test_recurrent_op.py 4.7 KB
Newer Older
Y
Yan Chunwei 已提交
1
import logging
Y
Yan Chunwei 已提交
2 3 4
import paddle.v2.framework.core as core
import unittest
import numpy as np
Y
Yan Chunwei 已提交
5
from paddle.v2.framework.op import Operator, RecurrentOp
Y
Yan Chunwei 已提交
6

S
superjom 已提交
7 8

def py_sigmoid(x):
S
superjom 已提交
9
    return 1. / (1. + np.exp(-x))
S
superjom 已提交
10

S
fix res  
superjom 已提交
11

S
superjom 已提交
12 13 14 15
class PySimpleRNN(object):
    '''
    A simple implementation of RNN based on numpy, to futhur test RecurrentOp's alogorithm
    '''
S
fix res  
superjom 已提交
16 17

    def __init__(self, input_dim=30, batch_size=50, weight_dim=15, sent_len=11):
S
superjom 已提交
18 19 20 21 22 23
        self.x = np.random.normal(size=(sent_len, batch_size, input_dim))
        self.W = np.random.normal(size=(input_dim, input_dim))
        self.U = np.random.normal(size=(input_dim, input_dim))
        self.h_boot = np.random.normal(size=(batch_size, input_dim))

        # memories
S
fix res  
superjom 已提交
24 25 26
        self.mems = [
            np.zeros(shape=(batch_size, input_dim)) for i in range(sent_len)
        ]
S
superjom 已提交
27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

    def forward(self):
        xs = self.segment_inputs()
        for step_id in range(self.x.shape[0]):
            self.step(step_id, xs[step_id])
        return self.concat_outputs()

    def segment_inputs(self):
        return [self.x[i] for i in range(self.x.shape[0])]

    def concat_outputs(self):
        return np.array(self.mems)

    def step(self, step_id, x):
        '''
        run a step
        '''
        mem = self.mems[step_id]
        if step_id > 0:
S
fix res  
superjom 已提交
46
            pre_mem = self.mems[step_id - 1]
S
superjom 已提交
47 48 49 50 51 52 53 54
        else:
            pre_mem = self.h_boot
        xW = np.matmul(x, self.W)
        hU = np.matmul(mem, self.U)

        sum = xW + hU
        self.mems[step_id] = py_sigmoid(sum)

S
fix res  
superjom 已提交
55

S
superjom 已提交
56 57 58 59 60 61 62
class PySimpleRNNTest(unittest.TestCase):
    def setUp(self):
        self.rnn = PySimpleRNN()

    def test_forward(self):
        output = self.rnn.forward()
        print 'output', output
Y
Yan Chunwei 已提交
63 64


S
superjom 已提交
65
def create_tensor(scope, name, shape, np_data):
Y
Yan Chunwei 已提交
66
    tensor = scope.new_var(name).get_tensor()
Y
Yan Chunwei 已提交
67
    tensor.set_dims(shape)
S
superjom 已提交
68
    tensor.set(np_data, core.CPUPlace())
Y
Yan Chunwei 已提交
69 70 71
    return tensor


S
superjom 已提交
72
class TestRecurrentOp(unittest.TestCase):
Y
Yan Chunwei 已提交
73 74 75 76 77 78 79 80 81 82 83 84 85
    '''
    Test RNNOp

    equation:
        h_t = \sigma (W x_t + U h_{t-1})
    weights:
        - W
        - U
    vars:
        - x
    memories:
        - h
    outputs:
S
superjom 已提交
86
       - h
Y
Yan Chunwei 已提交
87 88
    '''

Y
Yan Chunwei 已提交
89 90 91 92 93
    input_dim = 30
    batch_size = 50
    weight_dim = 15
    sent_len = 11

S
superjom 已提交
94
    def setUp(self):
S
fix res  
superjom 已提交
95 96
        self.py_rnn = PySimpleRNN(self.input_dim, self.batch_size,
                                  self.weight_dim, self.sent_len)
Y
Yan Chunwei 已提交
97

S
superjom 已提交
98 99
    def forward(self):
        self.scope = core.Scope()
Y
Yan Chunwei 已提交
100
        self.create_global_variables()
Y
Yan Chunwei 已提交
101
        self.create_rnn_op()
Y
Yan Chunwei 已提交
102 103
        self.create_step_net()
        ctx = core.DeviceContext.create(core.CPUPlace())
Y
Yan Chunwei 已提交
104 105
        self.rnnop.infer_shape(self.scope)
        self.rnnop.run(self.scope, ctx)
S
superjom 已提交
106
        return np.array(self.scope.find_var("h").get_tensor())
Y
Yan Chunwei 已提交
107 108 109

    def create_global_variables(self):
        # create inlink
S
superjom 已提交
110
        x_np_data = self.py_rnn.x
Y
Yan Chunwei 已提交
111
        create_tensor(self.scope, "x",
S
fix res  
superjom 已提交
112 113
                      [self.sent_len, self.batch_size, self.input_dim],
                      x_np_data)
S
superjom 已提交
114
        W_np_data = self.py_rnn.W
S
fix res  
superjom 已提交
115 116
        create_tensor(self.scope, "W", [self.input_dim, self.input_dim],
                      W_np_data)
S
superjom 已提交
117 118

        U_np_data = self.py_rnn.U
S
fix res  
superjom 已提交
119 120
        create_tensor(self.scope, "U", [self.input_dim, self.input_dim],
                      U_np_data)
S
superjom 已提交
121 122

        h_boot_np_data = self.py_rnn.h_boot
S
fix res  
superjom 已提交
123 124
        create_tensor(self.scope, "h_boot", [self.batch_size, self.input_dim],
                      h_boot_np_data)
Y
Yan Chunwei 已提交
125 126 127 128 129
        self.scope.new_var("step_scopes")
        self.scope.new_var("h@alias")
        self.scope.new_var("h")

    def create_rnn_op(self):
Y
Yan Chunwei 已提交
130
        # create RNNOp
Y
Yan Chunwei 已提交
131
        self.rnnop = RecurrentOp(
Y
Yan Chunwei 已提交
132 133 134 135 136
            # inputs
            inlinks=["x"],
            boot_memories=["h_boot"],
            step_net="stepnet",
            # outputs
Y
Yan Chunwei 已提交
137
            outlinks=["h"],
Y
Yan Chunwei 已提交
138 139 140
            step_scopes="step_scopes",
            # attributes
            inlink_alias=["x@alias"],
Y
Yan Chunwei 已提交
141 142 143 144 145
            outlink_alias=["h@alias"],
            pre_memories=["h@pre"],
            memories=["h@alias"])

    def create_step_net(self):
Y
Yan Chunwei 已提交
146
        stepnet = core.Net.create()
S
fix res  
superjom 已提交
147 148
        x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx")
        h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
149
        sum_op = Operator("add", X="Wx", Y="Uh", Out="sum")
S
superjom 已提交
150
        sig_op = Operator("sigmoid", X="sum", Y="h@alias")
Y
Yan Chunwei 已提交
151 152

        for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
Y
Yu Yang 已提交
153
            stepnet.append_op(op)
Y
Yan Chunwei 已提交
154
        stepnet.complete_add_op(True)
Y
Yan Chunwei 已提交
155
        self.rnnop.set_stepnet(stepnet)
Y
Yan Chunwei 已提交
156

S
superjom 已提交
157 158
    def test_forward(self):
        print 'test recurrent op forward'
S
superjom 已提交
159 160 161 162 163 164
        pd_output = self.forward()
        py_output = self.py_rnn.forward()
        print 'pd_output', pd_output
        print
        print 'py_output', py_output
        self.assertEqual(pd_output.shape, py_output.shape)
Y
Yan Chunwei 已提交
165

S
fix res  
superjom 已提交
166

Y
Yan Chunwei 已提交
167 168
if __name__ == '__main__':
    unittest.main()