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mnist_train.py
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mnist_train.py
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# Copyright 2021 The TensorFlow 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.
# ==============================================================================
# MIT License
#
# Copyright (c) 2017 François Chollet
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
# ==============================================================================
"""Train MNIST.
This is a basic working ML program which trains MNIST.
The code is modified from the tf.keras tutorial here:
https://www.tensorflow.org/tutorials/keras/classification
(The tutorial uses Fashion-MNIST,
but we just use "regular" MNIST for these tutorials.)
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
from typing import Tuple
import numpy as np
import tensorflow as tf
def create_arg_parser():
"""Creates arg parser."""
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--job-dir', type=str, help='Job output directory.')
parser.add_argument(
'--num_epochs', type=int, default=1, help='Number of epochs to train.')
return parser
def download_and_prep_data():
"""Download dataset and scale to [0, 1].
Returns:
tr_x: Training data.
tr_y: Training labels.
te_x: Testing data.
te_y: Testing labels.
"""
mnist_dataset = tf.keras.datasets.mnist
(tr_x, tr_y), (te_x, te_y) = mnist_dataset.load_data()
tr_x = tr_x / 255.0
te_x = te_x / 255.0
return tr_x, tr_y, te_x, te_y
def create_model():
"""Create a model for training.
Create a simple tf.keras model for training.
Returns:
The model to use for training.
"""
layers = [
tf.keras.layers.Lambda(lambda x: tf.reshape(x, (-1, 28, 28, 1))),
tf.keras.layers.Conv2D(
filters=4, kernel_size=(3, 3), padding='same', activation='relu'),
tf.keras.layers.Conv2D(
filters=8, kernel_size=(3, 3), padding='same', activation='relu')
]
layers.append(tf.keras.layers.Flatten())
layers.append(tf.keras.layers.Dense(10, activation='softmax'))
return tf.keras.Sequential(layers)
def train_and_eval(argv):
"""Trains and eval the NAS model."""
tr_x, tr_y, te_x, te_y = download_and_prep_data()
model = create_model()
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=argv.job_dir)
model.fit(
tr_x, tr_y, epochs=argv.num_epochs, callbacks=[tensorboard_callback])
model.evaluate(te_x, te_y, verbose=2)
if __name__ == '__main__':
flags = create_arg_parser().parse_args()
train_and_eval(flags)