TensorFlow学习心得

初次尝试TensorFlow

import tensorflow as tf
import numpy as np
#create data
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data*0.1+0.3
#随机生成weight，biases为0
Weight = tf.Variable(tf.random_uniform([1],-1.0,1.0))
biases = tf.Variable(tf.zeros([1]))
#设置假设函数，loss函数，优化器，以及init初始化
y = Weight*x_data+biases
loss = tf.reduce_mean(tf.square(y-y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
init = tf.initialize_all_variables()
#激活初始化
sess = tf.Session()
sess.run(init)
#for循环训练
for step in range(201):
    sess.run(train)
    if step%20==0:
        print(step,sess.run(Weight),sess.run(biases))

矩阵相乘以及输出

import tensorflow as tf
import numpy as np

maxtrix1 =  tf.constant([[3,3]])
maxtrix2 = tf.constant([[2],[2]])
#maxtrix multipy np.dot(maxtrix1,maxtrix2)
# 这两行一样的这行是numpy的矩阵相乘，下面是TensorFlow的矩阵相乘
product = tf.matmul(maxtrix2,maxtrix1)
#方法一
sess = tf.Session()
result = sess.run(product)
print(result)
sess.close()
#方法二,不用close
with tf.Session() as sess:
    result2 = sess.run(product)
    print(result2)

定义变量，相加，更新

import tensorflow as tf
import numpy as np

state = tf.Variable(0,name="counter")
#print(state.name)
one = tf.constant(1)

new_value = tf.add(state,one)
update = tf.assign(state,new_value)

#一定要有这个init，如果定义了一个变量
init = tf.initialize_all_variables()

with tf.Session() as sess:
    sess.run(init)
    for _ in range(3):
        sess.run(update)
        print(sess.run(state))

placeholder

意思是运行的时候再给placeholder赋值,在sess.run时确定placeholder的值
它有几个参数，第一个参数是你要保存的数据的数据类型，大多数是tensorflow中的float32数据类型，后面的参数就是要保存数据的结构，比如要保存一个1×2的矩阵，则struct=[1 2]。

xs = tf.placeholder(tf.float32,[None,1])
ys = tf.placeholder(tf.float32,[None,1])
sess.run(train_step,feed_dict={xs:x_data,ys:y_data})

数据初始化

init = tf.initialize_all_variables()

建造神经网络

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

def add_layer(input,in_size,out_size,activation_function=None):
    weight = tf.Variable(tf.random_normal([in_size,out_size]))
    biases = tf.Variable(tf.zeros([1,out_size])+0.1)
    Wx_plus_b = tf.matmul(input,weight)+biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs

#设置输入数据，加noise
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])
#设置hidden layer和output layer
l1 = add_layer(xs,1,10,activation_function=tf.nn.relu)
prediction = add_layer(l1,10,1,activation_function=None)

#设置loss函数，使用gd优化
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
#激活初始化
init = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init)
#绘制散点图，连续变化
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_data,y_data,s=10)
plt.ion()
plt.show()

#开始训练
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})
        #绘制预测曲线
        lines = ax.plot(x_data,prediction_value,"r-",lw=5)
        plt.pause(0.1)

tensorboard神经网络可视化工具

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

def add_layer(input,in_size,out_size,activation_function=None):
    with tf.name_scope('layer'):
        with tf.name_scope('Weight'):
            weight = tf.Variable(tf.random_normal([in_size,out_size]),name='W')
        with tf.name_scope('Biases'):
            biases = tf.Variable(tf.zeros([1,out_size])+0.1,name='B')
        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b = tf.add(tf.matmul(input,weight),biases)
        if activation_function is None:
            output = Wx_plus_b
        else:
            output = activation_function(Wx_plus_b)
        return output


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
with tf.name_scope('inputs'):
    xs = tf.placeholder(tf.float32,[None,1],name='x_input')
    ys = tf.placeholder(tf.float32,[None,1],name='y_input')

h1 = add_layer(xs,1,10,activation_function = tf.nn.relu)
prediction = add_layer(h1,10,1,activation_function=None)
with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)


init = tf.initialize_all_variables()
sess = tf.Session()
writer = tf.summary.FileWriter('logs/',sess.graph)
sess.run(init)

运行程序，会在log文件夹内产生名叫events.out…的文件
人后打开cmd，输入tensorboard --logdir logs，cmd会输出一个网址
然后在浏览器中输入该网址即可。

MNIST手写识别

• 使用sofemax激励函数，和cross entrpy损失函数

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

def add_layer(input,in_size,out_size,activation_function=None):
    Weight = tf.Variable(tf.random_normal([in_size,out_size]))
    biases = tf.Variable(tf.zeros([1,out_size])+0.1)
    Wx_plus_b = tf.matmul(input,Weight)+biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs



def comput_accurary(v_xs,v_ys):
    global prediction
    y_pre = sess.run(prediction,feed_dict={xs:v_xs})
    correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.arg_max(v_ys,1))
    accurary = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    result = sess.run(accurary,feed_dict={xs:v_xs,ys:v_ys})
    return result

xs = tf.placeholder(tf.float32,[None,784])
ys = tf.placeholder(tf.float32,[None,10])
#sofemax
prediction= add_layer(xs,784,10,activation_function=tf.nn.softmax)
#cross_entropy交叉熵
cross_entrpy = -tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1])
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entrpy)


sess = tf.Session()
sess.run(tf.initialize_all_variables())

for i in range(1000):
    batch_xs,batch_ys = mnist.train.next_batch(100)
    sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys})
    if i%50==0:
        print(comput_accurary(mnist.test.images,mnist.test.labels))

• 使用卷积神经网络

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

def compute_accuracy(test_x,test_y):
    global prediction
    test_prediction = sess.run(prediction,feed_dict={xs:test_x,keep_prob:1})
    if_correct = tf.equal(tf.arg_max(test_prediction,1),tf.arg_max(test_y,1))
    accurary = tf.reduce_mean(tf.cast(if_correct,tf.float32))
    result = sess.run(accurary,feed_dict={xs:test_x,ys:test_y,keep_prob:1})
    return result

def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)

def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)

def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')

xs = tf.placeholder(tf.float32,[None,784])
ys = tf.placeholder(tf.float32,[None,10])
keep_prob = tf.placeholder(tf.float32)
x_image = tf.reshape(xs,[-1,28,28,1])

#conv1 layer
W_conv1 = weight_variable([5,5,1,32])  #28*28*32
b_conv1 = bias_variable([32])  #14*14*32
h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

#conv2 layer
W_conv2 = weight_variable([5,5,32,64])  #14*14*64
b_conv2 = bias_variable([64])  #7*7*64
h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

#func1 layer
W_fc1 = weight_variable([7*7*64,1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])
h_fc1= tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)

#func2 layer
W_fc2 = weight_variable([1024,10])
b_fc2 = bias_variable([10])
prediction= tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)
#训练过程
cross_entrpy = -tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1])
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entrpy)

sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)

for i in range(1000):
    batch_xs,batch_ys = mnist.train.next_batch(100)
    sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys,keep_prob:0.5})
    if i%50 ==0:
        print(compute_accuracy(mnist.test.images,mnist.test.labels))

 - 基于tensorflow1.11版本的cnn，tsne可视化降维

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from matplotlib import cm
from sklearn.manifold import TSNE
from sklearn.model_selection import GridSearchCV
from tensorflow.examples.tutorials.mnist import input_data
#导入tensorflow自带的mnist数据集，并使用# one_hot编码
mnist = input_data.read_data_sets(’./mnist’, one_hot=True)

#设置参数
learning_rate = 0.02
batch_size = 50
train_size = 5000
plot_num = 500
#显示数据集大小，格式
print(“train image shape”, mnist.train.images.shape)
print(“train image labels”, mnist.train.labels.shape)
print(“test image shape”, mnist.test.images.shape)
print(“test image labels”, mnist.test.labels.shape)
#展示数据
plt.imshow(mnist.train.images[0].reshape((28, 28)), cmap=“gray”)
plt.title("%i" % np.argmax(mnist.train.labels[0]))
plt.show()

x = tf.placeholder(tf.float32, [None, 28*28]) / 255.
y = tf.placeholder(tf.float32, [None,10])
image = tf.reshape(x, [-1, 28, 28, 1])

##CNN
#卷积层
conv1 = tf.layers.conv2d(inputs=image, filters=16, kernel_size=5, strides=1, padding=“same”, activation=tf.nn.relu) # ->28,28,16
pool1 = tf.layers.max_pooling2d(conv1, pool_size=2, strides=2) # ->14,14,16
conv2 = tf.layers.conv2d(pool1, 32, 5, 1, “same”, activation=tf.nn.relu) # ->14,14,32
pool2 = tf.layers.max_pooling2d(conv2, 2, 2) # ->7,7,32
flat = tf.reshape(pool2, [-1, 7732])
hidden1 = tf.layers.dense(flat, 64)
#drop_out防止过拟合，提高训练速度
hidden1_dropout = tf.layers.dropout(hidden1, 0.5)
output = tf.layers.dense(hidden1_dropout, 10)

loss = tf.losses.softmax_cross_entropy(onehot_labels=y, logits=output)
train_op = tf.train.AdamOptimizer(learning_rate).minimize(loss)
accuracy = tf.metrics.accuracy(labels=tf.argmax(y, 1), predictions=tf.argmax(output, 1))[1]

sess = tf.Session()
init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init)

plt.ion()
for epoch in range(train_size):
batch_x, batch_y = mnist.train.next_batch(batch_size)
, loss = sess.run([train_op, loss], {x: batch_x, y: batch_y, })
if epoch % 50 == 0:
accuracy_test, hidden1_dropout_ = sess.run([accuracy, hidden1_dropout], {x: mnist.test.images, y: mnist.test.labels})
accuracy_train = sess.run(accuracy,{x: batch_x,y:batch_y})
print(“epoch:”, epoch, “| loss:%.4f” % loss_, “| accuracy_train:%.4f” % accuracy_train, “| accuracy_test:%.4f” % accuracy_test,)

#show two dimension
def plot_labels(two_dim, labels):
plt.cla()
X, Y = two_dim[:, 0], two_dim[:, 1]
for x, y, s in zip(X, Y, labels):
c = cm.rainbow(int(255 * s / 9))
plt.text(x, y, s, backgroundcolor=c, fontsize=9)
plt.xlim(X.min(), X.max())
plt.ylim(Y.min(), Y.max())
plt.title(‘Visualize last layer’)
plt.show()
plt.pause(0.01)
tsne = TSNE(perplexity=30, n_components=2, init=‘pca’, n_iter=5000)
two_dim = tsne.fit_transform(hidden1_dropout_[:plot_num, :])
labels = np.argmax(mnist.test.labels, axis=1)[:plot_num]
plot_labels(two_dim, labels)
plt.ioff()

 - 使用RNN，LSTM实现MNIST

import numpy as np
import tensorflow as tf
import torch
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets(‘MNIST_data’,one_hot=True) ##使用one-hot编码，把每张图片具体的数字，转化为[0,0,0,0,1,0,0,0,0,0]的形式，与输出的结果相匹配

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##参数设置
learn_rate = 0.001 ##学习率
training_iters = 100000 ##总训练次数
batch_size = 128 ##分批次训练，每批次数

n_inputs = 28 ##每一个神经网络输入图片的一行，即28个像素
n_steps = 28 ##设置为28个神经网络连起来，每一个输入一行，有28行
n_hidden_units = 128 ##每一个神经网络有128个隐藏层
n_classes = 10 ##结果分为10类

x = tf.placeholder(tf.float32, [None, n_steps, n_inputs]) ##设置占位符，存放参数，方便换参数
y = tf.placeholder(tf.float32, [None, n_classes])

weights = {
“in”: tf.Variable(tf.random_normal([n_inputs,n_hidden_units])),
“out”: tf.Variable(tf.random_normal([n_hidden_units,n_classes]))
}
biases = {
“in”: tf.Variable(tf.constant(0.1, shape=[n_hidden_units,])),
“out”: tf.Variable(tf.constant(0.1, shape=[n_classes,]))
}

def RNN(X, weights, biases):

##设置单个神经网络结构
X = tf.reshape(X, [-1, n_inputs])   ##转化三维为二维[128*28，28]
X_in = tf.matmul(X, weights['in'])+biases['in']     ##[128*28,128]
X_in = tf.reshape(X_in, [-1, n_steps, n_hidden_units])  ##二维转换成三维[128，28，128]


##设置单个神经网络里面的cell
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units,forget_bias=1, state_is_tuple=True)     ##设置cell
##lstm state被分为c_state和m_state
_init_state = lstm_cell.zero_state(batch_size, dtype=tf.float32)        ##设置初始state
output,states = tf.nn.dynamic_rnn(lstm_cell, X_in, initial_state=_init_state, time_major=False)     ##使用dynamic_rnn即使用最后一个step填充后面不够的step


result = tf.matmul(states[1],weights["out"])+biases["out"]      ##第一种结果计算方法，使用states[1]。
outputs = tf.unstack(tf.transpose(output,[1,0,2]))
result = tf.matmul(outputs[-1], weights["out"])+biases["out"]       ##第二种结果计算方法，使用outputs[-1],它是一个数列。

return result

##训练步骤
pred = RNN(x, weights, biases)
cost = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits( logits= pred, labels=y)) ##使用softmax和cross entropy来作为损失函数
train_op = tf.train.AdamOptimizer(learn_rate).minimize(cost) ##使用AdamOptimizer的优化器

##计算准确率
correct = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))

init = tf.initialize_all_variables() ##初始化所有变量

with tf.Session() as sess:
sess.run(init)
step = 0
while step*batch_size < training_iters:
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
batch_xs = batch_xs.reshape([batch_size, n_steps, n_inputs]) ##转化为[128,28,28]的形式
sess.run([train_op], feed_dict={x: batch_xs, y: batch_ys})
if step % 20 == 0:
print(sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys}))

    step += 1