innat

import tensorflow as tf
# https://github.com/tensorflow/tensorflow/issues/55646

def unique_uniform(num_samples, 
                   minval, 
                   maxval, 
                   seed, 
                   shape, 
                   dtype):  # maxval is inclusive

prerakmody

"""
pip install tensorflow
pip install tf2onnx keras2onnx onnxmltools
"""

import os
import pdb
import json
import traceback
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" 

innat

import tensorflow as tf

# credit: https://stackoverflow.com/a/66524901/9215780
class CustomTrainStep(tf.keras.Model):
    def __init__(self, n_gradients, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.n_gradients = tf.constant(n_gradients, dtype=tf.int32)
        self.n_acum_step = tf.Variable(0, dtype=tf.int32, trainable=False)
        self.gradient_accumulation = [tf.Variable(tf.zeros_like(v, dtype=tf.float32), 
                                                  trainable=False) for v in self.trainable_variables]

innat

All we need to worry about _sass/_highlights.scss.

First visit This web page to see live example.

Copy the following piece of code and paste or replace all code there in _highlights.scss file.

Approach_One

highlight { 
  background-color: #ffffff;

pca_animation.m

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Matlab code to produce PCA animations shown here:
% http://stats.stackexchange.com/questions/2691
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Static image

clear all
rng(42)

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

baraldilorenzo

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman