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author | alexander <alexander.efremov@arm.com> | 2021-03-26 21:42:19 +0000 |
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committer | Kshitij Sisodia <kshitij.sisodia@arm.com> | 2021-03-29 16:29:55 +0100 |
commit | 3c79893217bc632c9b0efa815091bef3c779490c (patch) | |
tree | ad06b444557eb8124652b45621d736fa1b92f65d /model_conditioning_examples/training_utils.py | |
parent | 6ad6d55715928de72979b04194da1bdf04a4c51b (diff) | |
download | ml-embedded-evaluation-kit-3c79893217bc632c9b0efa815091bef3c779490c.tar.gz |
Opensource ML embedded evaluation kit21.03
Change-Id: I12e807f19f5cacad7cef82572b6dd48252fd61fd
Diffstat (limited to 'model_conditioning_examples/training_utils.py')
-rw-r--r-- | model_conditioning_examples/training_utils.py | 61 |
1 files changed, 61 insertions, 0 deletions
diff --git a/model_conditioning_examples/training_utils.py b/model_conditioning_examples/training_utils.py new file mode 100644 index 0000000..3467b2a --- /dev/null +++ b/model_conditioning_examples/training_utils.py @@ -0,0 +1,61 @@ +# Copyright (c) 2021 Arm Limited. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# 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. +""" +Utility functions related to data and models that are common to all the model conditioning examples. +""" +import tensorflow as tf +import numpy as np + + +def get_data(): + """Downloads and returns the pre-processed data and labels for training and testing. + + Returns: + Tuple of: (train data, train labels, test data, test labels) + """ + + # To save time we use the MNIST dataset for this example. + (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() + + # Convolution operations require data to have 4 dimensions. + # We divide by 255 to help training and cast to float32 for TensorFlow. + x_train = (x_train[..., np.newaxis] / 255.0).astype(np.float32) + x_test = (x_test[..., np.newaxis] / 255.0).astype(np.float32) + + return x_train, y_train, x_test, y_test + + +def create_model(): + """Create and returns a simple Keras model for training MNIST. + + We will use a simple convolutional neural network for this example, + but the model optimization methods employed should be compatible with a + wide variety of CNN architectures such as Mobilenet and Inception etc. + + Returns: + Uncompiled Keras model. + """ + + keras_model = tf.keras.models.Sequential([ + tf.keras.layers.Conv2D(32, 3, padding='same', input_shape=(28, 28, 1), activation=tf.nn.relu), + tf.keras.layers.Conv2D(32, 3, padding='same', activation=tf.nn.relu), + tf.keras.layers.MaxPool2D(), + tf.keras.layers.Conv2D(32, 3, padding='same', activation=tf.nn.relu), + tf.keras.layers.MaxPool2D(), + tf.keras.layers.Flatten(), + tf.keras.layers.Dense(units=10, activation=tf.nn.softmax) + ]) + + return keras_model |