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#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file 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 argparse
import os
import chainer
from chainer import serializers, training
from chainer.datasets import tuple_dataset
import chainer.functions as F
import chainer.links as L
from chainer.training import extensions
import numpy as np
import sagemaker_containers
class MLP(chainer.Chain):
def __init__(self, n_units, n_out):
super(MLP, self).__init__()
with self.init_scope():
# the size of the inputs to each layer will be inferred
self.l1 = L.Linear(None, n_units) # n_in -> n_units
self.l2 = L.Linear(None, n_units) # n_units -> n_units
self.l3 = L.Linear(None, n_out) # n_units -> n_out
def __call__(self, x):
h1 = F.relu(self.l1(x))
h2 = F.relu(self.l2(h1))
return self.l3(h2)
def _preprocess_mnist(raw, withlabel, ndim, scale, image_dtype, label_dtype, rgb_format):
images = raw["x"][-100:]
if ndim == 2:
images = images.reshape(-1, 28, 28)
elif ndim == 3:
images = images.reshape(-1, 1, 28, 28)
if rgb_format:
images = np.broadcast_to(images, (len(images), 3) + images.shape[2:])
elif ndim != 1:
raise ValueError("invalid ndim for MNIST dataset")
images = images.astype(image_dtype)
images *= scale / 255.0
if withlabel:
labels = raw["y"][-100:].astype(label_dtype)
return tuple_dataset.TupleDataset(images, labels)
else:
return images
if __name__ == "__main__":
env = sagemaker_containers.training_env()
parser = argparse.ArgumentParser()
# Data and model checkpoints directories
parser.add_argument("--units", type=int, default=1000)
parser.add_argument("--epochs", type=int, default=20)
parser.add_argument("--frequency", type=int, default=20)
parser.add_argument("--batch-size", type=int, default=100)
parser.add_argument("--alpha", type=float, default=0.001)
parser.add_argument("--model-dir", type=str, default=env.model_dir)
parser.add_argument("--train", type=str, default=env.channel_input_dirs["train"])
parser.add_argument("--test", type=str, default=env.channel_input_dirs["test"])
parser.add_argument("--num-gpus", type=int, default=env.num_gpus)
args = parser.parse_args()
train_file = np.load(os.path.join(args.train, "train.npz"))
test_file = np.load(os.path.join(args.test, "test.npz"))
preprocess_mnist_options = {
"withlabel": True,
"ndim": 1,
"scale": 1.0,
"image_dtype": np.float32,
"label_dtype": np.int32,
"rgb_format": False,
}
train = _preprocess_mnist(train_file, **preprocess_mnist_options)
test = _preprocess_mnist(test_file, **preprocess_mnist_options)
# Set up a neural network to train
# Classifier reports softmax cross entropy loss and accuracy at every
# iteration, which will be used by the PrintReport extension below.
model = L.Classifier(MLP(args.units, 10))
if chainer.cuda.available:
chainer.cuda.get_device_from_id(0).use()
# Setup an optimizer
optimizer = chainer.optimizers.Adam(alpha=args.alpha)
optimizer.setup(model)
# Load the MNIST dataset
train_iter = chainer.iterators.SerialIterator(train, args.batch_size)
test_iter = chainer.iterators.SerialIterator(test, args.batch_size, repeat=False, shuffle=False)
# Set up a trainer
device = 0 if chainer.cuda.available else -1 # -1 indicates CPU, 0 indicates first GPU device.
if chainer.cuda.available:
def device_name(device_intra_rank):
return "main" if device_intra_rank == 0 else str(device_intra_rank)
devices = {device_name(device): device for device in range(args.num_gpus)}
updater = training.updater.ParallelUpdater(
train_iter,
optimizer,
# The device of the name 'main' is used as a "master", while others are
# used as slaves. Names other than 'main' are arbitrary.
devices=devices,
)
else:
updater = training.updater.StandardUpdater(train_iter, optimizer, device=device)
# Write output files to output_data_dir.
# These are zipped and uploaded to S3 output path as output.tar.gz.
trainer = training.Trainer(updater, (args.epochs, "epoch"), out=env.output_data_dir)
# Evaluate the model with the test dataset for each epoch
trainer.extend(extensions.Evaluator(test_iter, model, device=device))
# Dump a computational graph from 'loss' variable at the first iteration
# The "main" refers to the target link of the "main" optimizer.
trainer.extend(extensions.dump_graph("main/loss"))
# Take a snapshot for each specified epoch
trainer.extend(extensions.snapshot(), trigger=(args.frequency, "epoch"))
# Write a log of evaluation statistics for each epoch
trainer.extend(extensions.LogReport())
# Save two plot images to the result dir
if extensions.PlotReport.available():
trainer.extend(
extensions.PlotReport(
["main/loss", "validation/main/loss"], "epoch", file_name="loss.png"
)
)
trainer.extend(
extensions.PlotReport(
["main/accuracy", "validation/main/accuracy"], "epoch", file_name="accuracy.png"
)
)
# Print selected entries of the log to stdout
# Here "main" refers to the target link of the "main" optimizer again, and
# "validation" refers to the default name of the Evaluator extension.
# Entries other than 'epoch' are reported by the Classifier link, called by
# either the updater or the evaluator.
trainer.extend(
extensions.PrintReport(
[
"epoch",
"main/loss",
"validation/main/loss",
"main/accuracy",
"validation/main/accuracy",
"elapsed_time",
]
)
)
# Print a progress bar to stdout
trainer.extend(extensions.ProgressBar())
# Run the training
trainer.run()
serializers.save_npz(os.path.join(args.model_dir, "model.npz"), model)
def model_fn(model_dir):
model = L.Classifier(MLP(1000, 10))
serializers.load_npz(os.path.join(model_dir, "model.npz"), model)
return model.predictor
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