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

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

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									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