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# code is taken from https://www.tensorflow.org/tutorials/keras/classification # Tensorflow version 2.317.10 import tensorflow as tf import numpy as np fashion_mnist = tf.keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() with open('mnist_data.npy', 'wb') as f: np.save(f, train_images) np.save(f, train_labels) np.save(f, test_images) np.save(f, test_labels) |
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# this code is taken from https://www.tensorflow.org/tutorials/keras/classification # the code was modified to using pre-downloaded data instead of downloading from the API. # Tensorflow version 2.317.10 import tensorflow as tf import numpy as np fashion_mnist = tf.keras.datasets.fashion_mnist #(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() with open('mnist_data.npy', 'rb') as f: train_images = np.load(f) train_labels = np.load(f) test_images = np.load(f) test_labels = np.load(f) class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] # Explore Data train_images.shape len(train_labels) test_images.shape len(test_labels) # Preprocess Data train_images = train_images / 255.0 test_images = test_images / 255.0 tf.debugging.set_log_device_placement(True) strategy = tf.distribute.MirroredStrategy() with strategy.scope() : model = tf.keras.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(10) ]) model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) model.fit(train_images, train_labels, epochs=10) test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2) print('\n Test loss:', test_loss) print('\nTest accuracy:', test_acc) # 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. |
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#!/bin/bash
#SBATCH -p gpu # Specify partition [Compute/Memory/GPU]
#SBATCH -N 1 -c 16 # Specify number of nodes and processors per task
#SBATCH --gpus-per-node=4 # Specify number of GPU per task
#SBATCH --ntasks-per-node=4 # Specify tasks per node
#SBATCH -t 120:00:00 # Specify maximum time limit (hour: minute: second)
#SBATCH -A ltxxxxxx # Specify project name
#SBATCH -J JOBNAME # Specify job name
module load Apptainer/1.1.6 # Load the Apptainer module
apptainer exec --nv -B $PWD:$PWD tensorflow_2.17.0-gpu.sif python3 MNIST.py # Run your program |
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