def test_srl_neural_model(train_file_path, valid_file_path):
train_corpora = Conll05Corpora()
train_corpora.load(train_file_path)
train_problem = SRLProblem(train_corpora)
valid_corpora = Conll05Corpora()
valid_corpora.load(valid_file_path)
valid_problem = SRLProblem(valid_corpora)
init_rng()
nn_architecture = SRLNetowrkArchitecture()
nn_architecture.word_feature_dim = 50
nn_architecture.pos_feature_dim = 50
nn_architecture.dist_feature_dim = 50
nn_architecture.conv_window_height = 3
nn_architecture.conv_output_dim = 500
nn_architecture.hidden_layer_output_dims = [500,500]
hyper_param = NeuralModelHyperParameter()
hyper_param.n_epochs = 1000
hyper_param.learning_rate = 0.001 #1
hyper_param.learning_rate_decay_ratio = 0.8
hyper_param.learning_rate_lowerbound = 0.0000
hyper_param.l1_reg = 0
hyper_param.l2_reg = 0
problem_character = train_problem.get_problem_property()
m = SRLNetwork(problem_character, nn_architecture)
optimizer = CGDOptimizer()
trained_batch_num = 0
valid_freq = 10
for iter in range(1000):
for sentence in train_problem.sentences():
for X, y in train_problem.get_dataset_for_sentence(sentence):
if trained_batch_num % valid_freq == 0:
evaluate(m, valid_problem, trained_batch_num/valid_freq)
optimizer.batch_size = X.shape[0]
optimizer.update_chunk(X, y)
param = optimizer.optimize(m, m.get_parameter())
m.set_parameter(param)
trained_batch_num += 1
def test_cdg_optimizer():
layer_setting = [{"type": "perception", "activator_type": "sigmoid", "input_dim":50, "output_dim": 100},
{"type": "perception", "activator_type": "sigmoid", "input_dim":100, "output_dim": 100},
{"type": "perception", "activator_type": "sigmoid", "input_dim":100, "output_dim": 100},
{"type": "perception", "activator_type": "sigmoid", "input_dim":100, "output_dim": 1}]
cost = {"type":"mse"}
m = MultiLayerPerception(layer_setting, cost)
optimizer = CGDOptimizer(max_epoches=3, batch_size=500)
X = numpy.random.random((1000, 50))
y = numpy.random.random((1000, 1))
optimizer.update_chunk(X,y)
param = optimizer.optimize(m, m.get_parameter())
m.set_parameter(param)
