p4.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]

# # output=np.dot(inputs,weights)+biases    ''This would throw a shape error, and so will np.dot(weights,inputs)''
# layer1_outputs=np.dot(inputs,np.array(weights).T)+biases
# # print(output)

# layer2_outputs=np.dot(layer1_outputs,np.array(weights2).T)+biases2

# print(layer2_outputs)

np.random.seed(0)

X=[[1,2,3,2.5],
    [2.0,5.0,-1.0,2.0],
    [-1.5,2.7,3.3,-0.8]]


class Layer_Dense:
    def __init__(self,n_inputs,n_neurons):
        self.weights=0.10*np.random.randn(n_inputs,n_neurons)
        self.biases=np.zeros((1,n_neurons))
    def forward(self,inputs):
        self.output=np.dot(inputs,self.weights)+self.biases

layer1=Layer_Dense(4,5)
layer2=Layer_Dense(5,2)

layer1.forward(X)
# print(layer1.output)
layer2.forward(layer1.output)
print(layer2.output)