pkpk
6 years ago
committed by
GitHub
parent
cf60e5a2db
commit
d817263c80
@ -0,0 +1,175 @@
|
||||
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
import contextlib
|
||||
import unittest
|
||||
import numpy as np
|
||||
import six
|
||||
|
||||
import paddle
|
||||
import paddle.fluid as fluid
|
||||
from paddle.fluid import core
|
||||
from paddle.fluid.optimizer import SGDOptimizer
|
||||
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
|
||||
import paddle.fluid.dygraph.nn as nn
|
||||
from paddle.fluid.dygraph.base import to_variable
|
||||
from test_imperative_base import new_program_scope
|
||||
|
||||
|
||||
class Policy(fluid.dygraph.Layer):
|
||||
def __init__(self, name_scope):
|
||||
super(Policy, self).__init__(name_scope)
|
||||
|
||||
self.affine1 = nn.FC(self.full_name(), size=128)
|
||||
self.affine2 = nn.FC(self.full_name(), size=2)
|
||||
self.dropout_ratio = 0.6
|
||||
|
||||
self.saved_log_probs = []
|
||||
self.rewards = []
|
||||
|
||||
def forward(self, inputs):
|
||||
x = fluid.layers.reshape(inputs, shape=[-1, 4])
|
||||
x = self.affine1(x)
|
||||
x = fluid.layers.dropout(x, self.dropout_ratio)
|
||||
x = fluid.layers.relu(x)
|
||||
action_scores = self.affine2(x)
|
||||
return fluid.layers.softmax(action_scores, axis=1)
|
||||
|
||||
|
||||
class TestImperativeMnist(unittest.TestCase):
|
||||
def test_mnist_float32(self):
|
||||
seed = 90
|
||||
epoch_num = 1
|
||||
|
||||
state = np.random.normal(size=4).astype("float32")
|
||||
state_list = state.tolist()
|
||||
reward = np.random.random(size=[1, 1]).astype("float32")
|
||||
reward_list = reward.tolist()
|
||||
action_list = [1]
|
||||
action = np.array(action_list).astype("float32")
|
||||
mask_list = [[0, 1]]
|
||||
mask = np.array(mask_list).astype("float32")
|
||||
|
||||
with fluid.dygraph.guard():
|
||||
fluid.default_startup_program().random_seed = seed
|
||||
fluid.default_main_program().random_seed = seed
|
||||
|
||||
policy = Policy("PolicyModel")
|
||||
|
||||
dy_state = fluid.dygraph.base.to_variable(state)
|
||||
dy_state.stop_gradient = True
|
||||
loss_probs = policy(dy_state)
|
||||
|
||||
dy_mask = fluid.dygraph.base.to_variable(mask)
|
||||
dy_mask.stop_gradient = True
|
||||
|
||||
loss_probs = fluid.layers.log(loss_probs)
|
||||
loss_probs = fluid.layers.elementwise_mul(loss_probs, dy_mask)
|
||||
loss_probs = fluid.layers.reduce_sum(loss_probs, dim=-1)
|
||||
|
||||
dy_reward = fluid.dygraph.base.to_variable(reward)
|
||||
dy_reward.stop_gradient = True
|
||||
|
||||
loss_probs = fluid.layers.elementwise_mul(dy_reward, loss_probs)
|
||||
loss = fluid.layers.reduce_sum(loss_probs)
|
||||
|
||||
sgd = SGDOptimizer(learning_rate=1e-3)
|
||||
|
||||
dy_param_init_value = {}
|
||||
|
||||
dy_out = loss.numpy()
|
||||
|
||||
for param in policy.parameters():
|
||||
dy_param_init_value[param.name] = param.numpy()
|
||||
|
||||
loss.backward()
|
||||
sgd.minimize(loss)
|
||||
policy.clear_gradients()
|
||||
|
||||
dy_param_value = {}
|
||||
for param in policy.parameters():
|
||||
dy_param_value[param.name] = param.numpy()
|
||||
|
||||
with new_program_scope():
|
||||
fluid.default_startup_program().random_seed = seed
|
||||
fluid.default_main_program().random_seed = seed
|
||||
|
||||
exe = fluid.Executor(fluid.CPUPlace(
|
||||
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
|
||||
|
||||
policy = Policy("PolicyModel")
|
||||
|
||||
st_sgd = SGDOptimizer(learning_rate=1e-3)
|
||||
|
||||
st_state = fluid.layers.data(
|
||||
name='st_state', shape=[4], dtype='float32')
|
||||
st_reward = fluid.layers.data(
|
||||
name='st_reward', shape=[1], dtype='float32')
|
||||
st_mask = fluid.layers.data(
|
||||
name='st_mask', shape=[2], dtype='float32')
|
||||
|
||||
st_loss_probs = policy(st_state)
|
||||
|
||||
st_loss_probs = fluid.layers.log(st_loss_probs)
|
||||
st_loss_probs = fluid.layers.elementwise_mul(st_loss_probs, st_mask)
|
||||
st_loss_probs = fluid.layers.reduce_sum(st_loss_probs, dim=-1)
|
||||
|
||||
st_loss_probs = fluid.layers.elementwise_mul(st_reward,
|
||||
st_loss_probs)
|
||||
st_loss = fluid.layers.reduce_sum(st_loss_probs)
|
||||
|
||||
st_sgd.minimize(st_loss)
|
||||
|
||||
# initialize params and fetch them
|
||||
static_param_init_value = {}
|
||||
static_param_name_list = []
|
||||
for param in policy.parameters():
|
||||
static_param_name_list.append(param.name)
|
||||
|
||||
out = exe.run(fluid.default_startup_program(),
|
||||
fetch_list=static_param_name_list)
|
||||
|
||||
for i in range(len(static_param_name_list)):
|
||||
static_param_init_value[static_param_name_list[i]] = out[i]
|
||||
|
||||
fetch_list = [st_loss.name]
|
||||
fetch_list.extend(static_param_name_list)
|
||||
|
||||
out = exe.run(
|
||||
fluid.default_main_program(),
|
||||
feed={"st_state": state,
|
||||
"st_reward": reward,
|
||||
"st_mask": mask},
|
||||
fetch_list=fetch_list)
|
||||
|
||||
static_param_value = {}
|
||||
static_out = out[0]
|
||||
for i in range(1, len(out)):
|
||||
static_param_value[static_param_name_list[i - 1]] = out[i]
|
||||
|
||||
#self.assertTrue(np.allclose(dy_x_data.all(), static_x_data.all()))
|
||||
|
||||
for key, value in six.iteritems(static_param_init_value):
|
||||
self.assertTrue(np.equal(value, dy_param_init_value[key]).all())
|
||||
|
||||
self.assertTrue(np.equal(static_out, dy_out).all())
|
||||
|
||||
for key, value in six.iteritems(static_param_value):
|
||||
self.assertTrue(np.equal(value, dy_param_value[key]).all())
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue