Plot

plot1.py

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()

plot2.py

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()