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my_answers.py
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my_answers.py
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import numpy as np
class NeuralNetwork(object):
def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
# Set number of nodes in input, hidden and output layers.
self.input_nodes = input_nodes
self.hidden_nodes = hidden_nodes
self.output_nodes = output_nodes
# Initialize weights
self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5,
(self.input_nodes, self.hidden_nodes))
self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5,
(self.hidden_nodes, self.output_nodes))
self.lr = learning_rate
#### Set self.activation_function to the implemented sigmoid function ####
self.activation_function = lambda x : (1/ (1+ np.exp(-x)))
def train(self, features, targets):
''' Train the network on batch of features and targets.
Arguments
---------
features: 2D array, each row is one data record, each column is a feature
targets: 1D array of target values
'''
n_records = features.shape[0]
delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)
delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)
for X, y in zip(features, targets):
final_outputs, hidden_outputs = self.forward_pass_train(X) # Implement the forward pass function below
delta_weights_i_h, delta_weights_h_o = self.backpropagation(final_outputs, hidden_outputs, X, y,
delta_weights_i_h, delta_weights_h_o)
self.update_weights(delta_weights_i_h, delta_weights_h_o, n_records)
def forward_pass_train(self, X):
''' Implement forward pass
Arguments
---------
X: features batch
'''
### Forward pass ###
hidden_inputs = np.dot(X, self.weights_input_to_hidden) # signals into hidden layer
hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer
final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output)# signals into final output layer
final_outputs = final_inputs # signals from final output layer
return final_outputs, hidden_outputs
def backpropagation(self, final_outputs, hidden_outputs, X, y, delta_weights_i_h, delta_weights_h_o):
''' Implement backpropagation
Arguments
---------
final_outputs: output from forward pass
y: target (i.e. label) batch
delta_weights_i_h: change in weights from input to hidden layers
delta_weights_h_o: change in weights from hidden to output layers
'''
### Backward pass ###
error = y - final_outputs # Output layer error is the difference between desired target and actual output.
# the hidden layer's contribution to the error
hidden_error = np.dot(self.weights_hidden_to_output, error)
# Backpropagated error terms
output_error_term = error # f'(x) = (x)' = 1
hidden_error_term = hidden_error * hidden_outputs * (1 - hidden_outputs)
# Weight step (input to hidden)
delta_weights_i_h += hidden_error_term * X[:,None]
# Weight step (hidden to output)
delta_weights_h_o += output_error_term * hidden_outputs[:,None]
return delta_weights_i_h, delta_weights_h_o
def update_weights(self, delta_weights_i_h, delta_weights_h_o, n_records):
''' Update weights on gradient descent step
Arguments
---------
delta_weights_i_h: change in weights from input to hidden layers
delta_weights_h_o: change in weights from hidden to output layers
n_records: number of records
'''
self.weights_hidden_to_output += self.lr * delta_weights_h_o / n_records # update hidden-to-output weights with gradient descent step
self.weights_input_to_hidden += self.lr * delta_weights_i_h / n_records # update input-to-hidden weights with gradient descent step
def run(self, features):
''' Run a forward pass through the network with input features
Arguments
---------
features: 1D array of feature values
'''
#### the forward pass here ####
hidden_inputs = np.dot(features, self.weights_input_to_hidden)
hidden_outputs = self.activation_function(hidden_inputs)
# Set the output
final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output)
final_outputs = final_inputs
return final_outputs
#########################################################
# Set hyperparameters
##########################################################
iterations = 4000
learning_rate = 0.5
hidden_nodes = 17
output_nodes = 1