# tensorflow（6）结果可视化

2017-02-22

tensorflow（6）结果可视化：tensorflow怎么让结果可视化呢？希望下面的文章对大家有所帮助。

tensorflow（6）结果可视化：tensorflow怎么让结果可视化呢？希望下面的文章对大家有所帮助。
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

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

##plt.scatter(x_data, y_data)
##plt.show()

# 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]))
# important step
init = tf.initialize_all_variables()
sess= tf.Session()
sess.run(init)

# plot the real data
fig = plt.figure()
ax.scatter(x_data, y_data)
plt.ion()
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])
except Exception:
pass
prediction_value = sess.run(prediction, feed_dict={xs: x_data})
# plot the prediction
lines = ax.plot(x_data, prediction_value, &#39;r-&#39;, lw=5)
plt.pause(1)