提交 001b62a4 编写于 作者: S superjom

finish simple rnn in python

上级 d9f97b02
......@@ -2,9 +2,64 @@ import logging
import paddle.v2.framework.core as core
import unittest
import numpy as np
import paddle.v2.framework.create_op_creation_methods as creation
from paddle.v2.framework.op import Operator
ops = creation.op_creations
def py_sigmoid(x):
return 1. / (1 + np.exp(-x))
class PySimpleRNN(object):
'''
A simple implementation of RNN based on numpy, to futhur test RecurrentOp's alogorithm
'''
def __init__(self,
input_dim = 30,
batch_size = 50,
weight_dim = 15,
sent_len = 11):
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
self.mems = [np.zeros(shape=(batch_size, input_dim)) for i in range(sent_len)]
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:
pre_mem = self.mems[step_id-1]
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)
class PySimpleRNNTest(unittest.TestCase):
def setUp(self):
self.rnn = PySimpleRNN()
def test_forward(self):
output = self.rnn.forward()
print 'output', output
def create_tensor(scope, name, shape):
......@@ -14,7 +69,7 @@ def create_tensor(scope, name, shape):
return tensor
class TestRNN(unittest.TestCase):
class TestRecurrentOp(unittest.TestCase):
'''
Test RNNOp
......@@ -28,7 +83,7 @@ class TestRNN(unittest.TestCase):
memories:
- h
outputs:
- h
- h
'''
input_dim = 30
......@@ -36,7 +91,7 @@ class TestRNN(unittest.TestCase):
weight_dim = 15
sent_len = 11
def init(self):
def forward(self):
self.scope = core.Scope()
......@@ -46,7 +101,6 @@ class TestRNN(unittest.TestCase):
ctx = core.DeviceContext.create(core.CPUPlace())
print 'infer_shape'
rnn_op.infer_shape(self.scope)
rnn_op.run(self.scope, ctx)
def create_global_variables(self):
......@@ -62,7 +116,7 @@ class TestRNN(unittest.TestCase):
def create_rnn_op(self):
# create RNNOp
rnnop = ops.recurrent_op(
rnnop = Operator("recurrent_op",
# inputs
inlinks=["x"],
boot_memories=["h_boot"],
......@@ -81,17 +135,18 @@ class TestRNN(unittest.TestCase):
var = self.scope.new_var("stepnet")
stepnet = var.get_net()
x_fc_op = ops.fc(X="x@alias", W="W", Y="Wx")
h_fc_op = ops.fc(X="h@pre", W="U", Y="Uh")
sum_op = ops.add_two(X="Wx", Y="Uh", Out="sum")
sig_op = ops.sigmoid(X="sum", Y="h@alias")
x_fc_op = Operator("fc", X="x@alias", W="W", Y="Wx")
h_fc_op = Operator("fc", X="h@pre", W="U", Y="Uh")
sum_op = Operator("add_two", X="Wx", Y="Uh", Out="sum")
sig_op = Operator("sigmoid", X="sum", Y="h@alias")
for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
stepnet.add_op(op)
stepnet.complete_add_op(True)
def test_recurrent(self):
self.init()
def test_forward(self):
print 'test recurrent op forward'
self.forward()
if __name__ == '__main__':
......
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