From ce9bf2fd3266bc36d35b007cd012a485ad78fe25 Mon Sep 17 00:00:00 2001 From: fengjiayi Date: Wed, 13 Sep 2017 18:37:12 -0700 Subject: [PATCH] update train.py of chapter 6 --- 06.understand_sentiment/train.py | 23 ++++++++++++++--------- 1 file changed, 14 insertions(+), 9 deletions(-) diff --git a/06.understand_sentiment/train.py b/06.understand_sentiment/train.py index 7878f00..d3e467a 100644 --- a/06.understand_sentiment/train.py +++ b/06.understand_sentiment/train.py @@ -28,7 +28,7 @@ def convolution_net(input_dim, class_dim=2, emb_dim=128, hid_dim=128): input=[conv_3, conv_4], size=class_dim, act=paddle.activation.Softmax()) lbl = paddle.layer.data("label", paddle.data_type.integer_value(2)) cost = paddle.layer.classification_cost(input=output, label=lbl) - return cost + return cost, output def stacked_lstm_net(input_dim, @@ -51,7 +51,6 @@ def stacked_lstm_net(input_dim, """ assert stacked_num % 2 == 1 - layer_attr = paddle.attr.Extra(drop_rate=0.5) fc_para_attr = paddle.attr.Param(learning_rate=1e-3) lstm_para_attr = paddle.attr.Param(initial_std=0., learning_rate=1.) para_attr = [fc_para_attr, lstm_para_attr] @@ -66,7 +65,7 @@ def stacked_lstm_net(input_dim, fc1 = paddle.layer.fc( input=emb, size=hid_dim, act=linear, bias_attr=bias_attr) lstm1 = paddle.layer.lstmemory( - input=fc1, act=relu, bias_attr=bias_attr, layer_attr=layer_attr) + input=fc1, act=relu, bias_attr=bias_attr) inputs = [fc1, lstm1] for i in range(2, stacked_num + 1): @@ -80,8 +79,7 @@ def stacked_lstm_net(input_dim, input=fc, reverse=(i % 2) == 0, act=relu, - bias_attr=bias_attr, - layer_attr=layer_attr) + bias_attr=bias_attr) inputs = [fc, lstm] fc_last = paddle.layer.pooling( @@ -97,7 +95,7 @@ def stacked_lstm_net(input_dim, lbl = paddle.layer.data("label", paddle.data_type.integer_value(2)) cost = paddle.layer.classification_cost(input=output, label=lbl) - return cost + return cost, output if __name__ == '__main__': @@ -121,8 +119,8 @@ if __name__ == '__main__': # network config # Please choose the way to build the network # by uncommenting the corresponding line. - cost = convolution_net(dict_dim, class_dim=class_dim) - # cost = stacked_lstm_net(dict_dim, class_dim=class_dim, stacked_num=3) + [cost, output] = convolution_net(dict_dim, class_dim=class_dim) + # [cost, output] = stacked_lstm_net(dict_dim, class_dim=class_dim, stacked_num=3) # create parameters parameters = paddle.parameters.create(cost) @@ -143,15 +141,22 @@ if __name__ == '__main__': sys.stdout.write('.') sys.stdout.flush() if isinstance(event, paddle.event.EndPass): + with open('./params_pass_%d.tar' % event.pass_id, 'w') as f: + parameters.to_tar(f) + result = trainer.test(reader=test_reader, feeding=feeding) print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics) # create trainer trainer = paddle.trainer.SGD( cost=cost, parameters=parameters, update_equation=adam_optimizer) + # Save the inference topology to protobuf. + inference_topology = paddle.topology.Topology(layers=output) + with open("./inference_topology.pkl", 'wb') as f: + inference_topology.serialize_for_inference(f) trainer.train( reader=train_reader, event_handler=event_handler, feeding=feeding, - num_passes=2) + num_passes=20) -- GitLab