diff --git a/PaddleRec/gru4rec/net.py b/PaddleRec/gru4rec/net.py index 5f498569327911e7861afec9896e14e4f465d276..7e9f65bb4e589e02620248b17fd9851f216817a4 100644 --- a/PaddleRec/gru4rec/net.py +++ b/PaddleRec/gru4rec/net.py @@ -206,6 +206,7 @@ def infer_network(vocab_size, batch_size, hid_size, dropout=0.2): dtype="int64") emb_all_label = fluid.embedding( input=all_label, size=[vocab_size, hid_size], param_attr="emb") + emb_all_label = fluid.layers.squeeze(input=emb_all_label, axes=[1]) emb_all_label_drop = fluid.layers.dropout( emb_all_label, dropout_prob=dropout, is_test=True) diff --git a/PaddleRec/gru4rec/utils.py b/PaddleRec/gru4rec/utils.py index bae925ef32b37fb9476299a3a3bc9e4da1f4b2fd..8588a0f12c508070a8a4a00edb05fdae19c856c1 100644 --- a/PaddleRec/gru4rec/utils.py +++ b/PaddleRec/gru4rec/utils.py @@ -110,7 +110,7 @@ def prepare_data(file_dir, batch_size * 20) else: vocab_size = get_vocab_size(vocab_path) - reader = paddle.io.batch( + reader = fluid.io.batch( test( file_dir, buffer_size, data_type=DataType.SEQ), batch_size) return vocab_size, reader diff --git a/PaddleRec/ssr/utils.py b/PaddleRec/ssr/utils.py index 4fe9ef470ed0a2a5da7bef6a975f45e5a04ab18e..ce65e32f3c7fb40ecb1a366488b7583d9adb8c27 100644 --- a/PaddleRec/ssr/utils.py +++ b/PaddleRec/ssr/utils.py @@ -16,7 +16,7 @@ def construct_train_data(file_dir, vocab_path, batch_size): vocab_size = get_vocab_size(vocab_path) files = [file_dir + '/' + f for f in os.listdir(file_dir)] y_data = reader.YoochooseDataset(vocab_size) - train_reader = paddle.batch( + train_reader = fluid.io.batch( paddle.reader.shuffle( y_data.train(files), buf_size=batch_size * 100), batch_size=batch_size) @@ -27,7 +27,7 @@ def construct_test_data(file_dir, vocab_path, batch_size): vocab_size = get_vocab_size(vocab_path) files = [file_dir + '/' + f for f in os.listdir(file_dir)] y_data = reader.YoochooseDataset(vocab_size) - test_reader = paddle.batch(y_data.test(files), batch_size=batch_size) + test_reader = fluid.io.batch(y_data.test(files), batch_size=batch_size) return test_reader, vocab_size diff --git a/PaddleRec/word2vec/README.md b/PaddleRec/word2vec/README.md index 35d749b5c1ef8859c4e88da4f5d171269e66c2b5..1eceb5b2199d24752746f8b651aabd3af87232c4 100644 --- a/PaddleRec/word2vec/README.md +++ b/PaddleRec/word2vec/README.md @@ -35,7 +35,7 @@ mv 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tok ```bash mkdir data -wget https://paddlerec.bj.bcebos.com/word2vec/1-billion-word-language-modeling-benchmark-r13output.tar +wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/1-billion-word-language-modeling-benchmark-r13output.tar tar xvf 1-billion-word-language-modeling-benchmark-r13output.tar mv 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ data/ ``` @@ -44,7 +44,7 @@ mv 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tok ```bash mkdir data -wget https://paddlerec.bj.bcebos.com/word2vec/text.tar +wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/text.tar tar xvf text.tar mv text data/ ``` @@ -105,9 +105,9 @@ sh cluster_train.sh ```bash #全量数据集测试集 -wget https://paddlerec.bj.bcebos.com/word2vec/test_dir.tar +wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/test_dir.tar #样本数据集测试集 -wget https://paddlerec.bj.bcebos.com/word2vec/test_mid_dir.tar +wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/test_mid_dir.tar ``` 预测命令，注意词典名称需要加后缀"_word_to_id_", 此文件是预处理阶段生成的。 diff --git a/PaddleRec/word2vec/infer.py b/PaddleRec/word2vec/infer.py index 1b3290029d620d130d2fe7b7c2bcfd8bbeae54c2..2a24fe92a6e29bba779415dc612d780fd8aab72d 100644 --- a/PaddleRec/word2vec/infer.py +++ b/PaddleRec/word2vec/infer.py @@ -78,13 +78,13 @@ def infer_epoch(args, vocab_size, test_reader, use_cuda, i2w): b_size = len([dat[0] for dat in data]) wa = np.array( [dat[0] for dat in data]).astype("int64").reshape( - b_size, 1) + b_size) wb = np.array( [dat[1] for dat in data]).astype("int64").reshape( - b_size, 1) + b_size) wc = np.array( [dat[2] for dat in data]).astype("int64").reshape( - b_size, 1) + b_size) label = [dat[3] for dat in data] input_word = [dat[4] for dat in data] @@ -95,7 +95,7 @@ def infer_epoch(args, vocab_size, test_reader, use_cuda, i2w): "analogy_c": wc, "all_label": np.arange(vocab_size).reshape( - vocab_size, 1).astype("int64"), + vocab_size).astype("int64"), }, fetch_list=[pred.name, values], return_numpy=False) @@ -145,13 +145,13 @@ def infer_step(args, vocab_size, test_reader, use_cuda, i2w): b_size = len([dat[0] for dat in data]) wa = np.array( [dat[0] for dat in data]).astype("int64").reshape( - b_size, 1) + b_size) wb = np.array( [dat[1] for dat in data]).astype("int64").reshape( - b_size, 1) + b_size) wc = np.array( [dat[2] for dat in data]).astype("int64").reshape( - b_size, 1) + b_size) label = [dat[3] for dat in data] input_word = [dat[4] for dat in data] @@ -162,7 +162,7 @@ def infer_step(args, vocab_size, test_reader, use_cuda, i2w): "analogy_b": wb, "analogy_c": wc, "all_label": - np.arange(vocab_size).reshape(vocab_size, 1), + np.arange(vocab_size).reshape(vocab_size), }, fetch_list=[pred.name, values], return_numpy=False) diff --git a/PaddleRec/word2vec/net.py b/PaddleRec/word2vec/net.py index ab2abbc76bde8e03c9a6e1e0abb062aa467d2c91..b20b88fdeea9bdc1a2c74c0f196f94b03cb65ee2 100644 --- a/PaddleRec/word2vec/net.py +++ b/PaddleRec/word2vec/net.py @@ -23,10 +23,10 @@ import paddle.fluid as fluid def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5): datas = [] - input_word = fluid.layers.data(name="input_word", shape=[1], dtype='int64') - true_word = fluid.layers.data(name='true_label', shape=[1], dtype='int64') - neg_word = fluid.layers.data( - name="neg_label", shape=[neg_num], dtype='int64') + input_word = fluid.data(name="input_word", shape=[None, 1], dtype='int64') + true_word = fluid.data(name='true_label', shape=[None, 1], dtype='int64') + neg_word = fluid.data( + name="neg_label", shape=[None, neg_num], dtype='int64') datas.append(input_word) datas.append(true_word) @@ -37,7 +37,7 @@ def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5): words = fluid.layers.read_file(py_reader) init_width = 0.5 / embedding_size - input_emb = fluid.layers.embedding( + input_emb = fluid.embedding( input=words[0], is_sparse=is_sparse, size=[dict_size, embedding_size], @@ -45,38 +45,37 @@ def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5): name='emb', initializer=fluid.initializer.Uniform(-init_width, init_width))) - true_emb_w = fluid.layers.embedding( + true_emb_w = fluid.embedding( input=words[1], is_sparse=is_sparse, size=[dict_size, embedding_size], param_attr=fluid.ParamAttr( name='emb_w', initializer=fluid.initializer.Constant(value=0.0))) - true_emb_b = fluid.layers.embedding( + true_emb_b = fluid.embedding( input=words[1], is_sparse=is_sparse, size=[dict_size, 1], param_attr=fluid.ParamAttr( name='emb_b', initializer=fluid.initializer.Constant(value=0.0))) - neg_word_reshape = fluid.layers.reshape(words[2], shape=[-1, 1]) - neg_word_reshape.stop_gradient = True + input_emb = fluid.layers.squeeze(input=input_emb, axes=[1]) + true_emb_w = fluid.layers.squeeze(input=true_emb_w, axes=[1]) + true_emb_b = fluid.layers.squeeze(input=true_emb_b, axes=[1]) - neg_emb_w = fluid.layers.embedding( - input=neg_word_reshape, + neg_emb_w = fluid.embedding( + input=words[2], is_sparse=is_sparse, size=[dict_size, embedding_size], param_attr=fluid.ParamAttr( name='emb_w', learning_rate=1.0)) - - neg_emb_w_re = fluid.layers.reshape( - neg_emb_w, shape=[-1, neg_num, embedding_size]) - neg_emb_b = fluid.layers.embedding( - input=neg_word_reshape, + neg_emb_b = fluid.embedding( + input=words[2], is_sparse=is_sparse, size=[dict_size, 1], param_attr=fluid.ParamAttr( name='emb_b', learning_rate=1.0)) + neg_emb_b_vec = fluid.layers.reshape(neg_emb_b, shape=[-1, neg_num]) true_logits = fluid.layers.elementwise_add( fluid.layers.reduce_sum( @@ -87,7 +86,7 @@ def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5): input_emb_re = fluid.layers.reshape( input_emb, shape=[-1, 1, embedding_size]) neg_matmul = fluid.layers.matmul( - input_emb_re, neg_emb_w_re, transpose_y=True) + input_emb_re, neg_emb_w, transpose_y=True) neg_matmul_re = fluid.layers.reshape(neg_matmul, shape=[-1, neg_num]) neg_logits = fluid.layers.elementwise_add(neg_matmul_re, neg_emb_b_vec) #nce loss @@ -111,22 +110,21 @@ def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5): def infer_network(vocab_size, emb_size): - analogy_a = fluid.layers.data(name="analogy_a", shape=[1], dtype='int64') - analogy_b = fluid.layers.data(name="analogy_b", shape=[1], dtype='int64') - analogy_c = fluid.layers.data(name="analogy_c", shape=[1], dtype='int64') - all_label = fluid.layers.data( + analogy_a = fluid.data(name="analogy_a", shape=[None], dtype='int64') + analogy_b = fluid.data(name="analogy_b", shape=[None], dtype='int64') + analogy_c = fluid.data(name="analogy_c", shape=[None], dtype='int64') + all_label = fluid.data( name="all_label", - shape=[vocab_size, 1], - dtype='int64', - append_batch_size=False) - emb_all_label = fluid.layers.embedding( + shape=[vocab_size], + dtype='int64') + emb_all_label = fluid.embedding( input=all_label, size=[vocab_size, emb_size], param_attr="emb") - emb_a = fluid.layers.embedding( + emb_a = fluid.embedding( input=analogy_a, size=[vocab_size, emb_size], param_attr="emb") - emb_b = fluid.layers.embedding( + emb_b = fluid.embedding( input=analogy_b, size=[vocab_size, emb_size], param_attr="emb") - emb_c = fluid.layers.embedding( + emb_c = fluid.embedding( input=analogy_c, size=[vocab_size, emb_size], param_attr="emb") target = fluid.layers.elementwise_add( fluid.layers.elementwise_sub(emb_b, emb_a), emb_c) diff --git a/PaddleRec/word2vec/utils.py b/PaddleRec/word2vec/utils.py index 01cd04e493b09e880303d7b0c87f5ed71cf86357..14da068864f9a9ee31a99efba60c9e94413dcf79 100644 --- a/PaddleRec/word2vec/utils.py +++ b/PaddleRec/word2vec/utils.py @@ -22,7 +22,7 @@ def BuildWord_IdMap(dict_path): def prepare_data(file_dir, dict_path, batch_size): w2i, i2w = BuildWord_IdMap(dict_path) vocab_size = len(i2w) - reader = paddle.batch(test(file_dir, w2i), batch_size) + reader = fluid.io.batch(test(file_dir, w2i), batch_size) return vocab_size, reader, i2w