[GitHub] bowenroom opened a new issue #12366: MXNetError: [12:17:04] src/imperative/imperative.cc:285: Check failed: !AGInfo::IsNone(*i) Cannot differentiate node because it is not in a computational graph.

[GitBox <gi...@apache.org>]

bowenroom opened a new issue #12366: MXNetError: [12:17:04] src/imperative/imperative.cc:285: Check failed: !AGInfo::IsNone(*i) Cannot differentiate node because it is not in a computational graph.
URL: https://github.com/apache/incubator-mxnet/issues/12366
 
 
    #  have an mxnet error ,can anyone help me to fix it ?thanks a lot!!
   
   from __future__ import print_function
   import matplotlib as mpl
   from matplotlib import pyplot as plt
   import mxnet as mx
   from mxnet import gluon, autograd, nd
   from mxnet.gluon import nn
   import numpy as np
   
   # In[2]:
   
   
   # Hyper Parameters
   BATCH_SIZE = 64
   LR_G = 0.0001  # learning rate for generator
   LR_D = 0.0001  # learning rate for discriminator
   N_IDEAS = 55  # think of this as number of ideas for generating an art work (Generator)
   ART_COMPONENTS = 15  # it could be total point G can draw in the canvas
   PAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)])
   PAINT_POINTS = nd.array(PAINT_POINTS)  # 64*15 为所要绘制的所有的点的数目
   
   # In[3]:
   
   
   # show our beautiful painting range
   plt.plot(PAINT_POINTS[0].asnumpy(), (2 * PAINT_POINTS[0] ** 2 + 1).asnumpy(), c='#74BCFF', lw=3, label='upper bound')
   plt.plot(PAINT_POINTS[0].asnumpy(), (1 * PAINT_POINTS[0] ** 2 + 0).asnumpy(), c='#FF9359', lw=3, label='lower bound')
   plt.legend(loc='upper right')
   plt.show()
   print(PAINT_POINTS.shape)
   
   
   # In[4]:
   def artist_works():  # painting from the famous artist (real target)
       a = np.random.uniform(1, 2, size=BATCH_SIZE)[:, np.newaxis]
       a = nd.array(a)
   
       paintings = a * PAINT_POINTS[0] ** 2 + (a - 1)
       # paintings = torch.from_numpy(paintings).float()
       return paintings
   
   
    
   
    
   G = nn.Sequential()
   with G.name_scope():
       G.add(nn.Dense(128, activation='relu'), nn.Dense(ART_COMPONENTS))
    
   D = nn.Sequential()
   with D.name_scope():
       D.add(nn.Dense(128, activation='relu'), nn.Dense(1, activation='sigmoid'))
   
   ctx = mx.cpu()
   # initialize the generator and the discriminator
   G.initialize(mx.init.Normal(0.02), ctx=ctx)
   D.initialize(mx.init.Normal(0.02), ctx=ctx)
   loss = gluon.loss.SoftmaxCrossEntropyLoss()
   # trainer for the generator and the discriminator
   trainerG = gluon.Trainer(G.collect_params(), 'adam', {'learning_rate': LR_G})
   trainerD = gluon.Trainer(D.collect_params(), 'adam', {'learning_rate': LR_D})
   
   # In[6]:
   
   for step in range(10000):
       artist_paintings = artist_works()  # real painting from artist shape:64*15
       G_ideas = nd.random_normal(shape=(BATCH_SIZE, N_IDEAS))
       G_paintings = G(G_ideas)  # fake painting from G (random ideas)
   
       prob_artist0 = D(artist_paintings)  # D try to increase this prob
       prob_artist1 = D(G_paintings)  # D try to decreases this prob
   
   
       # start the process of training
       with autograd.record():
           D_loss = -nd.mean(nd.log(prob_artist0) + nd.log((1 - prob_artist1)))
           D_loss.backward()
       trainerD.step(N_IDEAS)
   
       with autograd.record():
           G_loss = nd.mean(nd.log(1. - prob_artist1))
           G_loss.backward()
       trainerG.step(N_IDEAS)
   
       if step % 1000 == 0:  # plotting
           plt.cla()
           plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting', )
           plt.plot(PAINT_POINTS[0].asnumpy(), (2 * PAINT_POINTS[0] ** 2 + 1).asnumpy(), c='#74BCFF', lw=3,
                    label='upper bound')
           plt.plot(PAINT_POINTS[0].asnumpy(), (1 * PAINT_POINTS[0] ** 2 + 0).asnumpy(), c='#FF9359', lw=3,
                    label='lower bound')
           plt.legend(loc='upper right')
           plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(),
                    fontdict={'size': 15})
           plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 15})
           plt.ylim((0, 3));
           plt.legend(loc='upper right', fontsize=12);
           plt.draw();
           plt.pause(0.01)
           plt.show()
   

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