mnist | mnist digits in javascript | Frontend Framework library
kandi X-RAY | mnist Summary
kandi X-RAY | mnist Summary
mnist digits in javascript
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mnist Examples and Code Snippets
def main(strategy):
"""Trains an MNIST model using the given tf.distribute.Strategy."""
# TODO(fengliuai): put this in some automatically generated code.
os.environ[
'TF_MLIR_TFR_LIB_DIR'] = 'tensorflow/compiler/mlir/tfr/examples/mnist'
def download(directory, filename):
"""Download (and unzip) a file from the MNIST dataset if not already done."""
filepath = os.path.join(directory, filename)
if tf.gfile.Exists(filepath):
return filepath
if not tf.gfile.Exists(directory):
def download_mnist(path):
"""
Download and unzip the dataset mnist if it's not already downloaded
Download from http://yann.lecun.com/exdb/mnist
"""
safe_mkdir(path)
url = 'http://yann.lecun.com/exdb/mnist'
filenames = [
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
print(tf.__version__)
#### Import the Fashion MNIST dataset
fashion_mnist = keras.datasets.fashion_mnist
(train_ima
try:
%tensorflow_version 2.x
except Exception:
pass
import tensorflow as tf
print(tf.__version__)
# TensorFlow and tf.keras
from tensorflow import keras
# Helper libraries
import numpy as np
#### Import the Fashion MNIST dataset
fa
I found the answer myself. Answering it as I now understand the question was confusing
Machine learning algorithms require well-designed attributes. Their performance highly depend on the attributes. Normally we must write some code to re
import tensorflow as tf
import numpy as np
import PIL
from PIL import Image
from keras.preprocessing import image
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
tf.keras.backend.image_data_form
# tf yield ops that supply dataset images and labels
x_train_batch, y_train_batch = read_and_decode_recordinput(...)
# create a basic cnn
x_train_input = Input(tensor=x_train_batch)
x_train_out = cnn_layers(x_train_input)
model = Model(i
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_3 (Conv2D) (None, 24, 2
import numpy as np
from io import StringIO
import matplotlib.pyplot as plt
from sklearn.linear_model import SGDClassifier
from tensorflow.keras.datasets import mnist
(x_tr, y_tr), (x_te, y_te) = mnist.load_dataset()
x_tr, x_te = x_tr.resh
Community Discussions
Trending Discussions on mnist
QUESTION
I writing my code within a Jupyter notebook in VS Code. I am hoping to play some of the audio within my data set. However, when I execute the cell, the console reports no errors, produces the widget, but the widget displays 0:00 / 0:00 (see below), indicating there is no sound to play.
Below, I have listed two ways to reproduce the error.
- I have acquired data from the hub data store. Looking specifically at the spoken MNIST data set, I cannot get the data from the
audio
tensor to play
ANSWER
Answered 2022-Mar-15 at 00:07Apologies for the late reply! In the future, please tag the questions with activeloop so it's easier to sort through (or hit us up directly in community slack -> slack.activeloop.ai).
Regarding the Free Spoken Digit Dataset, I managed to track the error with your usage of activeloop hub and audio display.
adding [:,0] to 9th line will help fixing display on Colab as Audio expects one-dimensional data
QUESTION
I try to implement a fully-connected model for classification using the MNIST dataset. A part of the code is the following:
...ANSWER
Answered 2022-Mar-10 at 08:19You could start off with a custom training loop using tf.GradientTape
:
QUESTION
I have generated some images from the Fashion Mnist dataset, However, I am not able to come up with a function or the way to save each image as a single file. I only have found a way to save them in groups. Can someone help me on how to save images one by one?
This is what I have for the moment:
...ANSWER
Answered 2022-Mar-13 at 17:07Try using plt.imsave
to save each image separately:
QUESTION
I'm trying to implement a gradient-free optimizer function to train convolutional neural networks with Julia using Flux.jl. The reference paper is this: https://arxiv.org/abs/2005.05955. This paper proposes RSO, a gradient-free optimization algorithm updates single weight at a time on a sampling bases. The pseudocode of this algorithm is depicted in the picture below.
I'm using MNIST dataset.
...ANSWER
Answered 2022-Jan-14 at 23:47Based on the paper you shared, it looks like you need to change the weight arrays per each output neuron per each layer. Unfortunately, this means that the implementation of your optimization routine is going to depend on the layer type, since an "output neuron" for a convolution layer is quite different than a fully-connected layer. In other words, just looping over Flux.params(model)
is not going to be sufficient, since this is just a set of all the weight arrays in the model and each weight array is treated differently depending on which layer it comes from.
Fortunately, Julia's multiple dispatch does make this easier to write if you use separate functions instead of a giant loop. I'll summarize the algorithm using the pseudo-code below:
QUESTION
I'm training GAN with MNIST and I want to visualize Generator output with noise input during training.
Here is the code:
...ANSWER
Answered 2022-Jan-15 at 02:45when you use cmap="gray"
in plt.imshow()
you must either unscale your output or set vmin and vmax.
From what I see you scaled by dividing 255, so you must multiply your data by 255 or, alternativle set vmin=0, vmax=1
Option1:
QUESTION
I'm working on Convolution Tasnet, model size I made is about 5.05 million variables.
I want to train this using custom training loops, and the problem is,
...ANSWER
Answered 2022-Jan-07 at 11:08Gradient tape triggers automatic differentiation which requires tracking gradients on all your weights and activations. Autodiff requires multiple more memory. This is normal. You'll have to manually tune your batch size until you find one that works, then tune your LR. Usually, the tune just means guess & check or grid search. (I am working on a product to do all of that for you but I'm not here to plug it).
QUESTION
I want to apply a partial tucker decomposition algorithm to minimize MNIST image tensor dataset of (60000,28,28), in order to conserve its features when applying another machine algorithm afterwards like SVM. I have this code that minimizes the second and third dimension of the tensor
...ANSWER
Answered 2021-Dec-28 at 21:05So if you look at the source code for tensorly
linked here you can see that the documentation for the function in question partial_tucker
says:
QUESTION
I have created a working CNN model in Keras/Tensorflow, and have successfully used the CIFAR-10 & MNIST datasets to test this model. The functioning code as seen below:
...ANSWER
Answered 2021-Dec-16 at 10:18If the hyperspectral dataset is given to you as a large image with many channels, I suppose that the classification of each pixel should depend on the pixels around it (otherwise I would not format the data as an image, i.e. without grid structure). Given this assumption, breaking up the input picture into 1x1 parts is not a good idea as you are loosing the grid structure.
I further suppose that the order of the channels is arbitrary, which implies that convolution over the channels is probably not meaningful (which you however did not plan to do anyways).
Instead of reformatting the data the way you did, you may want to create a model that takes an image as input and also outputs an "image" containing the classifications for each pixel. I.e. if you have 10 classes and take a (145, 145, 200) image as input, your model would output a (145, 145, 10) image. In that architecture you would not have any fully-connected layers. Your output layer would also be a convolutional layer.
That however means that you will not be able to keep your current architecture. That is because the tasks for MNIST/CIFAR10 and your hyperspectral dataset are not the same. For MNIST/CIFAR10 you want to classify an image in it's entirety, while for the other dataset you want to assign a class to each pixel (while most likely also using the pixels around each pixel).
Some further ideas:
- If you want to turn the pixel classification task on the hyperspectral dataset into a classification task for an entire image, maybe you can reformulate that task as "classifying a hyperspectral image as the class of it's center (or top-left, or bottom-right, or (21th, 104th), or whatever) pixel". To obtain the data from your single hyperspectral image, for each pixel, I would shift the image such that the target pixel is at the desired location (e.g. the center). All pixels that "fall off" the border could be inserted at the other side of the image.
- If you want to stick with a pixel classification task but need more data, maybe split up the single hyperspectral image you have into many smaller images (e.g. 10x10x200). You may even want to use images of many different sizes. If you model only has convolution and pooling layers and you make sure to maintain the sizes of the image, that should work out.
QUESTION
I want to import mnist digits digits to show in one figure, and code like that,
...ANSWER
Answered 2021-Dec-07 at 04:04I was able to reproduce this bug too. It seems to be related to the plt.tight_layout()
that you apply within the loop. Instead of doing this, use plt.subplots
to produce the axes objects first, then iterate over those instead. Once you plot everything, use tight_layout
on the opened figure:
QUESTION
I am trying to run a TensorFlow-lite model on my App on a smartphone. First, I trained the model with numerical data using LSTM and build the model layer using TensorFlow.Keras. I used TensorFlow V2.x and saved the trained model on a server. After that, the model is downloaded to the internal memory of the smartphone by the App and loaded to the interpreter using "MappedByteBuffer". Until here everything is working correctly.
The problem is in the interpreter can not read and run the model. I also added the required dependencies on the build.gradle.
The conversion code to tflite model in python:
...ANSWER
Answered 2021-Nov-24 at 00:05Referring to one of the most recent TfLite android app examples might help: Model Personalization App. This demo app uses transfer learning model instead of LSTM, but the overall workflow should be similar.
As Farmaker mentioned in the comment, try using SNAPSHOT in the gradle dependency:
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