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@yydai
Last active May 17, 2018 08:02
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Plot
import matplotlib.pyplot as plt
x = np.linspace(-5, 5, 200) # x data, shape=(100, 1)
y1 = 2 * x #...
y2 = x ** 2 + 2
y3 = sin(x)
plt.figure(1, figsize=(8, 6))
plt.subplot(221)
plt.plot(x, y1, c='red', label='y1')
plt.ylim(-1, 5)
plt.legend(loc='best')
plt.subplot(222)
plt.plot(x, y2, c='red', label='y2')
plt.ylim((-0.2, 1.2))
plt.legend(loc='best')
plt.subplot(223)
plt.plot(x, y3, c='red', label='y3')
plt.ylim((-1.2, 1.2))
plt.legend(loc='best')
plt.subplot(224)
plt.plot(x, y4, c='red', label='y4')
plt.ylim((-0.2, 6))
plt.legend(loc='best')
plt.show()
import matplotlib.pyplot as plt
# tensorflow
# 这种方式可以动态的画图,我们可以动态的看到gd的优化过程
plt.ion() # something about plotting
for step in range(100):
# train and net output
_, acc, pred = sess.run([train_op, accuracy, output], {tf_x: x, tf_y: y})
if step % 2 == 0:
# plot and show learning process
plt.cla()
plt.scatter(x[:, 0], x[:, 1], c=pred.argmax(1),
s=100, lw=0, cmap='RdYlGn')
plt.text(1.5, -4, 'Accuracy=%.2f' %
acc, fontdict={'size': 20, 'color': 'red'})
plt.pause(0.1)
plt.ioff()
plt.show()
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