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In number theory, the dispersive limit of a sequence of primes is a certain formula relating successive prime gaps to a certain polynomial. The main results are due to John Lindgren, who in 1978 proved the following theorem for all positive integers.

Theorem. For the proportion of primes less than or equal to that are not congruent to modulo is as.

For many years the formula (which is known to be true since the 1950s) was stated with an error of 0.001 or less, but more recent experimental results show that the error is around 0.0007, making the theorem an exact formula.

Lindgren goes on to conjecture that the remaining primes are less than or equal to are congruent to modulo for all. He then further assumes the Riemann hypothesis, and goes on to conjecture that this additional assumption is unnecessary.

Using Chebyshev’s theorem, one can easily derive stronger results than the dispersive limit, as follows. Given a sequence of primes, we can construct a sequence of odd integers such that is always divisible by the second prime of the form

Using the formula for the count of primes below in terms of, we see that. Thus, if

is the correct analogue of the number of primes below, one has

At this point, Chebyshev’s theorem gives a polynomial that dominates, as is always odd. Thus, if is always greater than or equal to, then

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.Q:

How to get byte value in Tensorflow

I have a numpy array of floating values. When I convert it to a Tensor, I get a TensorValue which has type tf.uint8. I want to know the value of the generated Tensor, like: 0 -> 0, 0.1 -> 256, etc.
I have tried to store the Tensor as a dense array:
x_dense = tf.constant(np.array(np.float64(data_array), dtype=np.float64))
tf.convert_to_tensor(x_dense).eval()

but then it has type tf.Dense(shape=(), dtype=tf.float64, name=’dense/value’) which doesn’t give me any information about the Tensor type.

A:

It sounds like the value is actually floating point rather than integer, but you do not say it. In any case, you could do the conversion explicitly:
x_value = tf.cast(tensor, tf.float32)

To answer your question, your dense array will be a tensor of type tf.float32. As a result, since the conversion is lossless, the result of the cast will be the same as the input. For example, the values 0, 0.1, 1, 1.1, 1.2 are all casted to the same value: 0.

A:

Update Nov 2020
The tensor in the answer by @Singhana works fine for the same input as the OP. The tensor-value approach of @thealexk also works fine. The latter is the recommended approach, but I have included it here to show that the numpy array approach is also valid.
The original answer below is relevant for older versions of Tensorflow.
Original answer:
You can use the value attribute to access the numpy float elements:
import tensorflow as tf
import numpy as np

x = tf