TF之NN：利用神經網絡系統自動學習散點(二次函數+noise+優化修正)輸出結果可視化(matplotlib動態演示)

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目錄

輸出結果

代碼設計

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輸出結果

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代碼設計

import tensorflow as tf import numpy as np import 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_belse: outputs = activation_function(Wx_plus_b)return outputsx_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])l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu) prediction = add_layer(l1, 10, 1, activation_function=None)# the error between prediciton 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 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() plt.show()for i in range(1000): # trainingsess.run(train_step, feed_dict={xs: x_data, ys: y_data}) if i % 50 == 0: # to visualize the result and improvementtry:ax.lines.remove(lines[0])except Exception:passprediction_value = sess.run(prediction, feed_dict={xs: x_data})# plot the predictionlines = ax.plot(x_data, prediction_value, 'r-', lw=5)plt.title('Matplotlib,NN,Efficient learning,Approach,Quadratic function --Jason Niu')plt.pause(0.1)

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TF之NN：matplotlib動態演示深度學習之tensorflow將神經網絡系統自動學習散點(二次函數+noise)并優化修正并且將輸出結果可視化

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