0
$\begingroup$

I recently started to follow along with Siraj Raval's Deep Learning tutorials on YouTube, but I an error came up when I tried to run my code. The code is from the second episode of his series, How To Make A Neural Network. When I ran the code I got the error:

Traceback (most recent call last):
File "C:\Users\dpopp\Documents\Machine Learning\first_neural_net.py", line 66, in <module>
neural_network.train(training_set_inputs, training_set_outputs, 10000)
File "C:\Users\dpopp\Documents\Machine Learning\first_neural_net.py", line 44, in train
self.synaptic_weights += adjustment
ValueError: non-broadcastable output operand with shape (3,1) doesn't match the broadcast shape (3,4)

I checked multiple times with his code and couldn't find any differences, and even tried copying and pasting his code from the GitHub link. This is the code I have now:

from numpy import exp, array, random, dot

class NeuralNetwork():
    def __init__(self):
        # Seed the random number generator, so it generates the same numbers
        # every time the program runs.
        random.seed(1)

        # We model a single neuron, with 3 input connections and 1 output connection.
        # We assign random weights to a 3 x 1 matrix, with values in the range -1 to 1
        # and mean 0.
        self.synaptic_weights = 2 * random.random((3, 1)) - 1

    # The Sigmoid function, which describes an S shaped curve.
    # We pass the weighted sum of the inputs through this function to
    # normalise them between 0 and 1.
    def __sigmoid(self, x):
        return 1 / (1 + exp(-x))

    # The derivative of the Sigmoid function.
    # This is the gradient of the Sigmoid curve.
    # It indicates how confident we are about the existing weight.
    def __sigmoid_derivative(self, x):
        return x * (1 - x)

    # We train the neural network through a process of trial and error.
    # Adjusting the synaptic weights each time.
    def train(self, training_set_inputs, training_set_outputs, number_of_training_iterations):
        for iteration in range(number_of_training_iterations):
            # Pass the training set through our neural network (a single neuron).
            output = self.think(training_set_inputs)

            # Calculate the error (The difference between the desired output
            # and the predicted output).
            error = training_set_outputs - output

            # Multiply the error by the input and again by the gradient of the Sigmoid curve.
            # This means less confident weights are adjusted more.
            # This means inputs, which are zero, do not cause changes to the weights.
            adjustment = dot(training_set_inputs.T, error * self.__sigmoid_derivative(output))

            # Adjust the weights.
            self.synaptic_weights += adjustment

    # The neural network thinks.
    def think(self, inputs):
        # Pass inputs through our neural network (our single neuron).
        return self.__sigmoid(dot(inputs, self.synaptic_weights))

if __name__ == '__main__':

    # Initialize a single neuron neural network
    neural_network = NeuralNetwork()

    print("Random starting synaptic weights:")
    print(neural_network.synaptic_weights)

    # The training set. We have 4 examples, each consisting of 3 input values
    # and 1 output value.
    training_set_inputs = array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]])
    training_set_outputs = array([[0, 1, 1, 0]])

    # Train the neural network using a training set
    # Do it 10,000 times and make small adjustments each time
    neural_network.train(training_set_inputs, training_set_outputs, 10000)

    print("New Synaptic weights after training:")
    print(neural_network.synaptic_weights)

    # Test the neura net with a new situation
    print("Considering new situation [1, 0, 0] -> ?:")
    print(neural_network.think(array([[1, 0, 0]])))

Even after copying and pasting the same code that worked in Siraj's episode, I'm still getting the same error.

I just started out look into artificial intelligence, and don't understand what the error means. Could someone please explain what it means and how to fix it? Thanks!

$\endgroup$
0
$\begingroup$

You forgot to transpose the output values in line 61. The line should read as follows:

training_set_outputs = array([[0, 1, 1, 0]]).T

Otherwise the format of the matrices that you want to add together in line 43 does not match.

$\endgroup$
  • $\begingroup$ Excellent observation @demento $\endgroup$ – Seth Simba Jan 9 '18 at 7:33

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.