diff --git a/example/speech-demo/default.cfg b/example/speech-demo/default.cfg index 3e1734b9..072a4aeb 100644 --- a/example/speech-demo/default.cfg +++ b/example/speech-demo/default.cfg @@ -39,7 +39,7 @@ optimizer = speechSGD momentum = 0.9 # set to 0 to disable gradient clipping -clip_gradient = 1 +clip_gradient = 0 # uniform, normal, xavier initializer = Uniform diff --git a/example/speech-demo/lstm_proj.py b/example/speech-demo/lstm_proj.py index 1af26962..ae2271c8 100644 --- a/example/speech-demo/lstm_proj.py +++ b/example/speech-demo/lstm_proj.py @@ -6,8 +6,8 @@ LSTMState = namedtuple("LSTMState", ["c", "h"]) LSTMParam = namedtuple("LSTMParam", ["i2h_weight", "i2h_bias", "h2h_weight", "h2h_bias", - "ph2h_weight" - ]) + "ph2h_weight", + "c2i_bias", "c2f_bias", "c2o_bias"]) LSTMModel = namedtuple("LSTMModel", ["rnn_exec", "symbol", "init_states", "last_states", "seq_data", "seq_labels", "seq_outputs", @@ -32,11 +32,18 @@ def lstm(num_hidden, indata, prev_state, param, seqidx, layeridx, dropout=0., nu gates = i2h + h2h slice_gates = mx.sym.SliceChannel(gates, num_outputs=4, name="t%d_l%d_slice" % (seqidx, layeridx)) - in_gate = mx.sym.Activation(slice_gates[0], act_type="sigmoid") + + Wcidc = mx.sym.broadcast_mul(param.c2i_bias, prev_state.c) + slice_gates[0] + in_gate = mx.sym.Activation(Wcidc, act_type="sigmoid") in_transform = mx.sym.Activation(slice_gates[1], act_type="tanh") - forget_gate = mx.sym.Activation(slice_gates[2], act_type="sigmoid") - out_gate = mx.sym.Activation(slice_gates[3], act_type="sigmoid") + + Wcfdc = mx.sym.broadcast_mul(param.c2f_bias, prev_state.c) + slice_gates[2] + forget_gate = mx.sym.Activation(Wcfdc, act_type="sigmoid") next_c = (forget_gate * prev_state.c) + (in_gate * in_transform) + + Wcoct = mx.sym.broadcast_mul(param.c2o_bias, next_c) + slice_gates[3] + out_gate = mx.sym.Activation(Wcoct, act_type="sigmoid") + next_h = out_gate * mx.sym.Activation(next_c, act_type="tanh") if num_hidden_proj > 0: @@ -62,7 +69,10 @@ def lstm_unroll(num_lstm_layer, seq_len, input_size, i2h_bias = mx.sym.Variable("l%d_i2h_bias" % i), h2h_weight = mx.sym.Variable("l%d_h2h_weight" % i), h2h_bias = mx.sym.Variable("l%d_h2h_bias" % i), - ph2h_weight = mx.sym.Variable("l%d_ph2h_weight" % i) + ph2h_weight = mx.sym.Variable("l%d_ph2h_weight" % i), + c2i_bias = mx.sym.Variable("l%d_c2i_bias" % i, shape=(1,num_hidden)), + c2f_bias = mx.sym.Variable("l%d_c2f_bias" % i, shape=(1,num_hidden)), + c2o_bias = mx.sym.Variable("l%d_c2o_bias" % i, shape=(1, num_hidden)) )) state = LSTMState(c=mx.sym.Variable("l%d_init_c" % i), h=mx.sym.Variable("l%d_init_h" % i)) @@ -102,8 +112,8 @@ def lstm_unroll(num_lstm_layer, seq_len, input_size, hidden_final = mx.sym.Reshape(hidden_concat, target_shape=(0, num_hidden)) pred = mx.sym.FullyConnected(data=hidden_final, num_hidden=num_label, weight=cls_weight, bias=cls_bias, name='pred') - pred = mx.sym.Reshape(pred, target_shape=(0, seq_len, num_label)) - + pred = mx.sym.Reshape(pred, shape=(-1, num_label)) + label = mx.sym.Reshape(label, shape=(-1,)) if take_softmax: sm = mx.sym.SoftmaxOutput(data=pred, label=label, ignore_label=0, use_ignore=True, name='softmax') diff --git a/example/speech-demo/train_lstm_proj.py b/example/speech-demo/train_lstm_proj.py index ec817736..d2a7a274 100644 --- a/example/speech-demo/train_lstm_proj.py +++ b/example/speech-demo/train_lstm_proj.py @@ -57,7 +57,7 @@ def prepare_data(args): def CrossEntropy(labels, preds): labels = labels.reshape((-1,)) - preds = preds.reshape((-1, preds.shape[2])) + preds = preds.reshape((-1, preds.shape[1])) loss = 0. num_inst = 0 for i in range(preds.shape[0]): @@ -70,7 +70,7 @@ def CrossEntropy(labels, preds): def Acc_exclude_padding(labels, preds): labels = labels.reshape((-1,)) - preds = preds.reshape((-1, preds.shape[2])) + preds = preds.reshape((-1, preds.shape[1])) sum_metric = 0 num_inst = 0 for i in range(preds.shape[0]): @@ -163,7 +163,7 @@ def do_training(training_method, args, module, data_train, data_val): def reset_optimizer(): if optimizer == "sgd" or optimizer == "speechSGD": - module.init_optimizer(kvstore='local', + module.init_optimizer(kvstore='device', optimizer=args.config.get('train', 'optimizer'), optimizer_params={'lr_scheduler': lr_scheduler, 'momentum': momentum, @@ -172,7 +172,7 @@ def reset_optimizer(): 'wd': weight_decay}, force_init=True) else: - module.init_optimizer(kvstore='local', + module.init_optimizer(kvstore='device', optimizer=args.config.get('train', 'optimizer'), optimizer_params={'lr_scheduler': lr_scheduler, 'rescale_grad': 1.0, @@ -191,7 +191,7 @@ def reset_optimizer(): lr_scheduler.momentum = np.power(np.power(momentum, 1.0/(data_train.batch_size * truncate_len)), data_batch.effective_sample_count) else: if data_batch.effective_sample_count is not None: - lr_scheduler.effective_sample_count = data_batch.effective_sample_count + lr_scheduler.effective_sample_count = 1#data_batch.effective_sample_count module.forward_backward(data_batch) module.update()