Merge branch 'develop' of https://github.com/PaddlePaddle/models into ctc_reader

.travis/unittest.sh

@@ -24,13 +24,24 @@ unittest(){
 trap 'abort' 0
 set -e
 
-for proj in */ ; do
+for proj in * ; do
+    if [ -d $proj ]; then
+        if [ "$proj" = "fluid" ]; then
+            for proj in fluid/* ; do
                 if [ -d $proj ]; then
                     unittest $proj
                     if [ $? != 0 ]; then
                         exit 1
                     fi
                 fi
+            done
+        else
+            unittest $proj
+            if [ $? != 0 ]; then
+                exit 1
+            fi
+        fi
+    fi
 done
 
 trap : 0

fluid/DeepASR/data_utils/augmentor/tests/data/global_mean_var_search26kHr

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fluid/DeepASR/data_utils/augmentor/tests/test_data_trans.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import sys
+import unittest
+import numpy as np
+import data_utils.augmentor.trans_mean_variance_norm as trans_mean_variance_norm
+import data_utils.augmentor.trans_add_delta as trans_add_delta
+import data_utils.augmentor.trans_splice as trans_splice
+
+
+class TestTransMeanVarianceNorm(unittest.TestCase):
+    """unit test for TransMeanVarianceNorm
+    """
+
+    def setUp(self):
+        self._file_path = "./data_utils/augmentor/tests/data/" \
+                          "global_mean_var_search26kHr"
+
+    def test(self):
+        feature = np.zeros((2, 120), dtype="float32")
+        feature.fill(1)
+        trans = trans_mean_variance_norm.TransMeanVarianceNorm(self._file_path)
+        (feature1, label1) = trans.perform_trans((feature, None))
+        (mean, var) = trans.get_mean_var()
+        feature_flat1 = feature1.flatten()
+        feature_flat = feature.flatten()
+        one = np.ones((1), dtype="float32")
+        for idx, val in enumerate(feature_flat1):
+            cur_idx = idx % 120
+            self.assertAlmostEqual(val, (one[0] - mean[cur_idx]) * var[cur_idx])
+
+
+class TestTransAddDelta(unittest.TestCase):
+    """unit test TestTransAddDelta
+    """
+
+    def test_regress(self):
+        """test regress
+        """
+        feature = np.zeros((14, 120), dtype="float32")
+        feature[0:5, 0:40].fill(1)
+        feature[0 + 5, 0:40].fill(1)
+        feature[1 + 5, 0:40].fill(2)
+        feature[2 + 5, 0:40].fill(3)
+        feature[3 + 5, 0:40].fill(4)
+        feature[8:14, 0:40].fill(4)
+        trans = trans_add_delta.TransAddDelta()
+        feature = feature.reshape((14 * 120))
+        trans._regress(feature, 5 * 120, feature, 5 * 120 + 40, 40, 4, 120)
+        trans._regress(feature, 5 * 120 + 40, feature, 5 * 120 + 80, 40, 4, 120)
+        feature = feature.reshape((14, 120))
+        tmp_feature = feature[5:5 + 4, :]
+        self.assertAlmostEqual(1.0, tmp_feature[0][0])
+        self.assertAlmostEqual(0.24, tmp_feature[0][119])
+        self.assertAlmostEqual(2.0, tmp_feature[1][0])
+        self.assertAlmostEqual(0.13, tmp_feature[1][119])
+        self.assertAlmostEqual(3.0, tmp_feature[2][0])
+        self.assertAlmostEqual(-0.13, tmp_feature[2][119])
+        self.assertAlmostEqual(4.0, tmp_feature[3][0])
+        self.assertAlmostEqual(-0.24, tmp_feature[3][119])
+
+    def test_perform(self):
+        """test perform
+        """
+        feature = np.zeros((4, 40), dtype="float32")
+        feature[0, 0:40].fill(1)
+        feature[1, 0:40].fill(2)
+        feature[2, 0:40].fill(3)
+        feature[3, 0:40].fill(4)
+        trans = trans_add_delta.TransAddDelta()
+        (feature, label) = trans.perform_trans((feature, None))
+        self.assertAlmostEqual(feature.shape[0], 4)
+        self.assertAlmostEqual(feature.shape[1], 120)
+        self.assertAlmostEqual(1.0, feature[0][0])
+        self.assertAlmostEqual(0.24, feature[0][119])
+        self.assertAlmostEqual(2.0, feature[1][0])
+        self.assertAlmostEqual(0.13, feature[1][119])
+        self.assertAlmostEqual(3.0, feature[2][0])
+        self.assertAlmostEqual(-0.13, feature[2][119])
+        self.assertAlmostEqual(4.0, feature[3][0])
+        self.assertAlmostEqual(-0.24, feature[3][119])
+
+
+class TestTransSplict(unittest.TestCase):
+    """unit test Test TransSplict
+    """
+
+    def test_perfrom(self):
+        feature = np.zeros((8, 10), dtype="float32")
+        for i in xrange(feature.shape[0]):
+            feature[i, :].fill(i)
+
+        trans = trans_splice.TransSplice()
+        (feature, label) = trans.perform_trans((feature, None))
+        self.assertEqual(feature.shape[1], 110)
+
+        for i in xrange(8):
+            nzero_num = 5 - i
+            cur_val = 0.0
+            if nzero_num < 0:
+                cur_val = i - 5 - 1
+            for j in xrange(11):
+                if j <= nzero_num:
+                    for k in xrange(10):
+                        self.assertAlmostEqual(feature[i][j * 10 + k], cur_val)
+                else:
+                    if cur_val < 7:
+                        cur_val += 1.0
+                    for k in xrange(10):
+                        self.assertAlmostEqual(feature[i][j * 10 + k], cur_val)
+
+
+if __name__ == '__main__':
+    unittest.main()

fluid/DeepASR/data_utils/augmentor/trans_add_delta.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import math
+import copy
+
+
+class TransAddDelta(object):
+    """ add delta of feature data 
+        trans feature for shape(a, b) to shape(a, b * 3)
+
+        Attributes:
+            _norder(int):
+            _window(int):
+    """
+
+    def __init__(self, norder=2, nwindow=2):
+        """ init construction
+            Args:
+                norder: default 2 
+                nwindow: default 2
+        """
+        self._norder = norder
+        self._nwindow = nwindow
+
+    def perform_trans(self, sample):
+        """ add delta for feature
+            trans feature shape from (a,b) to (a, b * 3)
+
+            Args: 
+                sample(object,tuple): contain feature numpy and label numpy
+            Returns:
+                (feature, label)
+        """
+        (feature, label) = sample
+        frame_dim = feature.shape[1]
+        d_frame_dim = frame_dim * 3
+        head_filled = 5
+        tail_filled = 5
+        mat = np.zeros(
+            (feature.shape[0] + head_filled + tail_filled, d_frame_dim),
+            dtype="float32")
+        #copy first frame
+        for i in xrange(head_filled):
+            np.copyto(mat[i, 0:frame_dim], feature[0, :])
+
+        np.copyto(mat[head_filled:head_filled + feature.shape[0], 0:frame_dim],
+                  feature[:, :])
+
+        # copy last frame
+        for i in xrange(head_filled + feature.shape[0], mat.shape[0], 1):
+            np.copyto(mat[i, 0:frame_dim], feature[feature.shape[0] - 1, :])
+
+        nframe = feature.shape[0]
+        start = head_filled
+        tmp_shape = mat.shape
+        mat = mat.reshape((tmp_shape[0] * tmp_shape[1]))
+        self._regress(mat, start * d_frame_dim, mat,
+                      start * d_frame_dim + frame_dim, frame_dim, nframe,
+                      d_frame_dim)
+        self._regress(mat, start * d_frame_dim + frame_dim, mat,
+                      start * d_frame_dim + 2 * frame_dim, frame_dim, nframe,
+                      d_frame_dim)
+        mat.shape = tmp_shape
+        return (mat[head_filled:mat.shape[0] - tail_filled, :], label)
+
+    def _regress(self, data_in, start_in, data_out, start_out, size, n, step):
+        """ regress
+            Args:
+                data_in: in data
+                start_in: start index of data_in
+                data_out: out data
+                start_out: start index of data_out
+                size: frame dimentional
+                n: frame num
+                step: 3 * (frame num)
+            Returns:
+                None
+        """
+        sigma_t2 = 0.0
+        delta_window = self._nwindow
+        for t in xrange(1, delta_window + 1):
+            sigma_t2 += t * t
+
+        sigma_t2 *= 2.0
+        for i in xrange(n):
+            fp1 = start_in
+            fp2 = start_out
+            for j in xrange(size):
+                back = fp1
+                forw = fp1
+                sum = 0.0
+                for t in xrange(1, delta_window + 1):
+                    back -= step
+                    forw += step
+                    sum += t * (data_in[forw] - data_in[back])
+
+                data_out[fp2] = sum / sigma_t2
+                fp1 += 1
+                fp2 += 1
+            start_in += step
+            start_out += step

fluid/DeepASR/data_utils/augmentor/trans_mean_variance_norm.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import math
+
+
+class TransMeanVarianceNorm(object):
+    """ normalization of mean variance for feature data 
+        Attributes:
+            _mean(numpy.array): the feature mean vector
+            _var(numpy.array): the feature variance 
+    """
+
+    def __init__(self, snorm_path):
+        """init construction
+            Args:
+                snorm_path: the path of mean and variance
+        """
+        self._mean = None
+        self._var = None
+        self._load_norm(snorm_path)
+
+    def _load_norm(self, snorm_path):
+        """ load mean var file
+            Args: 
+                snorm_path(str):the file path
+        """
+        lLines = open(snorm_path).readlines()
+        nLen = len(lLines)
+        self._mean = np.zeros((nLen), dtype="float32")
+        self._var = np.zeros((nLen), dtype="float32")
+        self._nLen = nLen
+        for nidx, l in enumerate(lLines):
+            s = l.split()
+            assert len(s) == 2
+            self._mean[nidx] = float(s[0])
+            self._var[nidx] = 1.0 / math.sqrt(float(s[1]))
+            if self._var[nidx] > 100000.0:
+                self._var[nidx] = 100000.0
+
+    def get_mean_var(self):
+        """ get mean and var 
+            Args:
+            Returns:
+                (mean, var)
+        """
+        return (self._mean, self._var)
+
+    def perform_trans(self, sample):
+        """ feature = (feature - mean) * var
+            Args:
+                sample(object):input sample, contain feature numpy and label numpy
+            Returns:
+                (feature, label)
+        """
+        (feature, label) = sample
+        shape = feature.shape
+        assert len(shape) == 2
+        nfeature_len = shape[0] * shape[1]
+        assert nfeature_len % self._nLen == 0
+        ncur_idx = 0
+        feature = feature.reshape((nfeature_len))
+        while ncur_idx < nfeature_len:
+            block = feature[ncur_idx:ncur_idx + self._nLen]
+            block = (block - self._mean) * self._var
+            feature[ncur_idx:ncur_idx + self._nLen] = block
+            ncur_idx += self._nLen
+        feature = feature.reshape(shape)
+        return (feature, label)

fluid/DeepASR/data_utils/augmentor/trans_splice.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import math
+
+
+class TransSplice(object):
+    """ copy feature context to construct new feature
+        expand feature data from shape (frame_num, frame_dim) 
+        to shape (frame_num, frame_dim * 11)
+
+        Attributes:
+            _nleft_context(int): copy left context number
+            _nright_context(int): copy right context number
+    """
+
+    def __init__(self, nleft_context=5, nright_context=5):
+        """ init construction
+            Args:
+                nleft_context(int):
+                nright_context(int):
+        """
+        self._nleft_context = nleft_context
+        self._nright_context = nright_context
+
+    def perform_trans(self, sample):
+        """ copy feature context 
+        Args:
+            sample(object): input sample(feature, label)
+        Return:
+            (feature, label)
+        """
+        (feature, label) = sample
+        nframe_num = feature.shape[0]
+        nframe_dim = feature.shape[1]
+        nnew_frame_dim = nframe_dim * (
+            self._nleft_context + self._nright_context + 1)
+        mat = np.zeros(
+            (nframe_num + self._nleft_context + self._nright_context,
+             nframe_dim),
+            dtype="float32")
+        ret = np.zeros((nframe_num, nnew_frame_dim), dtype="float32")
+
+        #copy left
+        for i in xrange(self._nleft_context):
+            mat[i, :] = feature[0, :]
+
+        #copy middle 
+        mat[self._nleft_context:self._nleft_context +
+            nframe_num, :] = feature[:, :]
+
+        #copy right
+        for i in xrange(self._nright_context):
+            mat[i + self._nleft_context + nframe_num, :] = feature[-1, :]
+
+        mat = mat.reshape(mat.shape[0] * mat.shape[1])
+        ret = ret.reshape(ret.shape[0] * ret.shape[1])
+        for i in xrange(nframe_num):
+            np.copyto(ret[i * nnew_frame_dim:(i + 1) * nnew_frame_dim],
+                      mat[i * nframe_dim:i * nframe_dim + nnew_frame_dim])
+        ret = ret.reshape((nframe_num, nnew_frame_dim))
+        return (ret, label)

fluid/DeepASR/data_utils/data_reader.py

+"""This model read the sample from disk. 
+   use multiprocessing to reading samples
+   push samples from one block to multiprocessing queue 
+   Todos:
+        1. multiprocess read block from disk
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import random
+import Queue
+import numpy as np
+import struct
+import data_utils.augmentor.trans_mean_variance_norm as trans_mean_variance_norm
+import data_utils.augmentor.trans_add_delta as trans_add_delta
+
+
+class OneBlock(object):
+    """ struct for one block :
+        contain label, label desc, feature, feature_desc
+
+        Attributes:
+            label(str) :  label path of one block
+            label_desc(str) : label description path of one block
+            feature(str) : feature path of on block
+            feature_desc(str) : feature description path of on block
+    """
+
+    def __init__(self):
+        """the constructor."""
+
+        self.label = "label"
+        self.label_desc = "label_desc"
+        self.feature = "feature"
+        self.feature_desc = "feature_desc"
+
+
+class DataRead(object):
+    """
+    Attributes:
+        _lblock(obj:`OneBlock`) : the list of OneBlock
+        _ndrop_sentence_len(int): dropout the sentence which's frame_num large than _ndrop_sentence_len  
+        _que_sample(obj:`Queue`): sample buffer
+        _nframe_dim(int): the batch sample frame_dim(todo remove)
+        _nstart_block_idx(int): the start block id
+        _nload_block_num(int): the block num
+    """
+
+    def __init__(self, sfeature_lst, slabel_lst, ndrop_sentence_len=512):
+        """
+        Args:
+            sfeature_lst(str):feature lst path
+            slabel_lst(str):label lst path
+        Returns:
+            None
+        """
+        self._lblock = []
+        self._ndrop_sentence_len = ndrop_sentence_len
+        self._que_sample = Queue.Queue()
+        self._nframe_dim = 120 * 11
+        self._nstart_block_idx = 0
+        self._nload_block_num = 1
+        self._ndrop_frame_len = 256
+
+        self._load_list(sfeature_lst, slabel_lst)
+
+    def _load_list(self, sfeature_lst, slabel_lst):
+        """ load list and shuffle
+        Args:
+            sfeature_lst(str):feature lst path
+            slabel_lst(str):label lst path
+        Returns:
+            None
+        """
+        lfeature = open(sfeature_lst).readlines()
+        llabel = open(slabel_lst).readlines()
+        assert len(llabel) == len(lfeature)
+        for i in range(0, len(lfeature), 2):
+            one_block = OneBlock()
+
+            one_block.label = llabel[i]
+            one_block.label_desc = llabel[i + 1]
+            one_block.feature = lfeature[i]
+            one_block.feature_desc = lfeature[i + 1]
+            self._lblock.append(one_block)
+
+        random.shuffle(self._lblock)
+
+    def _load_one_block(self, lsample, id):
+        """read one block by id and push load sample in list lsample 
+        Args:
+            lsample(list): return sample list
+            id(int): block id 
+        Returns:
+            None
+        """
+        if id >= len(self._lblock):
+            return
+
+        slabel_path = self._lblock[id].label.strip()
+        slabel_desc_path = self._lblock[id].label_desc.strip()
+        sfeature_path = self._lblock[id].feature.strip()
+        sfeature_desc_path = self._lblock[id].feature_desc.strip()
+
+        llabel_line = open(slabel_desc_path).readlines()
+        lfeature_line = open(sfeature_desc_path).readlines()
+
+        file_lable_bin = open(slabel_path, "r")
+        file_feature_bin = open(sfeature_path, "r")
+
+        sample_num = int(llabel_line[0].split()[1])
+        assert sample_num == int(lfeature_line[0].split()[1])
+
+        llabel_line = llabel_line[1:]
+        lfeature_line = lfeature_line[1:]
+
+        for i in range(sample_num):
+            # read label 
+            llabel_split = llabel_line[i].split()
+            nlabel_start = int(llabel_split[2])
+            nlabel_size = int(llabel_split[3])
+            nlabel_frame_num = int(llabel_split[4])
+
+            file_lable_bin.seek(nlabel_start, 0)
+            label_bytes = file_lable_bin.read(nlabel_size)
+            assert nlabel_frame_num * 4 == len(label_bytes)
+            label_array = struct.unpack('I' * nlabel_frame_num, label_bytes)
+            label_data = np.array(label_array, dtype="int64")
+            label_data = label_data.reshape((nlabel_frame_num, 1))
+
+            # read feature
+            lfeature_split = lfeature_line[i].split()
+            nfeature_start = int(lfeature_split[2])
+            nfeature_size = int(lfeature_split[3])
+            nfeature_frame_num = int(lfeature_split[4])
+            nfeature_frame_dim = int(lfeature_split[5])
+
+            file_feature_bin.seek(nfeature_start, 0)
+            feature_bytes = file_feature_bin.read(nfeature_size)
+            assert nfeature_frame_num * nfeature_frame_dim * 4 == len(
+                feature_bytes)
+            feature_array = struct.unpack('f' * nfeature_frame_num *
+                                          nfeature_frame_dim, feature_bytes)
+            feature_data = np.array(feature_array, dtype="float32")
+            feature_data = feature_data.reshape(
+                (nfeature_frame_num, nfeature_frame_dim))
+
+            #drop long sentence
+            if self._ndrop_frame_len < feature_data.shape[0]:
+                continue
+            lsample.append((feature_data, label_data))
+
+    def get_one_batch(self, nbatch_size):
+        """construct one batch(feature, label), batch size is nbatch_size
+        Args:
+            nbatch_size(int): batch size
+        Returns:
+            None
+        """
+        if self._que_sample.empty():
+            lsample = self._load_block(
+                range(self._nstart_block_idx, self._nstart_block_idx +
+                      self._nload_block_num, 1))
+            self._move_sample(lsample)
+            self._nstart_block_idx += self._nload_block_num
+
+        if self._que_sample.empty():
+            self._nstart_block_idx = 0
+            return None
+        #cal all frame num
+        ncur_len = 0
+        lod = [0]
+        samples = []
+        bat_feature = np.zeros((nbatch_size, self._nframe_dim))
+        for i in range(nbatch_size):
+            # empty clear zero 
+            if self._que_sample.empty():
+                self._nstart_block_idx = 0
+            # copy
+            else:
+                (one_feature, one_label) = self._que_sample.get()
+                samples.append((one_feature, one_label))
+                ncur_len += one_feature.shape[0]
+                lod.append(ncur_len)
+
+        bat_feature = np.zeros((ncur_len, self._nframe_dim), dtype="float32")
+        bat_label = np.zeros((ncur_len, 1), dtype="int64")
+        ncur_len = 0
+        for sample in samples:
+            one_feature = sample[0]
+            one_label = sample[1]
+            nframe_num = one_feature.shape[0]
+            nstart = ncur_len
+            nend = ncur_len + nframe_num
+            bat_feature[nstart:nend, :] = one_feature
+            bat_label[nstart:nend, :] = one_label
+            ncur_len += nframe_num
+        return (bat_feature, bat_label, lod)
+
+    def set_trans(self, ltrans):
+        """ set transform list
+        Args:
+            ltrans(list): data tranform list
+        Returns:
+            None
+        """
+        self._ltrans = ltrans
+
+    def _load_block(self, lblock_id):
+        """read blocks
+        """
+        lsample = []
+        for id in lblock_id:
+            self._load_one_block(lsample, id)
+
+        # transform sample
+        for (nidx, sample) in enumerate(lsample):
+            for trans in self._ltrans:
+                sample = trans.perform_trans(sample)
+            lsample[nidx] = sample
+
+        return lsample
+
+    def load_block(self, lblock_id):
+        """read blocks
+        Args:
+            lblock_id(list):the block list id
+        Returns:
+            None
+        """
+        lsample = []
+        for id in lblock_id:
+            self._load_one_block(lsample, id)
+
+        # transform sample
+        for (nidx, sample) in enumerate(lsample):
+            for trans in self._ltrans:
+                sample = trans.perform_trans(sample)
+            lsample[nidx] = sample
+
+        return lsample
+
+    def _move_sample(self, lsample):
+        """move sample to queue
+        Args:
+            lsample(list): one block of samples read from disk
+        Returns:
+            None
+        """
+        # random
+        random.shuffle(lsample)
+
+        for sample in lsample:
+            self._que_sample.put(sample)

fluid/DeepASR/data_utils/util.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+def to_lodtensor(data, place):
+    """convert tensor to lodtensor
+    """
+    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 = numpy.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 lodtensor_to_ndarray(lod_tensor):
+    """conver lodtensor to ndarray
+    """
+    dims = lod_tensor.get_dims()
+    ret = np.zeros(shape=dims).astype('float32')
+    for i in xrange(np.product(dims)):
+        ret.ravel()[i] = lod_tensor.get_float_element(i)
+    return ret, lod_tensor.lod()

fluid/DeepASR/stacked_dynamic_lstm.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import argparse
+import time
+
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.profiler as profiler
+import data_utils.trans_mean_variance_norm as trans_mean_variance_norm
+import data_utils.trans_add_delta as trans_add_delta
+import data_utils.trans_splice as trans_splice
+import data_utils.data_reader as reader
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("LSTM model benchmark.")
+    parser.add_argument(
+        '--batch_size',
+        type=int,
+        default=32,
+        help='The sequence number of a batch data. (default: %(default)d)')
+    parser.add_argument(
+        '--stacked_num',
+        type=int,
+        default=5,
+        help='Number of lstm layers to stack. (default: %(default)d)')
+    parser.add_argument(
+        '--proj_dim',
+        type=int,
+        default=512,
+        help='Project size of lstm unit. (default: %(default)d)')
+    parser.add_argument(
+        '--hidden_dim',
+        type=int,
+        default=1024,
+        help='Hidden size of lstm unit. (default: %(default)d)')
+    parser.add_argument(
+        '--pass_num',
+        type=int,
+        default=100,
+        help='Epoch number to train. (default: %(default)d)')
+    parser.add_argument(
+        '--learning_rate',
+        type=float,
+        default=0.002,
+        help='Learning rate used to train. (default: %(default)f)')
+    parser.add_argument(
+        '--device',
+        type=str,
+        default='GPU',
+        choices=['CPU', 'GPU'],
+        help='The device type. (default: %(default)s)')
+    parser.add_argument(
+        '--infer_only', action='store_true', help='If set, run forward only.')
+    parser.add_argument(
+        '--use_cprof', action='store_true', help='If set, use cProfile.')
+    parser.add_argument(
+        '--use_nvprof',
+        action='store_true',
+        help='If set, use nvprof for CUDA.')
+    parser.add_argument('--mean_var', type=str, help='mean var path')
+    parser.add_argument('--feature_lst', type=str, help='mean var path')
+    parser.add_argument('--label_lst', type=str, help='mean var path')
+    args = parser.parse_args()
+    return args
+
+
+def print_arguments(args):
+    vars(args)['use_nvprof'] = (vars(args)['use_nvprof'] and
+                                vars(args)['device'] == 'GPU')
+    print('-----------  Configuration Arguments -----------')
+    for arg, value in sorted(vars(args).iteritems()):
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------------')
+
+
+def dynamic_lstmp_model(hidden_dim,
+                        proj_dim,
+                        stacked_num,
+                        class_num=1749,
+                        is_train=True):
+    feature = fluid.layers.data(
+        name="feature", shape=[-1, 120 * 11], dtype="float32", lod_level=1)
+
+    seq_conv1 = fluid.layers.sequence_conv(
+        input=feature,
+        num_filters=1024,
+        filter_size=3,
+        filter_stride=1,
+        bias_attr=True)
+    bn1 = fluid.layers.batch_norm(
+        input=seq_conv1,
+        act="sigmoid",
+        is_test=False,
+        momentum=0.9,
+        epsilon=1e-05,
+        data_layout='NCHW')
+
+    stack_input = bn1
+    for i in range(stacked_num):
+        fc = fluid.layers.fc(input=stack_input,
+                             size=hidden_dim * 4,
+                             bias_attr=True)
+        proj, cell = fluid.layers.dynamic_lstmp(
+            input=fc,
+            size=hidden_dim * 4,
+            proj_size=proj_dim,
+            bias_attr=True,
+            use_peepholes=True,
+            is_reverse=False,
+            cell_activation="tanh",
+            proj_activation="tanh")
+        bn = fluid.layers.batch_norm(
+            input=proj,
+            act="sigmoid",
+            is_test=False,
+            momentum=0.9,
+            epsilon=1e-05,
+            data_layout='NCHW')
+        stack_input = bn
+
+    prediction = fluid.layers.fc(input=stack_input,
+                                 size=class_num,
+                                 act='softmax')
+
+    if not is_train: return feature, prediction
+
+    label = fluid.layers.data(
+        name="label", shape=[-1, 1], dtype="int64", lod_level=1)
+    cost = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+
+    return prediction, label, avg_cost
+
+
+def train(args):
+    if args.use_cprof:
+        pr = cProfile.Profile()
+        pr.enable()
+
+    prediction, label, avg_cost = dynamic_lstmp_model(
+        args.hidden_dim, args.proj_dim, args.stacked_num)
+
+    adam_optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
+    adam_optimizer.minimize(avg_cost)
+
+    accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
+
+    # clone from default main program
+    inference_program = fluid.default_main_program().clone()
+    with fluid.program_guard(inference_program):
+        test_accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
+        test_target = [avg_cost] + test_accuracy.metrics + test_accuracy.states
+        inference_program = fluid.io.get_inference_program(test_target)
+
+    place = fluid.CPUPlace() if args.device == 'CPU' else fluid.CUDAPlace(0)
+    exe = fluid.Executor(place)
+    exe.run(fluid.default_startup_program())
+
+    ltrans = [
+        trans_add_delta.TransAddDelta(2, 2),
+        trans_mean_variance_norm.TransMeanVarianceNorm(args.mean_var),
+        trans_splice.TransSplice()
+    ]
+
+    data_reader = reader.DataRead(args.feature_lst, args.label_lst)
+    data_reader.set_trans(ltrans)
+
+    res_feature = fluid.LoDTensor()
+    res_label = fluid.LoDTensor()
+    for pass_id in xrange(args.pass_num):
+        pass_start_time = time.time()
+        words_seen = 0
+        accuracy.reset(exe)
+        batch_id = 0
+        while True:
+            # load_data
+            one_batch = data_reader.get_one_batch(args.batch_size)
+            if one_batch == None:
+                break
+            (bat_feature, bat_label, lod) = one_batch
+            res_feature.set(bat_feature, place)
+            res_feature.set_lod([lod])
+            res_label.set(bat_label, place)
+            res_label.set_lod([lod])
+
+            batch_id += 1
+
+            words_seen += lod[-1]
+
+            loss, acc = exe.run(
+                fluid.default_main_program(),
+                feed={"feature": res_feature,
+                      "label": res_label},
+                fetch_list=[avg_cost] + accuracy.metrics,
+                return_numpy=False)
+            train_acc = accuracy.eval(exe)
+            print("acc:", lodtensor_to_ndarray(loss))
+
+        pass_end_time = time.time()
+        time_consumed = pass_end_time - pass_start_time
+        words_per_sec = words_seen / time_consumed
+
+
+def lodtensor_to_ndarray(lod_tensor):
+    dims = lod_tensor.get_dims()
+    ret = np.zeros(shape=dims).astype('float32')
+    for i in xrange(np.product(dims)):
+        ret.ravel()[i] = lod_tensor.get_float_element(i)
+    return ret, lod_tensor.lod()
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    print_arguments(args)
+
+    if args.infer_only:
+        pass
+    else:
+        if args.use_nvprof and args.device == 'GPU':
+            with profiler.cuda_profiler("cuda_profiler.txt", 'csv') as nvprof:
+                train(args)
+        else:
+            train(args)

youtube_recall/data_processor.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import sys
+import argparse
+import os
+import cPickle
+
+from utils import logger
+"""
+This script will output 2 files:
+1. feature_dict.pkl
+2. item_freq.pkl
+"""
+
+
+class FeatureGenerator(object):
+    """
+    Encode feature values with low-frequency filtering.
+    """
+
+    def __init__(self, feat_appear_limit=20):
+        """
+        @feat_appear_limit: int
+        """
+        self._dic = None  # feature value --> id
+        self._count = None  # numbers of appearances of feature values
+        self._feat_appear_limit = feat_appear_limit
+
+    def add_feat_val(self, feat_val):
+        """
+        Add feature values and count numbers of its appearance. 
+        """
+        if self._count is None:
+            self._count = {'<unk>': 0}
+        if feat_val == "NULL":
+            feat_val = '<unk>'
+        if feat_val not in self._count:
+            self._count[feat_val] = 1
+        else:
+            self._count[feat_val] += 1
+            self._count['<unk>'] += 1
+
+    def _filter_feat(self):
+        """
+        Filter low-frequency feature values.
+        """
+        self._items = filter(lambda x: x[1] > self._feat_appear_limit,
+                             self._count.items())
+        self._items.sort(key=lambda x: x[1], reverse=True)
+
+    def _build_dict(self):
+        """
+        Build feature values --> ids dict.
+        """
+        self._dic = {}
+        self._filter_feat()
+        for i in xrange(len(self._items)):
+            self._dic[self._items[i][0]] = i
+        self.dim = len(self._dic)
+
+    def get_feat_id(self, feat_val):
+        """
+        Get id of feature value after encoding.
+        """
+        # build dict
+        if self._dic is None:
+            self._build_dict()
+
+        # find id
+        if feat_val in self._dic:
+            return self._dic[feat_val]
+        else:
+            return self._dic['<unk>']
+
+    def get_dim(self):
+        """
+        Get dim.
+        """
+        # build dict
+        if self._dic is None:
+            self._build_dict()
+        return len(self._dic)
+
+    def get_dict(self):
+        """
+        Get dict.
+        """
+        # build dict
+        if self._dic is None:
+            self._build_dict()
+        return self._dic
+
+    def get_total_count(self):
+        """
+        Compute total num of count.
+        """
+        total_count = 0
+        for i in xrange(len(self._items)):
+            feat_val = self._items[i][0]
+            c = self._items[i][1]
+            total_count += c
+        return total_count
+
+    def count_iterator(self):
+        """
+        Iterate feature values and its num of appearance.
+        """
+        for i in xrange(len(self._items)):
+            yield self._items[i][0], self._items[i][1]
+
+    def __repr__(self):
+        """
+        """
+        return '<FeatureGenerator %d>' % self._dim
+
+
+def scan_build_dict(data_path, features_dict):
+    """
+    Scan the raw data and add all feature values.
+    """
+    logger.info('scan data set')
+
+    with open(data_path, 'r') as f:
+        for (line_id, line) in enumerate(f):
+            fields = line.strip('\n').split('\t')
+            user_id = fields[0]
+            province = fields[1]
+            features_dict['province'].add_feat_val(province)
+            city = fields[2]
+            features_dict['city'].add_feat_val(city)
+            item_infos = fields[3]
+            phone = fields[4]
+            features_dict['phone'].add_feat_val(phone)
+            for item_info in item_infos.split(";"):
+                item_info_array = item_info.split(":")
+                item = item_info_array[0]
+                features_dict['history_clicked_items'].add_feat_val(item)
+                features_dict['user_id'].add_feat_val(user_id)
+                category = item_info_array[1]
+                features_dict['history_clicked_categories'].add_feat_val(
+                    category)
+                tags = item_info_array[2]
+                for tag in tags.split("_"):
+                    features_dict['history_clicked_tags'].add_feat_val(tag)
+
+
+def parse_args():
+    """
+    parse arguments
+    """
+    parser = argparse.ArgumentParser(
+        description="PaddlePaddle Youtube Recall Model Example")
+    parser.add_argument(
+        '--train_set_path',
+        type=str,
+        required=True,
+        help="path of the train set")
+    parser.add_argument(
+        '--output_dir', type=str, required=True, help="directory to output")
+    parser.add_argument(
+        '--feat_appear_limit',
+        type=int,
+        default=20,
+        help="the minimum number of feature values appears (default: 20)")
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    # check argument
+    assert os.path.exists(
+        args.train_set_path), 'The train set path does not exist.'
+
+    # features used
+    features = [
+        'user_id', 'province', 'city', 'phone', 'history_clicked_items',
+        'history_clicked_tags', 'history_clicked_categories'
+    ]
+
+    # init feature generators
+    features_dict = {}
+    for feature in features:
+        features_dict[feature] = FeatureGenerator(
+            feat_appear_limit=args.feat_appear_limit)
+
+    # scan data for building dict
+    scan_build_dict(args.train_set_path, features_dict)
+
+    # generate feature_dict.pkl
+    feature_encoding_dict = {}
+    for feature in features:
+        d = features_dict[feature].get_dict()
+        feature_encoding_dict[feature] = d
+        logger.info('Feature:%s, dimension is %d' % (feature, len(d)))
+    output_dict_path = os.path.join(args.output_dir, 'feature_dict.pkl')
+    with open(output_dict_path, "w") as f:
+        cPickle.dump(feature_encoding_dict, f, -1)
+
+    # generate item_freq.pkl
+    item_freq_list = []
+    g = features_dict['history_clicked_items']
+    total_count = g.get_total_count()
+    for feat_val, feat_count in g.count_iterator():
+        item_freq_list.append(float(feat_count) / total_count)
+    logger.info('item_freq, dimension is %d' % (len(item_freq_list)))
+    output_item_freq_path = os.path.join(args.output_dir, 'item_freq.pkl')
+    with open(output_item_freq_path, "w") as f:
+        cPickle.dump(item_freq_list, f, -1)
+
+    logger.info('Complete!')

youtube_recall/infer.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import os
+import gzip
+import paddle.v2 as paddle
+import argparse
+import cPickle
+
+from reader import Reader
+from network_conf import DNNmodel
+from utils import logger
+
+
+def parse_args():
+    """
+    parse arguments
+    :return:
+    """
+    parser = argparse.ArgumentParser(
+        description="PaddlePaddle Youtube Recall Model Example")
+    parser.add_argument(
+        '--infer_set_path',
+        type=str,
+        required=True,
+        help="path of the infer set")
+    parser.add_argument(
+        '--model_path', type=str, required=True, help="path of the model")
+    parser.add_argument(
+        '--feature_dict',
+        type=str,
+        required=True,
+        help="path of feature_dict.pkl")
+    parser.add_argument(
+        '--batch_size',
+        type=int,
+        default=50,
+        help="size of mini-batch (default:50)")
+    return parser.parse_args()
+
+
+def infer():
+    """
+    infer
+    """
+    args = parse_args()
+
+    # check argument
+    assert os.path.exists(
+        args.infer_set_path), 'The infer_set_path path does not exist.'
+    assert os.path.exists(
+        args.model_path), 'The model_path path does not exist.'
+    assert os.path.exists(
+        args.feature_dict), 'The feature_dict path does not exist.'
+
+    paddle.init(use_gpu=False, trainer_count=1)
+
+    with open(args.feature_dict) as f:
+        feature_dict = cPickle.load(f)
+
+    nid_dict = feature_dict['history_clicked_items']
+    nid_to_word = dict((v, k) for k, v in nid_dict.items())
+
+    # load the trained model.
+    with gzip.open(args.model_path) as f:
+        parameters = paddle.parameters.Parameters.from_tar(f)
+
+    # build model
+    prediction_layer, fc = DNNmodel(
+        dnn_layer_dims=[256, 31], feature_dict=feature_dict,
+        is_infer=True).model_cost
+    inferer = paddle.inference.Inference(
+        output_layer=[prediction_layer, fc], parameters=parameters)
+
+    reader = Reader(feature_dict)
+    test_batch = []
+    for idx, item in enumerate(reader.infer(args.infer_set_path)):
+        test_batch.append(item)
+        if len(test_batch) == args.batch_size:
+            infer_a_batch(inferer, test_batch, nid_to_word)
+            test_batch = []
+    if len(test_batch):
+        infer_a_batch(inferer, test_batch, nid_to_word)
+
+
+def infer_a_batch(inferer, test_batch, nid_to_word):
+    """
+    input a batch of data and infer 
+    """
+    feeding = {
+        'user_id': 0,
+        'province': 1,
+        'city': 2,
+        'history_clicked_items': 3,
+        'history_clicked_categories': 4,
+        'history_clicked_tags': 5,
+        'phone': 6
+    }
+    probs = inferer.infer(
+        input=test_batch,
+        feeding=feeding,
+        field=["value"],
+        flatten_result=False)
+    for i, res in enumerate(zip(test_batch, probs[0], probs[1])):
+        softmax_output = res[1]
+        sort_nid = res[1].argsort()
+        # print top 30 recommended item 
+        ret = ""
+        for j in range(1, 30):
+            item_id = sort_nid[-1 * j]
+            item_id_to_word = nid_to_word[item_id]
+            ret += "%s:%.6f," \
+                    % (item_id_to_word, softmax_output[item_id])
+
+        print ret.rstrip(",")
+
+
+if __name__ == "__main__":
+    infer()

youtube_recall/infer_user.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import os
+import gzip
+import paddle.v2 as paddle
+import argparse
+import cPickle
+
+from reader import Reader
+from network_conf import DNNmodel
+from utils import logger
+import numpy as np
+
+
+def parse_args():
+    """
+    parse arguments
+    :return:
+    """
+    parser = argparse.ArgumentParser(
+        description="PaddlePaddle Youtube Recall Model Example")
+    parser.add_argument(
+        '--model_path', type=str, required=True, help="path of the model")
+    parser.add_argument(
+        '--feature_dict',
+        type=str,
+        required=True,
+        help="path of feature_dict.pkl")
+    return parser.parse_args()
+
+
+def infer_user():
+    """
+    infer_user
+    """
+    args = parse_args()
+
+    # check argument
+    assert os.path.exists(
+        args.model_path), 'The model_path path does not exist.'
+    assert os.path.exists(
+        args.feature_dict), 'The feature_dict path does not exist.'
+
+    paddle.init(use_gpu=False, trainer_count=1)
+
+    with open(args.feature_dict) as f:
+        feature_dict = cPickle.load(f)
+
+    nid_dict = feature_dict['history_clicked_items']
+    nid_to_word = dict((v, k) for k, v in nid_dict.items())
+
+    # load the trained model.
+    with gzip.open(args.model_path) as f:
+        parameters = paddle.parameters.Parameters.from_tar(f)
+    parameters.set('_proj_province', \
+            np.zeros(shape=parameters.get('_proj_province').shape))
+    parameters.set('_proj_city', \
+            np.zeros(shape=parameters.get('_proj_city').shape))
+    parameters.set('_proj_phone', \
+            np.zeros(shape=parameters.get('_proj_phone').shape))
+    parameters.set('_proj_history_clicked_items', \
+            np.zeros(shape= parameters.get('_proj_history_clicked_items').shape))
+    parameters.set('_proj_history_clicked_categories', \
+            np.zeros(shape= parameters.get('_proj_history_clicked_categories').shape))
+    parameters.set('_proj_history_clicked_tags', \
+            np.zeros(shape= parameters.get('_proj_history_clicked_tags').shape))
+
+    # build model
+    prediction_layer, fc = DNNmodel(
+        dnn_layer_dims=[256, 31], feature_dict=feature_dict,
+        is_infer=True).model_cost
+    inferer = paddle.inference.Inference(
+        output_layer=[prediction_layer, fc], parameters=parameters)
+
+    reader = Reader(feature_dict)
+    test_batch = []
+    for idx, item in enumerate(
+            reader.infer_user(['USER_ID_0', 'USER_ID_981', 'USER_ID_310806'])):
+        test_batch.append(item)
+    infer_a_batch(inferer, test_batch, nid_to_word)
+
+
+def infer_a_batch(inferer, test_batch, nid_to_word):
+    """
+    input a batch of data and infer 
+    """
+    feeding = {
+        'user_id': 0,
+        'province': 1,
+        'city': 2,
+        'history_clicked_items': 3,
+        'history_clicked_categories': 4,
+        'history_clicked_tags': 5,
+        'phone': 6
+    }
+    probs = inferer.infer(
+        input=test_batch,
+        feeding=feeding,
+        field=["value"],
+        flatten_result=False)
+    for i, res in enumerate(zip(test_batch, probs[0], probs[1])):
+        softmax_output = res[1]
+        sort_nid = res[1].argsort()
+
+        # print top 30 recommended item 
+        ret = ""
+        for j in range(1, 30):
+            item_id = sort_nid[-1 * j]
+            item_id_to_word = nid_to_word[item_id]
+            ret += "%s:%.6f," \
+                    % (item_id_to_word, softmax_output[item_id])
+        print ret.rstrip(",")
+
+
+if __name__ == "__main__":
+    infer_user()

youtube_recall/item_vector.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import os
+import gzip
+import paddle.v2 as paddle
+import argparse
+import cPickle
+
+from reader import Reader
+from network_conf import DNNmodel
+from utils import logger
+import numpy as np
+import math
+
+
+def parse_args():
+    """
+    parse arguments
+    :return:
+    """
+    parser = argparse.ArgumentParser(
+        description="PaddlePaddle Youtube Recall Model Example")
+    parser.add_argument(
+        '--model_path', type=str, required=True, help="path of the model")
+    parser.add_argument(
+        '--feature_dict',
+        type=str,
+        required=True,
+        help="path of feature_dict.pkl")
+    return parser.parse_args()
+
+
+def get_item_vec_from_softmax(nce_w, nce_b):
+    """
+    get item vectors from softmax parameter 
+    """
+    if nce_w is None or nce_b is None:
+        return None
+    vector = []
+    total_items_num = nce_w.shape[0]
+    if total_items_num != nce_b.shape[1]:
+        return None
+    dim_vector = nce_w.shape[1] + 1
+    for i in range(0, total_items_num):
+        vector.append([])
+        vector[i].append(nce_b[0][i])
+        for j in range(1, dim_vector):
+            vector[i].append(nce_w[i][j - 1])
+    return vector
+
+
+def convt_simple_lsh(vector):
+    """
+    do simple lsh conversion
+    """
+    max_norm = 0
+    num_of_vec = len(vector)
+    for i in range(0, num_of_vec):
+        norm = np.linalg.norm(vector[i])
+        if norm > max_norm:
+            max_norm = norm
+    for i in range(0, num_of_vec):
+        vector[i].append(
+            math.sqrt(
+                math.pow(max_norm, 2) - math.pow(np.linalg.norm(vector[i]), 2)))
+    return vector
+
+
+def item_vector():
+    """
+    get item vectors
+    """
+    args = parse_args()
+
+    # check argument
+    assert os.path.exists(
+        args.model_path), 'The model_path path does not exist.'
+    assert os.path.exists(
+        args.feature_dict), 'The feature_dict path does not exist.'
+
+    paddle.init(use_gpu=False, trainer_count=1)
+
+    with open(args.feature_dict) as f:
+        feature_dict = cPickle.load(f)
+
+    # load the trained model.
+    with gzip.open(args.model_path) as f:
+        parameters = paddle.parameters.Parameters.from_tar(f)
+
+    nid_dict = feature_dict['history_clicked_items']
+    nid_to_word = dict((v, k) for k, v in nid_dict.items())
+
+    nce_w = parameters.get("nce_w")
+    nce_b = parameters.get("nce_b")
+    item_vector = convt_simple_lsh(get_item_vec_from_softmax(nce_w, nce_b))
+    for i in range(0, len(item_vector)):
+        itemid = nid_to_word[i]
+        print itemid + "\t" + ",".join(map(str, item_vector[i]))
+
+
+if __name__ == "__main__":
+    item_vector()

youtube_recall/network_conf.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import paddle.v2 as paddle
+import cPickle
+
+
+class DNNmodel(object):
+    """
+    Deep Neural Networks for YouTube candidate generation
+    """
+
+    def __init__(self,
+                 dnn_layer_dims=None,
+                 feature_dict=None,
+                 item_freq=None,
+                 is_infer=False):
+        """
+        initialize model
+        @dnn_layer_dims: dimension of each hidden layer
+        @feature_dict: dictionary of encoded feature
+        @item_freq: dictionary of feature values and its frequency
+        @is_infer: if infer mode
+        """
+        self._dnn_layer_dims = dnn_layer_dims
+        self._feature_dict = feature_dict
+        self._item_freq = item_freq
+
+        self._is_infer = is_infer
+
+        # build model
+        self._build_input_layer()
+        self._build_embedding_layer()
+        self.model_cost = self._build_dnn_model()
+
+    def _build_input_layer(self):
+        """
+        build input layer
+        """
+        self._history_clicked_items = paddle.layer.data(
+            name="history_clicked_items",
+            type=paddle.data_type.integer_value_sequence(
+                len(self._feature_dict['history_clicked_items'])))
+        self._history_clicked_categories = paddle.layer.data(
+            name="history_clicked_categories",
+            type=paddle.data_type.integer_value_sequence(
+                len(self._feature_dict['history_clicked_categories'])))
+        self._history_clicked_tags = paddle.layer.data(
+            name="history_clicked_tags",
+            type=paddle.data_type.integer_value_sequence(
+                len(self._feature_dict['history_clicked_tags'])))
+        self._user_id = paddle.layer.data(
+            name="user_id",
+            type=paddle.data_type.integer_value(
+                len(self._feature_dict['user_id'])))
+        self._province = paddle.layer.data(
+            name="province",
+            type=paddle.data_type.integer_value(
+                len(self._feature_dict['province'])))
+        self._city = paddle.layer.data(
+            name="city",
+            type=paddle.data_type.integer_value(
+                len(self._feature_dict['city'])))
+        self._phone = paddle.layer.data(
+            name="phone",
+            type=paddle.data_type.integer_value(
+                len(self._feature_dict['phone'])))
+        self._target_item = paddle.layer.data(
+            name="target_item",
+            type=paddle.data_type.integer_value(
+                len(self._feature_dict['history_clicked_items'])))
+
+    def _create_emb_attr(self, name):
+        """
+        create embedding parameter
+        """
+        return paddle.attr.Param(
+            name=name,
+            initial_std=0.001,
+            learning_rate=1,
+            l2_rate=0,
+            sparse_update=False)
+
+    def _build_embedding_layer(self):
+        """
+        build embedding layer
+        """
+        self._user_id_emb = paddle.layer.embedding(
+            input=self._user_id,
+            size=64,
+            param_attr=self._create_emb_attr('_proj_user_id'))
+        self._province_emb = paddle.layer.embedding(
+            input=self._province,
+            size=8,
+            param_attr=self._create_emb_attr('_proj_province'))
+        self._city_emb = paddle.layer.embedding(
+            input=self._city,
+            size=16,
+            param_attr=self._create_emb_attr('_proj_city'))
+        self._phone_emb = paddle.layer.embedding(
+            input=self._phone,
+            size=16,
+            param_attr=self._create_emb_attr('_proj_phone'))
+        self._history_clicked_items_emb = paddle.layer.embedding(
+            input=self._history_clicked_items,
+            size=64,
+            param_attr=self._create_emb_attr('_proj_history_clicked_items'))
+        self._history_clicked_categories_emb = paddle.layer.embedding(
+            input=self._history_clicked_categories,
+            size=8,
+            param_attr=self._create_emb_attr(
+                '_proj_history_clicked_categories'))
+        self._history_clicked_tags_emb = paddle.layer.embedding(
+            input=self._history_clicked_tags,
+            size=64,
+            param_attr=self._create_emb_attr('_proj_history_clicked_tags'))
+
+    def _build_dnn_model(self):
+        """
+        build dnn model
+        """
+        self._rnn_cell = paddle.networks.simple_lstm(
+            input=self._history_clicked_items_emb, size=64)
+        self._lstm_last = paddle.layer.pooling(
+            input=self._rnn_cell, pooling_type=paddle.pooling.Max())
+        self._avg_emb_cats = paddle.layer.pooling(
+            input=self._history_clicked_categories_emb,
+            pooling_type=paddle.pooling.Avg())
+        self._avg_emb_tags = paddle.layer.pooling(
+            input=self._history_clicked_tags_emb,
+            pooling_type=paddle.pooling.Avg())
+        self._fc_0 = paddle.layer.fc(
+            name="Relu1",
+            input=[
+                self._lstm_last, self._user_id_emb, self._province_emb,
+                self._city_emb, self._avg_emb_cats, self._avg_emb_tags,
+                self._phone_emb
+            ],
+            size=self._dnn_layer_dims[0],
+            act=paddle.activation.Relu())
+
+        self._fc_1 = paddle.layer.fc(name="Relu2",
+                                     input=self._fc_0,
+                                     size=self._dnn_layer_dims[1],
+                                     act=paddle.activation.Relu())
+
+        if not self._is_infer:
+            return paddle.layer.nce(
+                input=self._fc_1,
+                label=self._target_item,
+                num_classes=len(self._feature_dict['history_clicked_items']),
+                param_attr=paddle.attr.Param(name="nce_w"),
+                bias_attr=paddle.attr.Param(name="nce_b"),
+                num_neg_samples=5,
+                neg_distribution=self._item_freq)
+        else:
+            self.prediction_layer = paddle.layer.mixed(
+                size=len(self._feature_dict['history_clicked_items']),
+                input=paddle.layer.trans_full_matrix_projection(
+                    self._fc_1, param_attr=paddle.attr.Param(name="nce_w")),
+                act=paddle.activation.Softmax(),
+                bias_attr=paddle.attr.Param(name="nce_b"))
+            return self.prediction_layer, self._fc_1
+
+
+if __name__ == "__main__":
+    # this is to test and debug the network topology defination.
+    # please set the hyper-parameters as needed.
+    item_freq_path = "./output/item_freq.pkl"
+    with open(item_freq_path) as f:
+        item_freq = cPickle.load(f)
+
+    feature_dict_path = "./output/feature_dict.pkl"
+    with open(feature_dict_path) as f:
+        feature_dict = cPickle.load(f)
+
+    a = DNNmodel(
+        dnn_layer_dims=[256, 31],
+        feature_dict=feature_dict,
+        item_freq=item_freq,
+        is_infer=False)

youtube_recall/reader.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import sys
+
+from utils import logger
+from utils import TaskMode
+
+
+class Reader(object):
+    """
+    Reader
+    """
+
+    def __init__(self, feature_dict=None, window_size=20):
+        """
+        init
+        @window_size: window_size
+        """
+        self._feature_dict = feature_dict
+        self._window_size = window_size
+
+    def train(self, path):
+        """
+        load train set
+        @path: train set path
+        """
+        logger.info("start train reader from %s" % path)
+        mode = TaskMode.create_train()
+        return self._reader(path, mode)
+
+    def test(self, path):
+        """
+        load test set
+        @path: test set path
+        """
+        logger.info("start test reader from %s" % path)
+        mode = TaskMode.create_test()
+        return self._reader(path, mode)
+
+    def infer(self, path):
+        """
+        load infer set
+        @path: infer set path
+        """
+        logger.info("start infer reader from %s" % path)
+        mode = TaskMode.create_infer()
+        return self._reader(path, mode)
+
+    def infer_user(self, user_list):
+        """
+        load user set to infer
+        @user_list: user list
+        """
+        return self._reader_user(user_list)
+
+    def _reader(self, path, mode):
+        """
+        parse data set
+        """
+        USER_ID_UNK = self._feature_dict['user_id'].get('<unk>')
+        PROVINCE_UNK = self._feature_dict['province'].get('<unk>')
+        CITY_UNK = self._feature_dict['city'].get('<unk>')
+        ITEM_UNK = self._feature_dict['history_clicked_items'].get('<unk>')
+        CATEGORY_UNK = self._feature_dict['history_clicked_categories'].get(
+            '<unk>')
+        TAG_UNK = self._feature_dict['history_clicked_tags'].get('<unk>')
+        PHONE_UNK = self._feature_dict['phone'].get('<unk>')
+        with open(path) as f:
+            for line in f:
+                fields = line.strip('\n').split('\t')
+                user_id = self._feature_dict['user_id'].get(fields[0],
+                                                            USER_ID_UNK)
+                province = self._feature_dict['province'].get(fields[1],
+                                                              PROVINCE_UNK)
+                city = self._feature_dict['city'].get(fields[2], CITY_UNK)
+                item_infos = fields[3]
+                phone = self._feature_dict['phone'].get(fields[4], PHONE_UNK)
+                history_clicked_items_all = []
+                history_clicked_tags_all = []
+                history_clicked_categories_all = []
+                for item_info in item_infos.split(';'):
+                    item_info_array = item_info.split(':')
+                    item = item_info_array[0]
+                    item_encoded_id = self._feature_dict['history_clicked_items'].get(\
+                            item, ITEM_UNK)
+                    if item_encoded_id != ITEM_UNK:
+                        history_clicked_items_all.append(item_encoded_id)
+                        category = item_info_array[1]
+                        history_clicked_categories_all.append(
+                                self._feature_dict['history_clicked_categories'].get(\
+                                        category, CATEGORY_UNK))
+                        tags = item_info_array[2]
+                        tag_split = map(str, [self._feature_dict['history_clicked_tags'].get(\
+                                tag, TAG_UNK) \
+                                for tag in tags.strip().split("_")])
+                        history_clicked_tags_all.append("_".join(tag_split))
+
+                if not mode.is_infer():
+                    history_clicked_items_all.insert(0, 0)
+                    history_clicked_tags_all.insert(0, "0")
+                    history_clicked_categories_all.insert(0, 0)
+
+                    for i in range(1, len(history_clicked_items_all)):
+                        start = max(0, i - self._window_size)
+                        history_clicked_items = history_clicked_items_all[start:
+                                                                          i]
+                        history_clicked_categories = history_clicked_categories_all[
+                            start:i]
+                        history_clicked_tags_str = history_clicked_tags_all[
+                            start:i]
+                        history_clicked_tags = []
+                        for tags_a in history_clicked_tags_str:
+                            for tag in tags_a.split("_"):
+                                history_clicked_tags.append(int(tag))
+                        target_item = history_clicked_items_all[i]
+                        yield user_id, province, city, \
+                              history_clicked_items, history_clicked_categories, \
+                              history_clicked_tags, phone, target_item
+                else:
+                    history_clicked_items = history_clicked_items_all
+                    history_clicked_categories = history_clicked_categories_all
+                    history_clicked_tags_str = history_clicked_tags_all
+                    history_clicked_tags = []
+                    for tags_a in history_clicked_tags_str:
+                        for tag in tags_a.split("_"):
+                            history_clicked_tags.append(int(tag))
+                    yield user_id, province, city, \
+                          history_clicked_items, history_clicked_categories, \
+                          history_clicked_tags, phone
+
+    def _reader_user(self, user_list):
+        """
+        parse user list
+        """
+        USER_ID_UNK = self._feature_dict['user_id'].get('<unk>')
+        for user in user_list:
+            user_id = self._feature_dict['user_id'].get(user, USER_ID_UNK)
+            yield user_id, 0, 0, [0], [0], [0], 0
+
+
+if __name__ == "__main__":
+    # this is to test and debug reader function
+    train_data = sys.argv[1]
+    feature_dict = sys.argv[2]
+    window_size = int(sys.argv[3])
+
+    import cPickle
+    with open(feature_dict) as f:
+        feature_dict = cPickle.load(f)
+
+    r = Reader(feature_dict, window_size)
+
+    for dat in r.train(train_data):
+        print dat

youtube_recall/train.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import os
+import gzip
+import paddle.v2 as paddle
+import argparse
+import cPickle
+
+from reader import Reader
+from network_conf import DNNmodel
+from utils import logger
+
+
+def parse_args():
+    """
+    parse arguments
+    """
+    parser = argparse.ArgumentParser(
+        description="PaddlePaddle Youtube Recall Model Example")
+    parser.add_argument(
+        '--train_set_path',
+        type=str,
+        required=True,
+        help="path of the train set")
+    parser.add_argument(
+        '--test_set_path', type=str, required=True, help="path of the test set")
+    parser.add_argument(
+        '--model_output_dir',
+        type=str,
+        required=True,
+        help="directory to output")
+    parser.add_argument(
+        '--feature_dict',
+        type=str,
+        required=True,
+        help="path of feature_dict.pkl")
+    parser.add_argument(
+        '--item_freq', type=str, required=True, help="path of item_freq.pkl ")
+    parser.add_argument(
+        '--window_size', type=int, default=20, help="window size(default: 20)")
+    parser.add_argument(
+        '--num_passes', type=int, default=1, help="number of passes to train")
+    parser.add_argument(
+        '--batch_size',
+        type=int,
+        default=50,
+        help="size of mini-batch (default:50)")
+    return parser.parse_args()
+
+
+def train():
+    """
+    train
+    """
+    args = parse_args()
+
+    # check argument
+    assert os.path.exists(
+        args.train_set_path), 'The train_set_path path does not exist.'
+    assert os.path.exists(
+        args.test_set_path), 'The test_set_path path does not exist.'
+    assert os.path.exists(
+        args.feature_dict), 'The feature_dict path does not exist.'
+    assert os.path.exists(args.item_freq), 'The item_freq path does not exist.'
+    assert os.path.exists(
+        args.model_output_dir), 'The model_output_dir path does not exist.'
+
+    paddle.init(use_gpu=False, trainer_count=1)
+
+    with open(args.feature_dict) as f:
+        feature_dict = cPickle.load(f)
+
+    with open(args.item_freq) as f:
+        item_freq = cPickle.load(f)
+
+    feeding = {
+        'user_id': 0,
+        'province': 1,
+        'city': 2,
+        'history_clicked_items': 3,
+        'history_clicked_categories': 4,
+        'history_clicked_tags': 5,
+        'phone': 6,
+        'target_item': 7
+    }
+    optimizer = paddle.optimizer.AdaGrad(
+        learning_rate=1e-1,
+        regularization=paddle.optimizer.L2Regularization(rate=1e-3))
+
+    cost = DNNmodel(
+        dnn_layer_dims=[256, 31],
+        feature_dict=feature_dict,
+        item_freq=item_freq,
+        is_infer=False).model_cost
+    parameters = paddle.parameters.create(cost)
+
+    trainer = paddle.trainer.SGD(cost, parameters, optimizer)
+
+    def event_handler(event):
+        """
+        event handler
+        """
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id and not event.batch_id % 10:
+                logger.info("Pass %d, Batch %d, Cost %f" %
+                            (event.pass_id, event.batch_id, event.cost))
+        elif isinstance(event, paddle.event.EndPass):
+            save_path = os.path.join(args.model_output_dir,
+                                     "model_pass_%05d.tar.gz" % event.pass_id)
+            logger.info("Save model into %s ..." % save_path)
+            with gzip.open(save_path, "w") as f:
+                trainer.save_parameter_to_tar(f)
+
+    reader = Reader(feature_dict, args.window_size)
+    trainer.train(
+        paddle.batch(
+            paddle.reader.shuffle(
+                lambda: reader.train(args.train_set_path), buf_size=7000),
+            args.batch_size),
+        num_passes=args.num_passes,
+        feeding=feeding,
+        event_handler=event_handler)
+
+
+if __name__ == "__main__":
+    train()

youtube_recall/user_vector.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import os
+import gzip
+import paddle.v2 as paddle
+import argparse
+import cPickle
+
+from reader import Reader
+from network_conf import DNNmodel
+from utils import logger
+import numpy as np
+
+
+def parse_args():
+    """
+    parse arguments
+    """
+    parser = argparse.ArgumentParser(
+        description="PaddlePaddle Youtube Recall Model Example")
+    parser.add_argument(
+        '--infer_set_path',
+        type=str,
+        required=True,
+        help="path of the infer set")
+    parser.add_argument(
+        '--model_path', type=str, required=True, help="path of the model")
+    parser.add_argument(
+        '--feature_dict',
+        type=str,
+        required=True,
+        help="path of feature_dict.pkl")
+    parser.add_argument(
+        '--batch_size',
+        type=int,
+        default=50,
+        help="size of mini-batch (default:50)")
+    return parser.parse_args()
+
+
+def user_vector():
+    """
+    get user vectors
+    """
+    args = parse_args()
+
+    # check argument
+    assert os.path.exists(
+        args.infer_set_path), 'The infer_set_path path does not exist.'
+    assert os.path.exists(
+        args.model_path), 'The model_path path does not exist.'
+    assert os.path.exists(
+        args.feature_dict), 'The feature_dict path does not exist.'
+
+    paddle.init(use_gpu=False, trainer_count=1)
+
+    with open(args.feature_dict) as f:
+        feature_dict = cPickle.load(f)
+
+    # load the trained model.
+    with gzip.open(args.model_path) as f:
+        parameters = paddle.parameters.Parameters.from_tar(f)
+
+    # build model
+    prediction_layer, fc = DNNmodel(
+        dnn_layer_dims=[256, 31], feature_dict=feature_dict,
+        is_infer=True).model_cost
+    inferer = paddle.inference.Inference(
+        output_layer=[prediction_layer, fc], parameters=parameters)
+
+    reader = Reader(feature_dict)
+    test_batch = []
+    for idx, item in enumerate(reader.infer(args.infer_set_path)):
+        test_batch.append(item)
+        if len(test_batch) == args.batch_size:
+            get_a_batch_user_vector(inferer, test_batch)
+            test_batch = []
+    if len(test_batch):
+        get_a_batch_user_vector(inferer, test_batch)
+
+
+def get_a_batch_user_vector(inferer, test_batch):
+    """
+    input a batch of data and get user vectors
+    """
+    feeding = {
+        'user_id': 0,
+        'province': 1,
+        'city': 2,
+        'history_clicked_items': 3,
+        'history_clicked_categories': 4,
+        'history_clicked_tags': 5,
+        'phone': 6
+    }
+    probs = inferer.infer(
+        input=test_batch,
+        feeding=feeding,
+        field=["value"],
+        flatten_result=False)
+    for i, res in enumerate(zip(probs[1])):
+        # do simple lsh conversion
+        user_vector = [1.000]
+        for i in res[0]:
+            user_vector.append(i)
+        user_vector.append(0.000)
+        norm = np.linalg.norm(user_vector)
+        user_vector_norm = [str(_ / norm) for _ in user_vector]
+        print ",".join(user_vector_norm)
+
+
+if __name__ == "__main__":
+    user_vector()

youtube_recall/utils.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import logging
+
+logging.basicConfig()
+logger = logging.getLogger("paddle")
+logger.setLevel(logging.INFO)
+
+
+class TaskMode(object):
+    """
+    TaskMode
+    """
+    TRAIN_MODE = 0
+    TEST_MODE = 1
+    INFER_MODE = 2
+
+    def __init__(self, mode):
+        """
+
+        :param mode:
+        """
+        self.mode = mode
+
+    def is_train(self):
+        """
+
+        :return:
+        """
+        return self.mode == self.TRAIN_MODE
+
+    def is_test(self):
+        """
+
+        :return:
+        """
+        return self.mode == self.TEST_MODE
+
+    def is_infer(self):
+        """
+
+        :return:
+        """
+        return self.mode == self.INFER_MODE
+
+    @staticmethod
+    def create_train():
+        """
+
+        :return:
+        """
+        return TaskMode(TaskMode.TRAIN_MODE)
+
+    @staticmethod
+    def create_test():
+        """
+
+        :return:
+        """
+        return TaskMode(TaskMode.TEST_MODE)
+
+    @staticmethod
+    def create_infer():
+        """
+
+        :return:
+        """
+        return TaskMode(TaskMode.INFER_MODE)