Problem# 3 Usually Softmax function is used as an activation function in the output layer of a neural network. Softmax function is defined as follows. 03 (1) s(yi) = ₁³ Compute the output a Softmax function using TensorFlow for the following 2 input vectors. V1 (2.3, 1.2, 0.3, 0.0) • V2 = (1.9, 1.7, 2.6, 0.2, 1.3) Answer: Input Vector 1 V1(2.3, 1.2, 0.3, 0.0) 2 V2 (1.9, 1.7, 2.6, 0.2, 1.3) Softmax [0.6375659 0.21222727 0.08628516 0.06392162] (0.21910708 0.17938972 0.44122744 0.04002726 0.12024851)
Problem# 3 Usually Softmax function is used as an activation function in the output layer of a neural network. Softmax function is defined as follows. 03 (1) s(yi) = ₁³ Compute the output a Softmax function using TensorFlow for the following 2 input vectors. V1 (2.3, 1.2, 0.3, 0.0) • V2 = (1.9, 1.7, 2.6, 0.2, 1.3) Answer: Input Vector 1 V1(2.3, 1.2, 0.3, 0.0) 2 V2 (1.9, 1.7, 2.6, 0.2, 1.3) Softmax [0.6375659 0.21222727 0.08628516 0.06392162] (0.21910708 0.17938972 0.44122744 0.04002726 0.12024851)
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![Problem#3
Usually Softmax function is used as an activation function in the output layer of a neural network.
Softmax function is defined as follows.
Compute the output a Softmax function using TensorFlow for the following 2 input vectors.
V1 = (2.3, 1.2, 0.3, 0.0)
• V2 = (1.9, 1.7, 2.6, 0.2, 1.3)
Answer:
1
@y(i)
s(yi) = ₁³
2
Input Vector
V1 = (2.3, 1.2, 0.3, 0.0)
V2 = (1.9, 1.7, 2.6, 0.2, 1.3)
Softmax
[0.6375659 0.21222727 0.08628516 0.06392162]
(0.21910708 0.17938972 0.44122744 0.04002726
0.12024851)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F75d451d4-8631-47c4-96fa-d3dc34739cc7%2Ff4459306-5b0b-4b64-a78c-ab3bb8f3b8a2%2F80vf60p_processed.png&w=3840&q=75)
Transcribed Image Text:Problem#3
Usually Softmax function is used as an activation function in the output layer of a neural network.
Softmax function is defined as follows.
Compute the output a Softmax function using TensorFlow for the following 2 input vectors.
V1 = (2.3, 1.2, 0.3, 0.0)
• V2 = (1.9, 1.7, 2.6, 0.2, 1.3)
Answer:
1
@y(i)
s(yi) = ₁³
2
Input Vector
V1 = (2.3, 1.2, 0.3, 0.0)
V2 = (1.9, 1.7, 2.6, 0.2, 1.3)
Softmax
[0.6375659 0.21222727 0.08628516 0.06392162]
(0.21910708 0.17938972 0.44122744 0.04002726
0.12024851)
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