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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. 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)
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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