Is Anderson Cooper of CNN anti-white?

Disgusting, right?

It is now fashionable to openly celebrate the decline of a particular race in the country.

When the Democrats passed the Hart Cellar Act of 1965, they assured the Senate that the law would not change the country's ethnic composition. But now they say that this is a good thing.

If this is the policy of the Democrats, why should not whites organize and start the policy of white identity and take care of our racial interests?

Anderson Cooper could be white. They are elites who live 97 percent in lily-white quarters. They do not experience the non-white quarters.

They signal virtue for money in MSM.

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Machine Learning – CNN prediction of a class and accuracy

My model is a binary classifier.

With the same exact architecture, the model sometimes gets high accuracies (90% etc), sometimes only one class is predicted (so the accuracy stays with a number all the time), and sometimes I get a loss value of "nan". (Either too big or too small, that the loss value is not a number, I guess).

I tried to simplify my architecture (on 2 Conv2D layers and 2 dense layers), randomize my kernel initializers (2), and change the learning rate, but none of these solves the problem of inconsistency (possibly) Help the model achieve high accuracy. However, if I do it again without changing any code, I get a completely different result, either because of the immutable precision, because only one class is predicted all the time, or because of the "nan" loss.

How can I solve this problem:
1st model with unchanging predictions for the entire run (always predicting only one class).
2. Inconsistent and non-reproducible results (if the above problems occur and are not resolved without changing the code)
3. Random retrieval of nano-loss values. (How can I get rid of them permanently?)

Many Thanks!!!!

Machine Learning – Use CNN with two inputs for prediction

I have a record like this:

q1 q2 label
ccc ddd 1
zzz yyy 0
, , ,
, , ,

Where q1 and q2 are sentences and the name indicates whether they are duplicates or not.

Now I am confused because I have two inputs q1 and q2 to concatenate both for the prediction. I have created two CNN functions for both columns and I want to concatenate them.

my cnn function:

                            def cnn_model (FILTER_SIZES, 
# Filter sizes as a list
MAX_NB_WORDS, 
# Total number of words
MAX_DOC_LEN, 
# max words in a document
EMBEDDING_DIM = 200, 
# Word vector dimension
NUM_FILTERS = 64, 
# Number of filters for all sizes
DROP_OUT = 0.5, 
# Cancellation rate
NUM_OUTPUT_UNITS = 1, 
# Number of output units
NUM_DENSE_UNITS = 100, 
# Number of units in dense layer
PRETRAINED_WORD_VECTOR = None, 
# Specifies whether trained word vectors should be used
LAM = 0.0):
# Regularization coefficient

main_input = Input (shape = (MAX_DOC_LEN,), 
dtype = & # 39; int32 & # 39 ;, name = & # 39; main_input & # 39;

if PRETRAINED_WORD_VECTOR is not None:
embed_1 = Embedding (input_dim = MAX_NB_WORDS + 1, 
output_dim = EMBEDDING_DIM, 
input_length = MAX_DOC_LEN, 
# Use ready-made word vectors
Weights =[PRETRAINED_WORD_VECTOR], 
# Word vectors can be further tuned
Set # to False when using static word vectors
trainable = true, 
name = & # 39; embedding & # 39;) (main_input)
otherwise:
embed_1 = Embedding (input_dim = MAX_NB_WORDS + 1, 
output_dim = EMBEDDING_DIM, 
input_length = MAX_DOC_LEN, 
name = & # 39; embedding & # 39;) (main_input)
# Add convolution-pooling-flat block
conv_blocks = []
   for f in FILTER_SIZES:
conv = Conv1D (filters = NUM_FILTERS, kernel_size = f, 
Activation = & # 39; relu & # 39 ;, name = & # 39; conv_ & # 39; + str (f)) (embed_1)
conv = MaxPooling1D (MAX_DOC_LEN-f + 1, name = max max _ # + str (f)) (conv)
conv = flattening (name = flat flat _ # + str (f)) (conv)
conv_blocks.append (conv)

if len (conv_blocks)> 1:
z = concatenate (name = & # 39; concatenate & # 39;) (conv_blocks)
otherwise:
z = conv_blocks[0]


   dense = dense (NUM_DENSE_UNITS, activation = & # 39; relu & # 39 ;, 
kernel_regularizer = l2 (LAM), name = & # 39; density & # 39;; (drop)

model = Model (inputs = main_input, outputs = tight)

model.compile (loss = "binary_crossentropy", 
optimizer = "adam", metrics =["accuracy"])

return model

First I have the pad sequence of the two columns:

            tokenizer = tokenizer (num_words = MAX_NB_WORDS)
tokenizer.fit_on_texts (data["q1"])

# set the dense units
dense_units_num = num_filters * len (FILTER_SIZES)

BTACH_SIZE = 32
NUM_EPOCHES = 100

sequence_1 = tokenizer. 
texte_zu_folgen (data["q1"])
#print (sequence_1)

sequence_2 = tokenizer. 
texte_zu_folgen (data["q2"])

Sequences = Sequences_1 + Sequences_2

output_units_num = 1



# Pad all sequences in the same length
# If a sentence is longer than maxlen, fill it up on the right
# If a sentence is shorter than maxlen, shorten it to the right
padded_sequences = pad_sequences (sequence, 
maxlen = MAX_DOC_LEN, 
padding = & # 39; post & # 39; 
truncating = & # 39; post & # 39;) `

Now I've made two models like this for both columns:

                left_cnn = cnn_model (FILTER_SIZES, MAX_NB_WORDS, 
MAX_DOC_LEN, 
NUM_FILTERS = num_filters, 
NUM_OUTPUT_UNITS = output_units_num, 
NUM_DENSE_UNITS = density_units_number, 
PRETRAINED_WORD_VECTOR = None)

right_cnn = cnn_model (FILTER_SIZES, MAX_NB_WORDS, 
MAX_DOC_LEN, 
NUM_FILTERS = num_filters, 
NUM_OUTPUT_UNITS = output_units_num, 
NUM_DENSE_UNITS = density_units_number, 
PRETRAINED_WORD_VECTOR = None)

Now I do not know how to link these two models. And what to do next!

Those on Yahoo tell us if you really believe that CNN feeds are not fake news and act as journalists as actors?

This child of a newspaper publisher grew up with journalists. Yes, CNN is a biased source, but they rely on facts to express this bias. On the other hand, Fox just does stuff. Therefore, I reject your reproach as a further projection from the extreme neurotic law.

Only Fox has argued in court of all news networks that the First Amendment protects their right to lie. The others have some standards even in this age.

,

Machine Learning – visualize and understand CNN with Mathematica

The famous 2013 article by Zeiler and Fergus "Visualization and Understanding of Convolution Networks" suggests a method to understand the behavior of CNN using one (or more) DeConv networks in conjunction with the original CNN.

The DeConv networks used use a set of unpooling and deconvolutional layers to reconstruct the features in the input image that are responsible for activating a particular feature map in a given layer.

These, however, use "Maximum location switchto undo the max pooling process, which, if I'm right, is to merge layers that are one argmax Operation, whereby the positions can be determined, from which the pooled maxima originate.

Unfortunately, PoolingLayer does not accept argmax as function Possibility.

Is it possible to circumvent this restriction and theMaximum location switchOr is there another technique that is applicable in Mathematica to produce a visualization similar to that proposed by Zeiler and Fergus to understand which features activate a given plane?

[ Politics ] Open Question: Now that there is no Trump / Russian Collusion, what about the credibility of CNN, MSNBC, Adam Schiff, Eric Swalwell and other fools?

[ Politics ] Open Question: Now that there is no Trump / Russian Collusion, what about the credibility of CNN, MSNBC, Adam Schiff, Eric Swalwell and other fools? ,

How did CNN know that there would be a raid on Roger Stone's house? They even filmed the FBI raid !!?

Normally, the grand jury will meet on Friday. This week they met on Thursday. This indicated to CNN that there would likely be unusual activity in connection with an indictment.

So they put away the houses of the people most likely on Mueller's list, and Roger Stone was one of them.

It's called "journalism." It takes thought, time and money. Maybe Fox News should practice journalism if they want to get those shovels.

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