MXNet Sample

MXNet Sample

  1. import pickle
  2. import numpy as np
  3.  
  4. def cos_curve(x):
  5.     return 0.25 * np.sin(2 * x * np.pi + 0.5 * np.pi) + 0.5
  6.  
  7. np.random.seed(123)
  8. samples = []
  9. labels = []
  10.  
  11. sample_density = 50
  12. for i in range(sample_density):
  13.     x1, x2 = np.random.random(2)
  14.  
  15.     bound = cos_curve(x1)
  16.  
  17.     if bound - 0.1 < x2 <= bound + 0.1:
  18.         continue
  19.     else:
  20.         samples.append((x1, x2))
  21.  
  22.         if x2 > bound:
  23.             labels.append(1)
  24.         else:
  25.             labels.append(0)
  26.  
  27. with open('data.pkl', 'wb') as f:
  28.     pickle.dump((samples, labels), f)
  29.  
  30. import matplotlib.pyplot as plt
  31.  
  32. for i, sample in enumerate(samples):
  33.     plt.plot(sample[0], sample[1], 'o' if labels[i] else '^',
  34.              mec='r' if labels[i] else 'b',
  35.              mfc='none',
  36.              markersize=10)
  37. x1 = np.linspace(0, 1)
  38. plt.plot(x1, cos_curve(x1), 'k--')
  39. plt.show()
  40.  
  41. #
  42.  
  43. import numpy as np
  44. import mxnet as mx
  45.  
  46. data = mx.sym.Variable('data')
  47.  
  48. fc1 = mx.sym.FullyConnected(data=data, name='fc1', num_hidden=2)
  49.  
  50. sigmoid1 = mx.sym.Activation(data=fc1, name='sigmoid1', act_type='sigmoid')
  51.  
  52. fc2 = mx.sym.FullyConnected(data=sigmoid1, name='fc2', num_hidden=2)
  53.  
  54. mlp = mx.sym.SoftmaxOutput(data=fc2, name='softmax')
  55.  
  56. shape = {'data': (2,)}
  57. mlp_dot = mx.viz.plot_network(symbol=mlp, shape=shape)
  58. mlp_dot.render('simple_mlp.gv', view=True)
  59.  
  60. #
  61.  
  62. import pickle
  63. import logging
  64.  
  65. with open('data.pkl', 'rb') as f:
  66.     samples, labels = pickle.load(f)
  67.  
  68. logging.getLogger().setLevel(logging.DEBUG)
  69.  
  70. batch_size = len(labels)
  71. samples = np.array(samples)
  72. labels = np.array(labels)
  73.  
  74. train_iter = mx.io.NDArrayIter(samples, labels, batch_size)
  75.  
  76. model = mx.model.FeedForward.create(
  77.     symbol=mlp,
  78.     X=train_iter,
  79.     num_epoch=1000,
  80.     learning_rate=0.1,
  81.     momentum=0.99
  82. )
  83. '''
  84. model = mx.model.FeedForward(
  85.     symbol=mlp,
  86.     num_epoch=1000,
  87.     learning_rate=0.1
  88.     momentum=0.99
  89. )
  90. model.fit(X=train_iter)
  91. '''
  92. print(model.predict(mx.nd.array([[0.5, 0.5]])))
  93.  
  94. #
  95.  
  96. import matplotlib.pyplot as plt
  97. from mpl_toolkits.mplot3d import Axes3D
  98.  
  99. X = np.arange(0, 1.05, 0.05)
  100. Y = np.arange(0, 1.05, 0.05)
  101. X, Y = np.meshgrid(X, Y)
  102.  
  103. grids = mx.nd.array([[X[i][j], Y[i][j]] for i in range(X.shape[0]) for j in range(X.shape[1])])
  104.  
  105. grid_probs = model.predict(grids)[:, 1].reshape(X.shape)
  106.  
  107. fig = plt.figure('Sample Surface')
  108. ax = fig.gca(projection='3d')
  109.  
  110. ax.plot_surface(X, Y, grid_probs, alpha=0.15, color='k', rstride=2, cstride=2, lw=0.5)
  111.  
  112. samples0 = samples[labels==0]
  113. samples0_probs = model.predict(samples0)[:, 1]
  114. samples1 = samples[labels==1]
  115. samples1_probs = model.predict(samples1)[:, 1]
  116.  
  117. ax.scatter(samples0[:, 0], samples0[:, 1], samples0_probs, c='r', marker='o', s=50)
  118. ax.scatter(samples1[:, 0], samples1[:, 1], samples1_probs, c='b', marker='^', s=50)
  119.  
  120. plt.show()


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