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476455e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | # Copyright Amazon.com, Inc. or its affiliates. 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. 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, absolute_import
import argparse
import numpy as np
import os
import joblib
from sklearn import svm
def preprocess_mnist(raw, withlabel, ndim, scale, image_dtype, label_dtype, rgb_format):
images = raw["x"]
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"].astype(label_dtype)
return images, labels
return images
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Data and model checkpoints directories
parser.add_argument("--epochs", type=int, default=-1)
parser.add_argument("--output-data-dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"])
parser.add_argument("--model-dir", type=str, default=os.environ["SM_MODEL_DIR"])
parser.add_argument("--train", type=str, default=os.environ["SM_CHANNEL_TRAIN"])
parser.add_argument("--test", type=str, default=os.environ["SM_CHANNEL_TEST"])
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,
}
# Preprocess MNIST data
train_images, train_labels = preprocess_mnist(train_file, **preprocess_mnist_options)
test_images, test_labels = preprocess_mnist(test_file, **preprocess_mnist_options)
# Set up a Support Vector Machine classifier to predict digit from images
clf = svm.SVC(gamma=0.001, C=100.0, max_iter=args.epochs)
# Fit the SVM classifier with the images and the corresponding labels
clf.fit(train_images, train_labels)
# Print the coefficients of the trained classifier, and save the coefficients
joblib.dump(clf, os.path.join(args.model_dir, "model.joblib"))
def model_fn(model_dir):
clf = joblib.load(os.path.join(model_dir, "model.joblib"))
return clf
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