Book demo understand sentiment distributed version

python/paddle/v2/fluid/tests/book_distribute/test_understand_sentiment_conv_dist.py

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+from __future__ import print_function
+import os
+import numpy as np
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+
+
+def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
+ hid_dim=32):
+ emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
+ conv_3 = fluid.nets.sequence_conv_pool(
+ input=emb,
+ num_filters=hid_dim,
+ filter_size=3,
+ act="tanh",
+ pool_type="sqrt")
+ conv_4 = fluid.nets.sequence_conv_pool(
+ input=emb,
+ num_filters=hid_dim,
+ filter_size=4,
+ act="tanh",
+ pool_type="sqrt")
+ prediction = fluid.layers.fc(input=[conv_3, conv_4],
+ size=class_dim,
+ act="softmax")
+ cost = fluid.layers.cross_entropy(input=prediction, label=label)
+ avg_cost = fluid.layers.mean(x=cost)
+ adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
+ optimize_ops, params_grads = adam_optimizer.minimize(avg_cost)
+ accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
+ return avg_cost, accuracy, accuracy.metrics[0], optimize_ops, params_grads
+
+
+def to_lodtensor(data, place):
+ seq_lens = [len(seq) for seq in data]
+ cur_len = 0
+ lod = [cur_len]
+ for l in seq_lens:
+ cur_len += l
+ lod.append(cur_len)
+ flattened_data = np.concatenate(data, axis=0).astype("int64")
+ flattened_data = flattened_data.reshape([len(flattened_data), 1])
+ res = fluid.LoDTensor()
+ res.set(flattened_data, place)
+ res.set_lod([lod])
+ return res
+
+
+def main():
+ BATCH_SIZE = 100
+ PASS_NUM = 5
+
+ word_dict = paddle.dataset.imdb.word_dict()
+ dict_dim = len(word_dict)
+ class_dim = 2
+
+ data = fluid.layers.data(
+ name="words", shape=[1], dtype="int64", lod_level=1)
+ label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+ cost, accuracy, acc_out, optimize_ops, params_grads = convolution_net(
+ data, label, input_dim=dict_dim, class_dim=class_dim)
+
+ train_data = paddle.batch(
+ paddle.reader.shuffle(
+ paddle.dataset.imdb.train(word_dict), buf_size=1000),
+ batch_size=BATCH_SIZE)
+ place = fluid.CPUPlace()
+ exe = fluid.Executor(place)
+
+ t = fluid.DistributeTranspiler()
+
+ # all parameter server endpoints list for spliting parameters
+ pserver_endpoints = os.getenv("PSERVERS")
+ # server endpoint for current node
+ current_endpoint = os.getenv("SERVER_ENDPOINT")
+ # run as trainer or parameter server
+ training_role = os.getenv(
+ "TRAINING_ROLE", "TRAINER") # get the training role: trainer/pserver
+ t.transpile(
+ optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
+
+ exe.run(fluid.default_startup_program())
+
+ if training_role == "PSERVER":
+ if not current_endpoint:
+ print("need env SERVER_ENDPOINT")
+ exit(1)
+ pserver_prog = t.get_pserver_program(current_endpoint, optimize_ops)
+ exe.run(pserver_prog)
+ elif training_role == "TRAINER":
+ trainer_prog = t.get_trainer_program()
+ feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
+
+ for pass_id in xrange(PASS_NUM):
+ accuracy.reset(exe)
+ for data in train_data():
+ cost_val, acc_val = exe.run(trainer_prog,
+ feed=feeder.feed(data),
+ fetch_list=[cost, acc_out])
+ pass_acc = accuracy.eval(exe)
+ print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
+ " pass_acc=" + str(pass_acc))
+ if cost_val < 1.0 and pass_acc > 0.8:
+ exit(0)
+ else:
+ print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
+
+
+if __name__ == '__main__':
+ main()