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  • Jason Aylward thinks this is interesting:

Figure 3-7 plots the data generated by this code with Matplotlib to verify that the distribution is as expected. We see that the data resides in two classes that are neatly separated.

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Cover of TensorFlow for Deep Learning

Note

I found this example really confusing.
Only two lines of the code in Example 3-3 are represented in Figure 3-7.

    x_zeros = np.random.multivariate_normal( mean=np.array((-1, -1)), cov=.1*np.eye(2), size=(N//2,))
    x_ones = np.random.multivariate_normal( mean=np.array((1, 1)), cov=.1*np.eye(2), size=(N//2,))

You can plot this with plt.scatter(x_zeros[:, 0], x_zeros[:, 1], color="blue") plt.scatter(x_ones[:, 0], x_ones[:, 1], color="red") to generate the graph.

x_zeros, y_zeros, x_np, y_np and all that vstack(), concatenate() usage completely unnecessary for generating a toy dataset until its time to feed it into the TensorFlow graph.