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barxatty [35]
2 years ago
7

From a neural network with 11 input variables, one hidden layer with three hidden units, and one output variable, how many param

eters (weights and biases) needs to be estimated by this model?
Computers and Technology
1 answer:
e-lub [12.9K]2 years ago
6 0

Answer:

40

Explanation:

Given that:

A neural network with 11 input variables possess;

one hidden layer with three hidden units; &

one output variable

For every input, a variable must go to every node.

Thus, we can calculate the weights of weight with respect to connections to input and hidden layer by using the formula:

= ( inputs + bias) × numbers of nodes

= (11 + 1 ) × 3

= 12 × 3

= 36 weights

Also, For one hidden layer (with 3 nodes) and one output

The entry result for every hidden node will go directly to the output

These results will have weights associated with them before computed in the output node.

Thus; using the formula

= (numbers of nodes + bais) output, we get;

= ( 3+ 1 ) × 1

= 4 weights

weights with respect to input and hidden layer total = 36

weights with respect to hidden and output layer total = 4

Finally, the sum of both weights is = 36 + 4

= 40

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Answer:

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fname = input("Enter the translation file name: ")

with open(fname) as file_in:

   lines = []

   for line in file_in:

       lines.append(line.rstrip('\n'))

myDict = {}

for i in range(len(lines)):

x = lines[i].split(":")

myDict[x[0].lower()] = x[1].lower()

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word = input("Enter an English word: ")

while(True):

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word = input("Enter an English word: ")

Explanation:

This prompts the user for file name

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This opens the file for read operation

with open(fname) as file_in:

This creates an empty list

   lines = []

This reads through the lines of the file

   for line in file_in:

This appends each line as an element of the list

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This iterates through the list

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This splits each list element by :

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