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

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
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))

# 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!

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