
sys.stderr.write("X_valid shape = %s\n" % str(X_valid.shape))
sys.stderr.write("X_test shape = %s\n" % str(X_test.shape))
args = dict()
args["num_inputs"] = 1
args["batch_size"] = 500
args["learning_rate"] = 0.01
args["momentum"] = 0.9
args["num_epochs"] = 2000
args["X_train"] = X_train
args["X_valid"] = X_valid
args["X_test"] = X_test
#args["adagrad"] = True
#args["config"] = "basic_net.py"
args["config"] = "basic_net_200x3.py"
model = experiment.train(args)
sys.stderr.write( "writing to file: %s\n" % (out_pkl) )
with open(out_pkl, "wb") as f:
pickle.dump(model, f, pickle.HIGHEST_PROTOCOL)
out_pkl = sys.argv[2]
with open(in_pkl) as f:
dat = pickle.load(f)
X_train, X_valid, X_test = dat[0]
sys.stderr.write("X_train shape = %s\n" % str(X_train.shape))
sys.stderr.write("X_valid shape = %s\n" % str(X_valid.shape))
sys.stderr.write("X_test shape = %s\n" % str(X_test.shape))
args = dict()
args["seed"] = 0
args["batch_size"] = 128
args["learning_rate"] = 0.01
args["momentum"] = 0.9
args["num_epochs"] = 4000
args["X_train"] = X_train
args["X_valid"] = X_valid
args["X_test"] = X_test
args["update_method"] = rmsprop
args["out_pkl"] = out_pkl
args["units"] = [1024, 1024]
args["config"] = "../../configurations/19mar_variable.py"
experiment.train(args)
#sys.stderr.write( "writing to file: %s\n" % (out_pkl) )
#with open(out_pkl, "wb") as f:
# pickle.dump(model, f, pickle.HIGHEST_PROTOCOL)