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model.py
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model.py
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#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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 singa import layer
from singa import model
from singa import tensor
from singa import opt
from singa import device
import argparse
import numpy as np
np_dtype = {"float16": np.float16, "float32": np.float32}
singa_dtype = {"float16": tensor.float16, "float32": tensor.float32}
class MLP(model.Model):
def __init__(self, data_size=10, perceptron_size=100, num_classes=10):
super(MLP, self).__init__()
self.num_classes = num_classes
self.dimension = 2
self.relu = layer.ReLU()
self.linear1 = layer.Linear(perceptron_size)
self.linear2 = layer.Linear(num_classes)
self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
def forward(self, inputs):
y = self.linear1(inputs)
y = self.relu(y)
y = self.linear2(y)
return y
def train_one_batch(self, x, y, dist_option, spars):
out = self.forward(x)
loss = self.softmax_cross_entropy(out, y)
if dist_option == 'plain':
self.optimizer(loss)
elif dist_option == 'half':
self.optimizer.backward_and_update_half(loss)
elif dist_option == 'partialUpdate':
self.optimizer.backward_and_partial_update(loss)
elif dist_option == 'sparseTopK':
self.optimizer.backward_and_sparse_update(loss,
topK=True,
spars=spars)
elif dist_option == 'sparseThreshold':
self.optimizer.backward_and_sparse_update(loss,
topK=False,
spars=spars)
return out, loss
def set_optimizer(self, optimizer):
self.optimizer = optimizer
def create_model(pretrained=False, **kwargs):
"""Constructs a CNN model.
Args:
pretrained (bool): If True, returns a pre-trained model.
Returns:
The created CNN model.
"""
model = MLP(**kwargs)
return model
__all__ = ['MLP', 'create_model']
if __name__ == "__main__":
np.random.seed(0)
parser = argparse.ArgumentParser()
parser.add_argument('-p',
choices=['float32', 'float16'],
default='float32',
dest='precision')
parser.add_argument('-g',
'--disable-graph',
default='True',
action='store_false',
help='disable graph',
dest='graph')
parser.add_argument('-m',
'--max-epoch',
default=1001,
type=int,
help='maximum epochs',
dest='max_epoch')
args = parser.parse_args()
# generate the boundary
f = lambda x: (5 * x + 1)
bd_x = np.linspace(-1.0, 1, 200)
bd_y = f(bd_x)
# generate the training data
x = np.random.uniform(-1, 1, 400)
y = f(x) + 2 * np.random.randn(len(x))
# choose one precision
precision = singa_dtype[args.precision]
np_precision = np_dtype[args.precision]
# convert training data to 2d space
label = np.asarray([5 * a + 1 > b for (a, b) in zip(x, y)]).astype(np.int32)
data = np.array([[a, b] for (a, b) in zip(x, y)], dtype=np_precision)
dev = device.create_cuda_gpu_on(0)
sgd = opt.SGD(0.1, 0.9, 1e-5, dtype=singa_dtype[args.precision])
tx = tensor.Tensor((400, 2), dev, precision)
ty = tensor.Tensor((400,), dev, tensor.int32)
model = MLP(data_size=2, perceptron_size=3, num_classes=2)
# attach model to graph
model.set_optimizer(sgd)
model.compile([tx], is_train=True, use_graph=args.graph, sequential=True)
model.train()
for i in range(args.max_epoch):
tx.copy_from_numpy(data)
ty.copy_from_numpy(label)
out, loss = model(tx, ty, 'fp32', spars=None)
if i % 100 == 0:
print("training loss = ", tensor.to_numpy(loss)[0])