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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:UsersdpoppDocumentsMachine Learningfirst_neural_net.py", line 66, in <module>

neural_network.train(training_set_inputs, training_set_outputs, 10000)

File "C:UsersdpoppDocumentsMachine Learningfirst_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 neural 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!

Change self.synaptic_weights += adjustment to

self.synaptic_weights = self.synaptic_weights + adjustment

self.synaptic_weights must have a shape of (3,1) and adjustment must have a shape of (3,4). While the shapes are broadcastable numpy must not like trying to assign the result with shape (3,4) to an array of shape (3,1)

a = np.ones((3,1))

b = np.random.randint(1,10, (3,4))



>>> a

array([[1],

       [1],

       [1]])

>>> b

array([[8, 2, 5, 7],

       [2, 5, 4, 8],

       [7, 7, 6, 6]])



>>> a + b

array([[9, 3, 6, 8],

       [3, 6, 5, 9],

       [8, 8, 7, 7]])



>>> b += a

>>> b

array([[9, 3, 6, 8],

       [3, 6, 5, 9],

       [8, 8, 7, 7]])

>>> a

array([[1],

       [1],

       [1]])



>>> a += b

Traceback (most recent call last):

  File "<pyshell#24>", line 1, in <module>

    a += b

ValueError: non-broadcastable output operand with shape (3,1) doesn't match the broadcast shape (3,4)

The same error occurs when using numpy.add and specifying a as the output array

>>> np.add(a,b, out = a)

Traceback (most recent call last):

  File "<pyshell#31>", line 1, in <module>

    np.add(a,b, out = a)

ValueError: non-broadcastable output operand with shape (3,1) doesn't match the broadcast shape (3,4)

>>> 

A new a needs to be created

>>> a = a + b

>>> a

array([[10,  4,  7,  9],

       [ 4,  7,  6, 10],

       [ 9,  9,  8,  8]])

>>> 

Hopefully, by now you must have executed the code, but the problem between his code and your code is this line:

training_output = np.array([[0,1,1,0]]).T  

While transposing don't forget to add 2 square brackets, I had the same problem for the same code, this worked for me.
Thanks