So let’s assume that you are in the directory: /somePath/tensorflow/tutorial (and this is your working directory). All you need to do is to download the input_data.py file and place it like this. Let’s assume that the file name you invoke: import input_data mnist = input_data.read_data_sets(“MNIST_data/”, one_hot=True) … is main.py and it is also in the … Read more
As stated in the torch.nn.CrossEntropyLoss() doc: This criterion combines nn.LogSoftmax() and nn.NLLLoss() in one single class. Therefore, you should not use softmax before.
For TensorFlow 2.2 none of the other answers worked when the CUBLAS_STATUS_ALLOC_FAILED problem was encountered. Found a solution on https://www.tensorflow.org/guide/gpu: import tensorflow as tf gpus = tf.config.experimental.list_physical_devices(‘GPU’) if gpus: try: # Currently, memory growth needs to be the same across GPUs for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) logical_gpus = tf.config.experimental.list_logical_devices(‘GPU’) print(len(gpus), “Physical GPUs,”, len(logical_gpus), “Logical … Read more
Download the training/test images and labels: train-images-idx3-ubyte.gz: training set images train-labels-idx1-ubyte.gz: training set labels t10k-images-idx3-ubyte.gz: test set images t10k-labels-idx1-ubyte.gz: test set labels And uncompress them in a workdir, say samples/. Get the python-mnist package from PyPi: pip install python-mnist Import the mnist package and read the training/test images: from mnist import MNIST mndata = MNIST(‘samples’) … Read more
Generally Your pkl file is, in fact, a serialized pickle file, which means it has been dumped using Python’s pickle module. To un-pickle the data you can: import pickle with open(‘serialized.pkl’, ‘rb’) as f: data = pickle.load(f) For the MNIST data set Note gzip is only needed if the file is compressed: import gzip import … Read more