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 tfimport numpy as npimport matplotlib.pyplot as pltdef 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 datax_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 networkxs = tf.placeholder(tf.float32, [None, 1])ys = tf.placeholder(tf.float32, [None, 1])# add hidden layerl1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)# add output layerprediction = add_layer(l1, 10, 1, activation_function=None)# the error between prediction and real dataloss = 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 datafig = 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。

以上这篇通过python的matplotlib包将Tensorflow数据进行可视化的方法就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。