start.py

import numpy as np

# changed the variable name to X as standard for the input layer in ML
X = [[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]

# Add a second layer of neuron weights
weights2 = [[0.1, -0.14, 0.5],
            [-0.5, 0.12, -0.33],
            [-0.44, 0.73, -0.13]]

biases2 = [-1, 2, -0.5]

# Because we are now using a batch of inputs we need to transpose the weights
# so that we dont get a shape error. Note we swapped the dot order too.

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

print(layer2_outputs    )

# UPTO pt.4 24.05