30 lines
818 B
Python
30 lines
818 B
Python
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import numpy as np
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#inputs * weights + biases = predictions
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inputs = [[1.0, 2.0, 3.0, 2.5],
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[2.0, 5.0, -1.0, 2.0],
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[-1.5, 2.7, 3.3, -0.8]]
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weights = [[0.2, 0.8, -0.5, 1.0],
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[0.5, -0.91, 0.26, -0.5],
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[-0.26, -0.27, 0.17, 0.87]]
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biases = [2.0, 3.0, 0.5]
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#adding more sets of weights and biases creates more layers for our neural network
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weights2 = [[0.1, -0.14, 0.5],
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[-0.5, 0.12, -0.33],
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[-0.44, 0.73, -0.13]]
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biases2 = [-1, 2, -0.5]
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#take dot product of the two matrices and add biases (transpose so that we can do matrix multiplication)
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layer_outputs = np.dot(inputs, np.array(weights).T) + biases
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#repeat dot product but instead with the output matrix from layer1 and the second layer of weights and biases
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layer_outputs2 = np.dot(layer_outputs, np.array(weights2).T) + biases2
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print(layer_outputs2)
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