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Two gradients with two distinct sections with just one div element


Two gradients with two distinct sections with just one div element

By : user2955516
Date : November 22 2020, 10:40 AM
seems to work fine You can get this effect, but you will need to set overflow hidden on the div and to set the background in a after pseudo class
code :
.test {
    width: 400px;
    height: 300px;
    border: solid 1px black;
    position: relative;
    overflow: hidden;
}

.test:after {
    content: "";
    position: absolute;
    width: 160%;
    height: 160%;
    top: -30%;
    left: -30%;
    background-image: linear-gradient(-210deg, #95D428 0%, #20560B 100%), linear-gradient(-210deg, #F5515F 0%, #8E0A1E 100%);
    background-position: top left, top right;
    background-size: 50% 100%;
    background-repeat: no-repeat;
    -webkit-transform: rotate(30deg);
}
<div class="test"></div>


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CSS linear gradients rainbow wheel... not showing all 12 sections

CSS linear gradients rainbow wheel... not showing all 12 sections


By : Ahmed Ayman
Date : March 29 2020, 07:55 AM
Hope this helps I'm only seeing 11 colours in your question: pink, yellow, cyan, orange, green, lawngreen, red, fuchsia, black, hotpink, purple.
I added this line before the lawngreen and it seems to work:
code :
linear-gradient(30deg, transparent 63.61%, blue 31.59%) 100% 100%,
body { background: white; } 

.colorWheel {
  position: relative;
  margin: 1em auto;
  /* border: solid 2em white; */
  width: 16em; height: 16em;
  border-radius: 50%;
  -ms-transform: rotate(45deg);
  transform: rotate(45deg);
  background: 
    linear-gradient(30deg, #9bcb61 36.78%, transparent 36.78%) 0 0,
    linear-gradient(60deg, #f6e04e 63.22%, transparent 63.22%) 0 0,
    linear-gradient(90deg, #f4c64d 100%, transparent 100%) 0 0,
    linear-gradient(-30deg, #f0463e 36.78%, transparent 36.78%) 100% 0,
    linear-gradient(-60deg, #f57d42 63.22%, transparent 63.22%) 100% 0,
    
    linear-gradient(30deg, transparent 63.22%, #c14273 63.22%) 100% 100%,
    linear-gradient(60deg, transparent 36.78%, #86489b 36.78%) 100% 100%,
    linear-gradient(90deg, transparent 0%, #008fca 0%) 100% 100%,
    linear-gradient(-30deg, transparent 63.22%, #82ba75 63.22%) 0 100%,
    linear-gradient(-60deg, transparent 36.78%, #00b8a9 36.78%) 0 100%,
    
    #f9a260 linear-gradient(#00bced, #00bced) 50% 100%;
  background-repeat: no-repeat;
  background-size: 50% 50%;
}

.colorWheel:after {
  position: absolute;
  top: 50%; left: 50%;
  margin: -7em;
  width: 14em; height: 14em;
  border-radius: 50%;
  background: white;
  content: '';
}

.center {
  text-align: center;
  position: absolute;
  top: 22%;
  transform: translateY(-50%);
  padding: 1.5em;
  font-size: 1.4em;
  font-color: black;
  z-index: 1;
  -ms-transform: rotate(315deg);
  transform: rotate(315deg);
}
<div class='colorWheel'>
  <div class='center'>Lorem ipsum dolor sit amet, consectetur adipiscing elit.</div>
</div>
tensorflow ValueError: No gradients provided for any variable, check your graph for ops that do not support gradients

tensorflow ValueError: No gradients provided for any variable, check your graph for ops that do not support gradients


By : user2312747
Date : March 29 2020, 07:55 AM
it fixes the issue You got this problem because the arguments you passed into tf.nn.softmax_cross_entropy_with_logits are not what it wants. From the doc of tf.nn.softmax_cross_entropy_with_logits:
code :
from sklearn import datasets
import random
import tensorflow as tf
import numpy as np
from sklearn.preprocessing import OneHotEncoder

wine=datasets.load_wine()
wine_data = wine.data
onehotencoder = OneHotEncoder()
wine_target = onehotencoder.fit_transform(wine.target[...,np.newaxis]).toarray()

def generate_batch(batch_size,wine):
    batch_x=[]
    batch_y=[]
    for _ in range(batch_size):
        index=random.randint(0,177)
        batch_y.append(wine_target[index])
        batch_x.append(wine_data[index])
    return batch_x,batch_y

def inference(x):
    with tf.variable_scope('layer1'):
        weight1 = tf.get_variable('weight', [13, 7], initializer=tf.truncated_normal_initializer(stddev=0.1))
        bias1 = tf.get_variable('bias', [7], initializer=tf.constant_initializer(0.1))
        layer1 = tf.nn.relu(tf.matmul(x, weight1) + bias1)
    weight2 =tf.get_variable('weight', [7, 3], initializer=tf.truncated_normal_initializer(stddev=0.1))
    bias2 = tf.get_variable('bias', [3], initializer=tf.constant_initializer(0.1))
    logit = tf.matmul(layer1, weight2) + bias2
    return logit

x=tf.placeholder(tf.float32,[None,13])
y_=tf.placeholder(tf.float32,[None, 3])
y=inference(x)

cross_entropy=tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y)
cross_entropy_mean=tf.reduce_mean(cross_entropy)

train_step=tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy_mean)
correct_prediction = tf.equal(y ,y_)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    for i in range(2000):
        data,target=generate_batch(20,wine)
        sess.run(train_step,feed_dict={x:data,y_:target})
My LSTM learns, loss decreases, but Numerical Gradients don't match Analytical Gradients

My LSTM learns, loss decreases, but Numerical Gradients don't match Analytical Gradients


By : Gurwinder Gora
Date : March 29 2020, 07:55 AM
To fix the issue you can do Solved it! in my check_grad, I need to build the caches which is served to df_analytical, but in so doing, I also overwrite the h and c which should have been np.zeroes.
code :
y, outputs, loss, h, c, caches = f(params, h, c, inputs, targets)

_, _, loss_minus, _, _, _ = f(params, h, c, inputs, targets)
p.flat[pix] = old_val
_, outputs, loss, _, _, caches = f(params, h, c, inputs, targets)
Build 3 distinct sections in HTML body tag

Build 3 distinct sections in HTML body tag


By : user3578799
Date : March 29 2020, 07:55 AM
around this issue How about this:
there were a bit of errors in the html. Remember to close html tags correctly. Hope this helps.
code :
<body>
  <div id="wrapper">
    <div id="content">
      <p>text</p>
    </div>
  </div>

  <div id="midsection">
    <div id="mid-content">Mid</div>
  </div>

  <footer id="footer">
    <div class="footer">Footer</div>       
  </footer>
</body>
html, body {
    margin: 0px;
    padding: 0px;
    min-height: 100%;
    height: 100%;
    background-color: #212121;
}

  #wrapper {

    min-height: 100%;
    height: auto !important;
    margin-bottom: -300px; /* the bottom margin is the negative value of the footer's total height */
  }

  #wrapper:after {
    content: "";
    display: block;
    height: 300px; /* the footer's total height */
  }

  #content {
    height: 100%;
  }

  #midsection {
    background-color: white;
    height: 300px; /* the footer's total height */
    margin-bottom: -100px;
  }

  #midsection:after {
    content: "";
    display: block;
    height: 100px;
  }

#footer {
  background-color: #212121;
  height: 200px;
}
Finding distinct sections in an image

Finding distinct sections in an image


By : bg080
Date : March 29 2020, 07:55 AM
Any of those help This is called 8-connected region growing. It is a standard technique in image processing, you select a seed pixel and "grow". This can be done with BFS in linear time in the number of pixels:
keep a queue with the fringe, put there the initial seed get an element from the queue call it current point and added to your "region" list push to the queue the neighbors of the current point that are one, never push to the queue a point that you've already pushed to the queue finish when the queue is empty.
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