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@ -16,10 +16,9 @@ from __future__ import print_function
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import unittest
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import numpy as np
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import paddle.fluid.core as core
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import paddle.fluid as fluid
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import math
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# import paddle.fluid as fluid
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# import paddle.fluid.core as core
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# from op_builder import OpBuilder
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from op_test import OpTest
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np.random.seed(100)
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@ -141,67 +140,148 @@ def hsigmoidWithCustomTree(x, w, ptable, pcode, label, bias, num_classes):
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return pre_output, out
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class TestHSigmoidOp(OpTest):
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def setUp(self):
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self.op_type = "hierarchical_sigmoid"
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num_classes = 6
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feature_size = 8
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batch_size = 4
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x = np.random.random((batch_size, feature_size)).astype("float32") * 2
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w = np.random.random(
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(num_classes - 1, feature_size)).astype("float32") * 2
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label = np.random.randint(0, num_classes, (batch_size, 1))
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bias = np.random.random((1, num_classes - 1)).astype("float32")
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self.attrs = {'num_classes': num_classes}
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self.inputs = {'X': x, 'W': w, 'Label': label, 'Bias': bias}
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pre_output, out = hsigmoid(x, w, label, bias, num_classes)
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self.outputs = {'PreOut': pre_output, 'Out': out}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['Bias', 'X', 'W'], ['Out'], no_grad_set=set('Label'))
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class TestHSigmoidOpWithCostumTree(OpTest):
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def setUp(self):
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self.op_type = "hierarchical_sigmoid"
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num_classes = 6 #using 1,2,3,4,5,6 to build a huffman tree and select 1,2,5,6 as sample
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feature_size = 8
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batch_size = 4
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x = np.random.random((batch_size, feature_size)).astype("float32") * 2
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w = np.random.random(
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(num_classes - 1, feature_size)).astype("float32") * 2
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label = np.array([0, 1, 4, 5])
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ptable = np.array(
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[(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1),
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(0, 2, -1, -1,
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-1)]) #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
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pcode = np.array([(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (
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1, 0, 0, -1, -1), (0, 1, -1, -1, -1)]) #np.array to store
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bias = np.random.random((1, num_classes - 1)).astype("float32")
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self.attrs = {'num_classes': num_classes}
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self.inputs = {
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'X': x,
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'W': w,
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'PTable': ptable,
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'PCode': pcode,
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'Label': label,
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'Bias': bias
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}
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pre_output, out = hsigmoidWithCustomTree(x, w, ptable, pcode, label,
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bias, num_classes)
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self.outputs = {'PreOut': pre_output, 'Out': out}
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def test_check_output(self):
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print("checking output in CostumTree")
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self.check_output()
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def test_check_grad(self):
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print("checking outputGrad in CostumTree")
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self.check_grad(['Bias', 'X', 'W'], ['Out'], no_grad_set=set('Label'))
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# class TestHSigmoidOp(OpTest):
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# def setUp(self):
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# self.op_type = "hierarchical_sigmoid"
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# num_classes = 6
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# feature_size = 8
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# batch_size = 4
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# x = np.random.random((batch_size, feature_size)).astype("float32") * 2
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# w = np.random.random(
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# (num_classes - 1, feature_size)).astype("float32") * 2
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# label = np.random.randint(0, num_classes, (batch_size, 1))
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# bias = np.random.random((1, num_classes - 1)).astype("float32")
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# self.attrs = {'num_classes': num_classes, 'is_sparse': False}
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# self.inputs = {'X': x, 'W': w, 'Label': label, 'Bias': bias}
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# pre_output, out = hsigmoid(x, w, label, bias, num_classes)
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# self.outputs = {'PreOut': pre_output, 'Out': out}
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# def test_check_output(self):
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# self.check_output()
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# def test_check_grad(self):
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# self.check_grad(['Bias', 'X', 'W'], ['Out'], no_grad_set=set('Label'))
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# class TestHSigmoidOpSparse(OpTest):
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# def setUp(self):
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# self.op_type = "hierarchical_sigmoid"
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# num_classes = 6 #using 1,2,3,4,5,6 to build a huffman tree and select 1,2,5,6 as sample
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# feature_size = 8
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# batch_size = 4
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# x = np.random.random((batch_size, feature_size)).astype("float32") * 2
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# w = np.random.random(
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# (num_classes - 1, feature_size)).astype("float32") * 2
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# label = np.array([0, 1, 4, 5])
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# ptable = np.array(
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# [(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1),
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# (0, 2, -1, -1,
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# -1)]) #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
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# pcode = np.array([(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (
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# 1, 0, 0, -1, -1), (0, 1, -1, -1, -1)]) #np.array to store
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# bias = np.random.random((1, num_classes - 1)).astype("float32")
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# self.attrs = {'num_classes': num_classes, 'is_sparse': True}
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# self.inputs = {
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# 'X': x,
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# 'W': w,
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# 'PTable': ptable,
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# 'PCode': pcode,
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# 'Label': label,
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# 'Bias': bias
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# }
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# pre_output, out = hsigmoidWithCustomTree(x, w, ptable, pcode, label,
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# bias, num_classes)
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# self.outputs = {'PreOut': pre_output, 'Out': out}
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# def test_check_output(self):
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# print("checking output in CostumTree")
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# self.check_output()
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class TestHSigmoidOpWithSparseGrad():
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def hs_net_conf(self):
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emb = fluid.layers.data(name="x", shape=[3], dtype='int64')
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ptable = fluid.layers.data(name='ptable', shape=[3], dtype='int64')
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pcode = fluid.layers.data(name='pcode', shape=[3], dtype='int64')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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data_list = [emb, ptable, pcode, label]
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cost = fluid.layers.hsigmoid(
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input=emb,
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label=predict_word,
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non_leaf_num=4,
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ptable=ptable,
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pcode=pcode,
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is_costum=True,
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is_sparse=True)
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avg_cost = fluid.layers.reduce_mean(cost)
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return avg_cost, data_list
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def test_training_test(self):
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print("im here")
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w = np.arange(12).reshape(4, 3)
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x = np.ones((2, 3))
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ptable = np.array([(1, 2, -1), (1, 2, -1)])
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pcode = np.array([(1, 0, -1), (0, 0, -1)])
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label = np.array([(1, 4)])
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loss, data_list = hs_net_conf()
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optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
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optimizer.minimize(loss)
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main_program = fluid.default_main_program()
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place = fluid.CPUPlace()
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feeder = fluid.DataFeeder(feed_list=data_list, place=place)
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data_name_list = [var.name for var in data_list]
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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for pass_id in range(args.num_passes):
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for i in range(10):
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data = [w, x[i % 2], ptable[i % 2], pcode[i % 2], label[i % 2]]
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loss_val = exe.run(main_program,
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feed=feeder.feed(data),
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fetch_list=[loss])
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print("loss is: {loss}".format(loss=loss))
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# class TestHSigmoidOpWithCostumTree(OpTest):
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# def setUp(self):
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# self.op_type = "hierarchical_sigmoid"
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# num_classes = 6 #using 1,2,3,4,5,6 to build a huffman tree and select 1,2,5,6 as sample
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# feature_size = 8
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# batch_size = 4
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# x = np.random.random((batch_size, feature_size)).astype("float32") * 2
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# w = np.random.random(
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# (num_classes - 1, feature_size)).astype("float32") * 2
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# label = np.array([0, 1, 4, 5])
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# ptable = np.array(
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# [(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1),
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# (0, 2, -1, -1,
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# -1)]) #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
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# pcode = np.array([(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (
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# 1, 0, 0, -1, -1), (0, 1, -1, -1, -1)]) #np.array to store
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# bias = np.random.random((1, num_classes - 1)).astype("float32")
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# self.attrs = {'num_classes': num_classes, 'is_sparse': False}
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# self.inputs = {
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# 'X': x,
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# 'W': w,
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# 'PTable': ptable,
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# 'PCode': pcode,
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# 'Label': label,
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# 'Bias': bias
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# }
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# pre_output, out = hsigmoidWithCustomTree(x, w, ptable, pcode, label,
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# bias, num_classes)
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# self.outputs = {'PreOut': pre_output, 'Out': out}
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# def test_check_output(self):
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# print("checking output in CostumTree")
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# self.check_output()
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# def test_check_grad(self):
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# print("checking outputGrad in CostumTree")
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# self.check_grad(['Bias', 'X', 'W'], ['Out'], no_grad_set=set('Label'))
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if __name__ == '__main__':
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unittest.main()
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JiabinYang
7 years ago