tensorflow增加层和数据可视化

添加层

import tensorflow as tf

def add_layer(inputs,insize,outsize,activation_function=None)
    Weights = tf.Variable(tf.random_normal([insize,outsize]))
    biases = tf.Variable(tf.zeros([1,outsize]) + 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

完整的一个小例子：

import tensorflow as tf
import numpy as np


def add_layer(inputs,insize,outsize,activation_function=None):
    Weights = tf.Variable(tf.random_normal([insize,outsize]))
    biases = tf.Variable(tf.zeros([1,outsize]) + 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

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
y = np.random.random(y_data.shape)
sess = tf.Session()


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)
# 这里对矩阵按行求和没有什么作用，因为数据每行只有一个实例，还是它本身
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
                      reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for i in range(1000):
    sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
    if i % 50 == 0:
        print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))

数据可视化：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt


def add_layer(inputs,insize,outsize,activation_function=None):
    Weights = tf.Variable(tf.random_normal([insize,outsize]))
    biases = tf.Variable(tf.zeros([1,outsize]) + 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

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
y = np.random.random(y_data.shape)

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)
# 这里对矩阵按行求和没有什么作用，因为数据每行只有一个实例，还是它本身
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
                      reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    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):
        sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
        if i % 50 == 0:
            # print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))
            try:
                ax.lines.remove(lines[0])
            except Exception:
                pass
            prediction_value = sess.run(prediction, feed_dict={xs: x_data})
            lines = ax.plot(x_data, prediction_value, 'r-', lw=5)
            # ax.lines.remove(lines[0])
            plt.pause(0.1)

参考：https://morvanzhou.github.io/tutorials/machine-learning/tensorflow/