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