intro_deep_learning1.py

import numpy as np
from numpy import array

input_data=array([3,5])

weights={'node_0_0': array([2, 4]),
 'node_0_1': array([ 4, -5]),
 'node_1_0': array([-1,  2]),
 'node_1_1': array([1, 2]),
 'output': array([2, 7])}


def predict_with_network(input_data):
    # Calculate node 0 in the first hidden layer
    node_0_0_input = (input_data * weights["node_0_0"]).sum()
    node_0_0_output = relu(node_0_0_input)

    # Calculate node 1 in the first hidden layer
    node_0_1_input = (input_data * weights["node_0_1"]).sum()
    node_0_1_output = relu(node_0_1_input)

    # Put node values into array: hidden_0_outputs
    hidden_0_outputs = np.array([node_0_0_output, node_0_1_output])
    
    # Calculate node 0 in the second hidden layer
    node_1_0_input = (hidden_0_outputs*weights["node_1_0"]).sum()
    node_1_0_output = relu(node_1_0_input)

    # Calculate node 1 in the second hidden layer
    node_1_1_input = (hidden_0_outputs*weights["node_1_1"]).sum()
    node_1_1_output = relu(node_1_1_input)

    # Put node values into array: hidden_1_outputs
    hidden_1_outputs = np.array([node_1_0_output, node_1_1_output])

    # Calculate model output: model_output
    model_output = (hidden_1_outputs*weights["output"]).sum()
    
    # Return model_output
    return(model_output)

output = predict_with_network(input_data)
print(output)