Deep Learning in Python-Chapter 1

# =============================================================================
# Introduction to deep learning
# =============================================================================
'''
input_data: array([3, 5])
weights: {'node_0': array([2, 4]), 'node_1': array([ 4, -5]), 'output': array([2, 7])}
'''

# Calculate node 0 value: node_0_value
node_0_value = (input_data * weights['node_0']).sum()

# Calculate node 1 value: node_1_value
node_1_value = (input_data * weights['node_1']).sum()

# Put node values into array: hidden_layer_outputs
hidden_layer_outputs = np.array([node_0_value, node_1_value])

# Calculate output: output
output = (hidden_layer_outputs * weights['output']).sum()

# Print output
print(output)

''' The Rectified Linear Activation Function '''

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)

''' Applying the network to many observations/rows of data '''

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

    # Calculate node 0 value
    node_0_input = (input_data_row * weights['node_0']).sum()
    node_0_output = relu(node_0_input)

    # Calculate node 1 value
    node_1_input = (input_data_row * weights['node_1']).sum()
    node_1_output = relu(node_1_input)

    # 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 = (hidden_layer_outputs * weights['output']).sum()
    model_output = relu(input_to_final_layer)
    
    # 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(predict_with_network(input_data_row, weights))

# Print results
print(results)  # [52, 63, 0, 148]
        
''' Multi-layer neural networks '''

# 在上述的 def 中，使用 "node_0_0" 標記多層節點