TensorFlow-matplotlib結果視覺化
阿新 • • 發佈:2019-01-11
TensorFlow-matplotlib結果視覺化
硬體:NVIDIA-GTX1080
軟體:Windows7、python3.6.5、tensorflow-gpu-1.4.0
一、基礎知識
matplotlib為matlab在python中的介面
二、程式碼展示
import tensorflow as tf import numpy as np import matplotlib.pyplot as plt def add_layer(inputs, in_size, out_size, activate_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 activate_function is None: outputs = Wx_plus_b else: outputs = activate_function(Wx_plus_b) return outputs 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 xs = tf.placeholder(tf.float32, [None, 1]) ys = tf.placeholder(tf.float32, [None, 1]) hide_layer = add_layer(xs, 1, 10, tf.nn.relu) prediction = add_layer(hide_layer, 10, 1, None) loss = tf.reduce_mean(tf.reduce_sum(tf.square(prediction - ys), reduction_indices = [1])) optimizer = tf.train.GradientDescentOptimizer(0.1) train_step = optimizer.minimize(loss) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) #draw input output data fig = plt.figure() ax = fig.add_subplot(1,1,1) ax.scatter(x_data, y_data) plt.ion() plt.show() for step in range(3000): sess.run(train_step, feed_dict = {xs: x_data, ys:y_data}) if step%50 == 0: #print(sess.run(loss, feed_dict = {xs: x_data, ys:y_data})) #draw input prediction loss try: ax.lines.remove(lines[0]) except Exception: pass prediction_value = sess.run(prediction, feed_dict = {xs: x_data, ys:y_data}) lines = ax.plot(x_data, prediction_value, 'r-', lw = 5) plt.pause(1)
三、結果展示
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