Applying the network to many observations/rows of data

You'll now define a function called predict_with_network() which will generate predictions for multiple data observations, which are pre-loaded as input_data. As before, weights are also pre-loaded. In addition, the relu() function you defined in the previous exercise has been pre-loaded.

This exercise is part of the course

Introduction to Deep Learning in Python

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Exercise instructions

Define a function called predict_with_network() that accepts two arguments - input_data_row and weights - and returns a prediction from the network as the output.
Calculate the input and output values for each node, storing them as: node_0_input, node_0_output, node_1_input, and node_1_output.
- To calculate the input value of a node, multiply the relevant arrays together and compute their sum.
- To calculate the output value of a node, apply the relu() function to the input value of the node.
Calculate the model output by calculating input_to_final_layer and model_output in the same way you calculated the input and output values for the nodes.
Use a for loop to iterate over input_data:
- Use your predict_with_network() to generate predictions for each row of the input_data - input_data_row. Append each prediction to results.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Define predict_with_network()
def predict_with_network(input_data_row, weights):

    # Calculate node 0 value
    node_0_input = ____
    node_0_output = ____

    # Calculate node 1 value
    node_1_input = ____
    node_1_output = ____

    # Put node values into array: hidden_layer_outputs
    hidden_layer_outputs = np.array([node_0_output, node_1_output])
    
    # Calculate model output
    input_to_final_layer = ____
    model_output = ____
    
    # Return model output
    return(model_output)

# Create empty list to store prediction results
results = []
for input_data_row in input_data:
    # Append prediction to results
    results.append(____)

# Print results
print(results)

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