explicit_example.py

import numpy as np

inputs = [[1, 2, 3, 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, 3, 0.5]

weights2 = [[0.1, -0.14, 0.5],
            [-0.5, 0.12, -0.33],
            [-0.44, 0.73, -0.13]]

biases2 = [-1, 2, -0.5]

layer1_outputs = np.dot(inputs, np.array(weights).T) + biases
layer2_outputs = np.dot(layer1_outputs, np.array(weights2).T) + biases2

print(layer2_outputs)

# layer_outputs = [] # Output of current layer
# for neuron_weights, neuron_bias in zip(weights, biases):
#     neuron_output = 0 # Output of given neuron
#     for n_input, weight in zip(inputs, neuron_weights):
#         neuron_output += n_input*weight
#     neuron_output += neuron_bias
#     layer_outputs.append(neuron_output)

# print(layer_outputs)