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forward propagation python

Innocent Iguana answered on April 27, 2023 Popularity 2/10 Helpfulness 8/10

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forward propagation python

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import numpy as np

def relu(input):
    '''Define your relu activation function here'''
    # Calculate the value for the output of the relu function: output
    output = max(input, 0)
    
    # Return the value just calculated
    return(output)

def tanh(input):
    '''Define your hyperbolic tangent activation function here'''
    # Calculate the value for the output of the tanh function: output
    output = np.tanh(input)
    
    # Return the value just calculated
    return(output)

input_data = np.array([3, 5])
weights = {'node_0_0': np.array([2, 4]),
 'node_0_1': np.array([ 4, -5]),
 'node_1_0': np.array([-1,  2]),
 'node_1_1': np.array([1, 2]),
 'output': np.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)

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Contributed on Apr 15 2024

Innocent Iguana

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import numpy as np

# Define the activation function (sigmoid in this case)
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# Define the neural network architecture
input_layer_size = 2
hidden_layer_size = 4
output_layer_size = 1

# Initialize the weights for each layer
w1 = np.random.uniform(size=(input_layer_size, hidden_layer_size))
w2 = np.random.uniform(size=(hidden_layer_size, output_layer_size))

# Define the input
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])

# Forward propagation
layer1_output = sigmoid(np.dot(X, w1))
output = sigmoid(np.dot(layer1_output, w2))

# Print the output
print(output)

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Tags: python forward propagation

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Contributed on Apr 27 2023

Innocent Iguana

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forward propagation python

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