products.py

# nnfs.io

import numpy as np

# a single array of inputs, for a layer of 3 neurons

inputs = [1, 2, 3, 2.5]
weights = [[0.2, 0.8, -0.5, 1], [0.5, -0.91, 0.26, -0.5], [-0.26, -0.27, 0.17, 0.87]] # 3 neurons
biases = [2, 3, 0.5] # and their bias

np.matmul(weights, inputs)

# array([ 2.8  , -1.79 ,  1.885])

# what about batching input data?
# enter numpy.matrix.transpose:

inputs = [[1, 2, 3, 2.5], [2, 5, -1, 2], [-1.5, 2.7, 3.3, -0.8]]

# >>> np.array(inputs)
# array([[ 1. ,  2. ,  3. ,  2.5],
#        [ 2. ,  5. , -1. ,  2. ],
#        [-1.5,  2.7,  3.3, -0.8]])

# >>> np.array(inputs).transpose()
# array([[ 1. ,  2. , -1.5],
#        [ 2. ,  5. ,  2.7],
#        [ 3. , -1. ,  3.3],
#        [ 2.5,  2. , -0.8]])

# now inputs are aligned to the number of neurons we can feed them into at a time
# inputs can now be fed into network in chunks

np.matmul(weights, np.array(inputs).transpose()) # transpose() for row and column vectors

# array([[ 2.8  ,  6.9  , -0.59 ],
#        [-1.79 , -4.81 , -1.949],
#        [ 1.885, -0.3  , -0.474]])

# 👏👏👏 encore 👏👏👏

layer_outputs = np.dot(inputs, np.array(weights).T) + biases

# >>> layer_outputs
# array([[ 4.8  ,  1.21 ,  2.385],
#        [ 8.9  , -1.81 ,  0.2  ],
#        [ 1.41 ,  1.051,  0.026]])

# questions? comment below!

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