else:
ctr = np.load(in_pkl)
X_train, X_valid, X_test = ctr["arr_0"], ctr["arr_1"], ctr["arr_2"]
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"] = [2000]
#args["out_nonlinearity"] = "sigmoid"
args["forget_gate"] = 1.0
args["config"] = "../../configurations/19mar_variable_3.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)
X_train, X_valid, X_test = ctr["arr_0"], ctr["arr_1"], ctr["arr_2"]
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"] = [600]
#args["out_nonlinearity"] = "sigmoid"
args["forget_gate"] = 1.0
args["config"] = "../../configurations/19mar_variable_3.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)
