mnist

问题 In the first chapter of Deep Learning with TensorFlow , it gives an example on how to build a simple neural network for recognizing handwritten digits. According to its description, the code bundle for the book can be found at GitHub. From the context, I think section Running a simple TensorFlow 2.0 net and establishing a baseline uses the code same with Deep-Learning-with-TensorFlow-2-and-Keras/mnist_V1.py. When I run this example code, it gives me the following output: The snapshot from the

问题 I am making a Stochastic Gradient Descent Classifier (SGDClassifier) using scikit- learn. While Fitting my training data (of shape (60000,784)), I am getting memory error. How to fix it? I have already tried switching from 32 bit to 64 bit IDE. And reducing the training data will decrease the performance (that is basically not the option). Code: (Python 3.7) # Classification Problem # Date: 1st September 2019 # Author: Pranay Saha import pandas as pd x_train= pd.read_csv('mnist_train.csv') y

问题 here is the whole code I use import os import numpy as np import matplotlib.pyplot as plt from tqdm import tqdm from keras.layers import Input from keras.models import Model, Sequential from keras.layers.core import Dense, Dropout from keras.layers.advanced_activations import LeakyReLU from keras.datasets import mnist from keras.optimizers import Adam from keras import initializers os.environ["KERAS_BACKEND"] = "tensorflow" np.random.seed(10) random_dim = 100 def load_mnist_data(): # load the

问题 I have read this example https://github.com/fchollet/keras/blob/master/examples/mnist_mlp.py and decide to use this idea to my base because this is the simplest NN for Keras. This is my base https://drive.google.com/file/d/0B-B3QUQOzGZ7WVhzQmRsOTB0eFE/view (you can download my csv file, it's only 83Kb ) This is picture my base: base.shape = (891, 23) import keras from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout from keras.optimizers

问题 I have read this example https://github.com/fchollet/keras/blob/master/examples/mnist_mlp.py and decide to use this idea to my base because this is the simplest NN for Keras. This is my base https://drive.google.com/file/d/0B-B3QUQOzGZ7WVhzQmRsOTB0eFE/view (you can download my csv file, it's only 83Kb ) This is picture my base: base.shape = (891, 23) import keras from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout from keras.optimizers

问题 I wrote a convolutional neural network in tensorflow to perform on the mnist dataset. Everything works just fine, but i want to save the model with the tf.train.Saver(). How am i gonna do it? Here is my code: from __future__ import print_function import tensorflow as tf # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) # Parameters learning_rate = 0.001 training_iters = 200000 batch_size = 128 display

问题 I wrote a convolutional neural network in tensorflow to perform on the mnist dataset. Everything works just fine, but i want to save the model with the tf.train.Saver(). How am i gonna do it? Here is my code: from __future__ import print_function import tensorflow as tf # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) # Parameters learning_rate = 0.001 training_iters = 200000 batch_size = 128 display

问题 I used this code almost exactly, just changing the line: f = gzip.open("../data/mnist.pkl.gz", 'rb') training_data, validation_data, test_data = cPickle.load(f) to these lines: import pickle as cPickle f = gzip.open("mnist.pkl.gz", 'rb') u = cPickle._Unpickler(f) u.encoding='latin1' training_data, validation_data, test_data = u.load() to account for pickling issues.The original mnist.pkl.gz was downloaded from his repo (available here), or the code to generate the .pkl.gz is here. The output

问题 I am setting up my first neural network with keras and tensorflow. I got my input into an array of shape (60000, 28, 28), but when I try and feed it to the model I get an error that the input shape is wrong. I have tried multiple different input shapes including (60000, 28, 28) (1, 28, 28) (28, 28) (28, 28, 1) but none of them seem to work. model = kr.Sequential() model.add(InputLayer(input_shape=(60000, 28, 28))) model.add(Dense(units=784, activation='relu')) model.add(Dense(units=392,

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