What's the difference between keras.datasets.mnist and tensorflow.examples.tutorials.mnist?
I am analysing this DCGAN. When I use input_data
from tensorflow.examples.tutorials.mnist
, as seen in line 144:
self.x_train = input_data.read_data_sets("mnist",
one_hot=True).train.images
I obtain reasonably good results:
Though when I use mnist
from keras.datasets
and the 144th line looks like this:
(xtr, ytr), (xte, yte) = mnist.load_data();
self.x_train = xtr
I get horribly bad results:
I have checked manually a few images from both datasets and they are quite similar.
So what is the difference between keras.datasets.mnist
and tensorflow.examples.tutorials.mnist
? Why are the resulting images so different? What am I doing wrong with keras.datasets.mnist
?
tensorflow keras neural-network mnist dcgan
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I am analysing this DCGAN. When I use input_data
from tensorflow.examples.tutorials.mnist
, as seen in line 144:
self.x_train = input_data.read_data_sets("mnist",
one_hot=True).train.images
I obtain reasonably good results:
Though when I use mnist
from keras.datasets
and the 144th line looks like this:
(xtr, ytr), (xte, yte) = mnist.load_data();
self.x_train = xtr
I get horribly bad results:
I have checked manually a few images from both datasets and they are quite similar.
So what is the difference between keras.datasets.mnist
and tensorflow.examples.tutorials.mnist
? Why are the resulting images so different? What am I doing wrong with keras.datasets.mnist
?
tensorflow keras neural-network mnist dcgan
add a comment |
I am analysing this DCGAN. When I use input_data
from tensorflow.examples.tutorials.mnist
, as seen in line 144:
self.x_train = input_data.read_data_sets("mnist",
one_hot=True).train.images
I obtain reasonably good results:
Though when I use mnist
from keras.datasets
and the 144th line looks like this:
(xtr, ytr), (xte, yte) = mnist.load_data();
self.x_train = xtr
I get horribly bad results:
I have checked manually a few images from both datasets and they are quite similar.
So what is the difference between keras.datasets.mnist
and tensorflow.examples.tutorials.mnist
? Why are the resulting images so different? What am I doing wrong with keras.datasets.mnist
?
tensorflow keras neural-network mnist dcgan
I am analysing this DCGAN. When I use input_data
from tensorflow.examples.tutorials.mnist
, as seen in line 144:
self.x_train = input_data.read_data_sets("mnist",
one_hot=True).train.images
I obtain reasonably good results:
Though when I use mnist
from keras.datasets
and the 144th line looks like this:
(xtr, ytr), (xte, yte) = mnist.load_data();
self.x_train = xtr
I get horribly bad results:
I have checked manually a few images from both datasets and they are quite similar.
So what is the difference between keras.datasets.mnist
and tensorflow.examples.tutorials.mnist
? Why are the resulting images so different? What am I doing wrong with keras.datasets.mnist
?
tensorflow keras neural-network mnist dcgan
tensorflow keras neural-network mnist dcgan
asked Dec 31 '18 at 11:20
LeftismerLeftismer
528
528
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1 Answer
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It is very likely that the images in tensorflow.examples.tutorials.mnist
have been normalized to the range [0, 1] and therefore you obtain better results. Whereas, the values in MNIST dataset in Keras are in the range [0, 255] and you are expected to normalize them (if needed, of course). Try this:
(xtr, ytr), (xte, yte) = mnist.load_data()
xtr = xtr.astype('float32') / 255.0
xte = xte.astype('float32') / 255.0
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
It is very likely that the images in tensorflow.examples.tutorials.mnist
have been normalized to the range [0, 1] and therefore you obtain better results. Whereas, the values in MNIST dataset in Keras are in the range [0, 255] and you are expected to normalize them (if needed, of course). Try this:
(xtr, ytr), (xte, yte) = mnist.load_data()
xtr = xtr.astype('float32') / 255.0
xte = xte.astype('float32') / 255.0
add a comment |
It is very likely that the images in tensorflow.examples.tutorials.mnist
have been normalized to the range [0, 1] and therefore you obtain better results. Whereas, the values in MNIST dataset in Keras are in the range [0, 255] and you are expected to normalize them (if needed, of course). Try this:
(xtr, ytr), (xte, yte) = mnist.load_data()
xtr = xtr.astype('float32') / 255.0
xte = xte.astype('float32') / 255.0
add a comment |
It is very likely that the images in tensorflow.examples.tutorials.mnist
have been normalized to the range [0, 1] and therefore you obtain better results. Whereas, the values in MNIST dataset in Keras are in the range [0, 255] and you are expected to normalize them (if needed, of course). Try this:
(xtr, ytr), (xte, yte) = mnist.load_data()
xtr = xtr.astype('float32') / 255.0
xte = xte.astype('float32') / 255.0
It is very likely that the images in tensorflow.examples.tutorials.mnist
have been normalized to the range [0, 1] and therefore you obtain better results. Whereas, the values in MNIST dataset in Keras are in the range [0, 255] and you are expected to normalize them (if needed, of course). Try this:
(xtr, ytr), (xte, yte) = mnist.load_data()
xtr = xtr.astype('float32') / 255.0
xte = xte.astype('float32') / 255.0
edited Dec 31 '18 at 12:10
answered Dec 31 '18 at 11:50
todaytoday
10.8k21837
10.8k21837
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