[MXNet] Unpin matplotlib==3.4 Dep & Fix #2046

chapter_appendix-mathematics-for-deep-learning/integral-calculus.md

@@ -352,7 +352,7 @@ z = np.exp(- x**2 - y**2)
 
 # Plot function
 ax = d2l.plt.figure().add_subplot(111, projection='3d')
-ax.plot_wireframe(x, y, z)
+ax.plot_wireframe(x.asnumpy(), y.asnumpy(), z.asnumpy())
 d2l.plt.xlabel('x')
 d2l.plt.ylabel('y')
 d2l.plt.xticks([-2, -1, 0, 1, 2])

chapter_appendix-mathematics-for-deep-learning/multivariable-calculus.md

@@ -553,8 +553,10 @@ w = np.exp(-1)*(-1 - (x + 1) + (x + 1)**2 + y**2)
 
 # Plot function
 ax = d2l.plt.figure().add_subplot(111, projection='3d')
-ax.plot_wireframe(x, y, z, **{'rstride': 10, 'cstride': 10})
-ax.plot_wireframe(x, y, w, **{'rstride': 10, 'cstride': 10}, color='purple')
+ax.plot_wireframe(x.asnumpy(), y.asnumpy(), z.asnumpy(),
+                  **{'rstride': 10, 'cstride': 10})
+ax.plot_wireframe(x.asnumpy(), y.asnumpy(), w.asnumpy(),
+                  **{'rstride': 10, 'cstride': 10}, color='purple')
 d2l.plt.xlabel('x')
 d2l.plt.ylabel('y')
 d2l.set_figsize()

chapter_linear-networks/linear-regression.md

@@ -391,7 +391,7 @@ rather than writing costly for-loops in Python.**)
 %matplotlib inline
 from d2l import mxnet as d2l
 import math
-import numpy as np
+from mxnet import np
 import time
 ```
 
@@ -507,7 +507,19 @@ def normal(x, mu, sigma):
 We can now (**visualize the normal distributions**).
 
 ```{.python .input  n=8}
-%%tab all
+%%tab mxnet
+# Use numpy again for visualization
+x = np.arange(-7, 7, 0.01)
+
+# Mean and standard deviation pairs
+params = [(0, 1), (0, 2), (3, 1)]
+d2l.plot(x.asnumpy(), [normal(x, mu, sigma).asnumpy() for mu, sigma in params], xlabel='x',
+         ylabel='p(x)', figsize=(4.5, 2.5),
+         legend=[f'mean {mu}, std {sigma}' for mu, sigma in params])
+```
+
+```{.python .input  n=8}
+%%tab pytorch, tensorflow
 # Use numpy again for visualization
 x = np.arange(-7, 7, 0.01)
 

chapter_optimization/optimization-intro.md

@@ -150,7 +150,25 @@ annotate('saddle point', (0, -0.2), (-0.52, -5.0))
 Saddle points in higher dimensions are even more insidious, as the example below shows. Consider the function $f(x, y) = x^2 - y^2$. It has its saddle point at $(0, 0)$. This is a maximum with respect to $y$ and a minimum with respect to $x$. Moreover, it *looks* like a saddle, which is where this mathematical property got its name.
 
 ```{.python .input}
-#@tab all
+#@tab mxnet
+x, y = d2l.meshgrid(
+    d2l.linspace(-1.0, 1.0, 101), d2l.linspace(-1.0, 1.0, 101))
+z = x**2 - y**2
+
+ax = d2l.plt.figure().add_subplot(111, projection='3d')
+ax.plot_wireframe(x.asnumpy(), y.asnumpy(), z.asnumpy(),
+                  **{'rstride': 10, 'cstride': 10})
+ax.plot([0], [0], [0], 'rx')
+ticks = [-1, 0, 1]
+d2l.plt.xticks(ticks)
+d2l.plt.yticks(ticks)
+ax.set_zticks(ticks)
+d2l.plt.xlabel('x')
+d2l.plt.ylabel('y');
+```
+
+```{.python .input}
+#@tab pytorch, tensorflow
 x, y = d2l.meshgrid(
     d2l.linspace(-1.0, 1.0, 101), d2l.linspace(-1.0, 1.0, 101))
 z = x**2 - y**2

setup.py

@@ -4,7 +4,7 @@
 requirements = [
     'jupyter',
     'numpy',
-    'matplotlib==3.4',
+    'matplotlib',
     'requests',
     'pandas',
     'gym'