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-Neuron with 3 inputs¶
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-In [4]:
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-inputs = [1, 2, 3]
-weights = [0.2, 0.8, -0.5]
-bias = 2
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-output = (inputs[0]*weights[0] + inputs[1]*weights[1] + inputs[2]*weights[2] + bias)
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-print(f"Output: {output}")
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-Output: 2.3 --
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-Neuron with 4 inputs¶
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-In [5]:
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-inputs = [1.0, 2.0, 3.0, 2.5]
-weights = [0.2, 0.8, -0.5, 1.0]
-bias = 2
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-output = (inputs[0]*weights[0] + inputs[1]*weights[1] + inputs[2]*weights[2] + inputs[3]*weights[3] + bias)
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-print(output)
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-Output: 4.8 --
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-Layer of 3 neurons with 4 inputs¶
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-In [1]:
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-num_neurons = 3
-num_inputs = 4
-inputs = [1.0, 2.0, 3.0, 2.5]
-weights = [[0.2, 0.8, -0.5, 1.0],
- [0.5, -0.91, 0.26, -0.5],
- [-0.26, -0.27, 0.17, 0.87]]
-biases = [2, 3, 0.5]
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-outputs = []
-for i in range(num_neurons):
- output = 0
- for j in range(num_inputs):
- output += inputs[j]*weights[i][j]
- output += biases[i]
- outputs.append(output)
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-print(outputs)
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-[4.8, 1.21, 2.385] --
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-Layer of 3 neurons with 4 inputs¶
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-In [2]:
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-num_neurons = 3
-num_inputs = 4
-inputs = [1.0, 2.0, 3.0, 2.5]
-weights = [[0.2, 0.8, -0.5, 1.0],
- [0.5, -0.91, 0.26, -0.5],
- [-0.26, -0.27, 0.17, 0.87]]
-biases = [2, 3, 0.5]
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-outputs = []
-for neuron_weights, neuron_bias in zip(weights, biases):
- neuron_output = 0
- for input, weight in zip(inputs, neuron_weights):
- neuron_output += input*weight
- neuron_output += neuron_bias
- outputs.append(neuron_output)
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-print(outputs)
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-[4.8, 1.21, 2.385] --
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-Single Neuron using Numpy¶
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-In [6]:
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-import numpy as np
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-inputs = [1.0, 2.0, 3.0, 2.5]
-weights = [0.2, 0.8, -0.5, 1.0]
-bias = 2
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-output = np.dot(inputs, weights) + bias
-print(output)
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-4.8 --