mnist

问题 I'm working with the MNIST data set. I have the training data vectors in one CSV file (i.e. 60,000 rows, each with 784 columns), and the labels in a separate CSV file. I want to bulk up the amount of training data, and append it to the CSV. It has to be done like this, because then the CSV file has to be fed in to a separate pipeline. I originally wrote this script: import keras from keras.preprocessing.image import ImageDataGenerator import pandas as pd X_train = pd.read_csv('train-images

问题 I'm working with the MNIST data set. I pulled down the original binary files (i.e. -ubyte; 784 columns X 60,000 rows for training data set), and converted them to CSV so I could do some processing on them. Now I want to convert the CSV files back to ubyte, to upload them to a pipeline I'm testing. I found this code, but I would have thought converting .csv to ubyte would be a common process, particularly as the MNIST data set is so famous, and I'm wondering am I missing something and if there

问题 I'm working with the MNIST data set. I pulled down the original binary files (i.e. -ubyte; 784 columns X 60,000 rows for training data set), and converted them to CSV so I could do some processing on them. Now I want to convert the CSV files back to ubyte, to upload them to a pipeline I'm testing. I found this code, but I would have thought converting .csv to ubyte would be a common process, particularly as the MNIST data set is so famous, and I'm wondering am I missing something and if there

问题 I'm working with the MNIST data set. I pulled down the original binary files (i.e. -ubyte; 784 columns X 60,000 rows for training data set), and converted them to CSV so I could do some processing on them. Now I want to convert the CSV files back to ubyte, to upload them to a pipeline I'm testing. I found this code, but I would have thought converting .csv to ubyte would be a common process, particularly as the MNIST data set is so famous, and I'm wondering am I missing something and if there

问题 i am training a mnist CNN. When i ran my code the problem is coming . I tried other answers but they do not work. I am a new to TensorFlow so can someone explain me this error. Here is my code. i am using Pycharm 2020.2. and Python 3.6 in anaconda. There is no help i could find. import tensorflow as tf from tensorflow.keras.models import Sequential mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = tf.keras.utils.normalize(x_train, axis=1) x

来源： https://stackoverflow.com/questions/44572141/why-do-we-flatten-the-data-before-we-feed-it-into-tensorflow

问题 I have a problem with classifying fully connected deep neural net with 2 hidden layers for MNIST dataset in pytorch . I want to use tanh as activations in both hidden layers, but in the end, I should use softmax . For the loss, I am choosing nn.CrossEntropyLoss() in pytorch, which (as I have found out) does not want to take one-hot encoded labels as true labels, but takes LongTensor of classes instead. My model is nn.Sequential() and when I am using softmax in the end, it gives me worse

问题 I have a problem with classifying fully connected deep neural net with 2 hidden layers for MNIST dataset in pytorch . I want to use tanh as activations in both hidden layers, but in the end, I should use softmax . For the loss, I am choosing nn.CrossEntropyLoss() in pytorch, which (as I have found out) does not want to take one-hot encoded labels as true labels, but takes LongTensor of classes instead. My model is nn.Sequential() and when I am using softmax in the end, it gives me worse

问题 When I import tensorflow import tensorflow as tf I don't get an error. However, I do get the error below. I'm using spyder if that helps. As per other questions, I ensured up to date (v1.8) tensorflow using both conda and then pip installs. This didn't resolve the issue. Please assist. import tensorflow.examples.tutorials.mnist.input_data as input_data ModuleNotFoundError: No module named 'tensorflow.examples' 回答1: Sometimes on downloading the TF, the example directory might not be available.

问题 Regarding the MNIST tutorial on the TensorFlow website, I ran an experiment (gist) to see what the effect of different weight initializations would be on learning. I noticed that, against what I read in the popular [Xavier, Glorot 2010] paper, learning is just fine regardless of weight initialization. The different curves represent different values for w for initializing the weights of the convolutional and fully connected layers. Note that all values for w work fine, even though 0.3 and 1.0