You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
67 lines
2.3 KiB
67 lines
2.3 KiB
# Copyright (c) 2019 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.
|
|
|
|
import paddle.fluid as fluid
|
|
import numpy as np
|
|
|
|
|
|
def simple_fc_net(use_feed=None):
|
|
img = fluid.layers.data(name='image', shape=[784], dtype='float32')
|
|
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
|
|
|
|
hidden = img
|
|
for _ in range(4):
|
|
hidden = fluid.layers.fc(
|
|
hidden,
|
|
size=200,
|
|
act='relu',
|
|
bias_attr=fluid.ParamAttr(
|
|
initializer=fluid.initializer.Constant(value=1.0)))
|
|
prediction = fluid.layers.fc(hidden, size=10, act='softmax')
|
|
loss = fluid.layers.cross_entropy(input=prediction, label=label)
|
|
loss = fluid.layers.mean(loss)
|
|
return loss
|
|
|
|
|
|
def fc_with_batchnorm(use_feed=None):
|
|
img = fluid.layers.data(name='image', shape=[784], dtype='float32')
|
|
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
|
|
|
|
hidden = img
|
|
for _ in range(2):
|
|
hidden = fluid.layers.fc(
|
|
hidden,
|
|
size=200,
|
|
act='relu',
|
|
bias_attr=fluid.ParamAttr(
|
|
initializer=fluid.initializer.Constant(value=1.0)))
|
|
|
|
hidden = fluid.layers.batch_norm(input=hidden)
|
|
|
|
prediction = fluid.layers.fc(hidden, size=10, act='softmax')
|
|
loss = fluid.layers.cross_entropy(input=prediction, label=label)
|
|
loss = fluid.layers.mean(loss)
|
|
return loss
|
|
|
|
|
|
def init_data(batch_size=32, img_shape=[784], label_range=9):
|
|
np.random.seed(5)
|
|
assert isinstance(img_shape, list)
|
|
input_shape = [batch_size] + img_shape
|
|
img = np.random.random(size=input_shape).astype(np.float32)
|
|
label = np.array(
|
|
[np.random.randint(0, label_range) for _ in range(batch_size)]).reshape(
|
|
(-1, 1)).astype("int64")
|
|
return img, label
|