通过python的matplotlib包将Tensorflow数据进行可视化的方法


						使用matplotlib中的一些函数将tensorflow中的数据可视化，更加便于分析





import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt



def add_layer(inputs, in_size, out_size, activation_function=None):

  Weights = tf.Variable(tf.random_normal([in_size, out_size]))

  biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)

  Wx_plus_b = tf.matmul(inputs, Weights) + biases

  if activation_function is None:

    outputs = Wx_plus_b

  else:

    outputs = activation_function(Wx_plus_b)

  return outputs



# Make up some real data

x_data = np.linspace(-1, 1, 300)[:, np.newaxis]

noise = np.random.normal(0, 0.05, x_data.shape)

y_data = np.square(x_data) - 0.5 + noise





# define placeholder for inputs to network

xs = tf.placeholder(tf.float32, [None, 1])

ys = tf.placeholder(tf.float32, [None, 1])

# add hidden layer

l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)

# add output layer

prediction = add_layer(l1, 10, 1, activation_function=None)



# the error between prediction and real data

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction), reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

# important step



#initialize_all_variables已被弃用,使用tf.global_variables_initializer代替。 

init = tf.global_variables_initializer()

sess = tf.Session()

sess.run(init)



# plot the real data

fig = plt.figure()

ax = fig.add_subplot(1,1,1)

ax.scatter(x_data, y_data)

plt.ion() #使plt不会在show之后停止而是继续运行

plt.show()





for i in range(1000):

  # training

  sess.run(train_step, feed_dict={xs: x_data, ys: y_data})

  if i % 50 == 0:

    # to visualize the result and improvement

    try:

      ax.lines.remove(lines[0]) #在每一次绘图之前先讲上一次绘图删除，使得画面更加清晰

    except Exception:

      pass

    prediction_value = sess.run(prediction, feed_dict={xs: x_data})

    # plot the prediction

    lines = ax.plot(x_data, prediction_value, 'r-', lw=5) #'r-'指绘制一个红色的线

    plt.pause(1) #指等待一秒钟





运行结果如下：（实际效果应该是动态的，应当使用ipython运行，使用jupyter运行则图片不是动态的）



注意：initialize_all_variables已被弃用,使用tf.global_variables_initializer代替。

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