operands could not be broadcast together with remapped shape

I keep getting this error message: ValueError: operands could not be broadcast together with remapped shapes [original->remapped]: (1000,) and requested shape (1000,1)

I do understand the concept behind it but I do not see where, and how to fix the code, which part of my code is causing that error?

# We must always import the relevant libraries for our problem at hand. NumPy is a must for this example.
import numpy as np

# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# First, we should declare a variable containing the size of the training set we want to generate.
observations = 1000

# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.
# We have picked x and z, since it is easier to differentiate them.
# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).
# The size of xs and zs is observations x 1. In this case: 1000 x 1.
xs = np.random.uniform(low=-10, high=10, size=(observations,1))
zs = np.random.uniform(-10, 10, (observations,1))

# Combine the two dimensions.сх of the input into one input matrix. 
# This is the X matrix from the linear model y = x*w + b.
# column_stack is a Numpy method, which combines two matrices (vectors) into one.
inputs = np.column_stack((xs,zs))

# Check if the dimensions of the inputs are the same as the ones we defined in the linear model lectures. 
# They should be n x k, where n is the number of observations, and k is the number of variables, so 1000 x 2.
print (inputs.shape)

# We add a random small noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>
noise = np.random.uniform(-1, 1, (observations,1))

# Produce the targets according to our f(x,z) = 2x - 3z + 5 + noise definition.
# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.
targets = 2*xs - 3*zs + 5 + noise

# Check the shape of the targets just in case.
print (targets.shape)

# In order to use the 3D plot, the objects should have a certain shape, so we reshape the targets.
# The proper method to use is reshape and takes as arguments the dimensions in which we want to fit the object.
targets = targets.reshape(observations,)

# Plotting according to the conventional matplotlib.pyplot syntax

# Declare the figure
fig = plt.figure()

# A method allowing us to create the 3D plot
ax = fig.add_subplot(111, projection='3d')

# Choose the axes.
ax.plot(xs, zs, targets)

# Set labels
ax.set_xlabel('xs')
ax.set_ylabel('zs')
ax.set_zlabel('Targets')

# You can fiddle with the azim parameter to plot the data from different angles. Just change the value of azim=100
# to azim = 0 ; azim = 200, or whatever. Check and see what happens.
ax.view_init(azim=100)

# So far we were just describing the plot. This method actually shows the plot. 
plt.show()

# We reshape the targets back to the shape that they were in before plotting.
# This reshaping is a side-effect of the 3D plot. Sorry for that.
targets = targets.reshape(observations,1)