TensorFlow的mnist例子

通过几个不同神经网络模型评估训练结果

单层网络（梯度下降）

只有输入和输出

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist=input_data.read_data_sets("mnist/",one_hot=True)

print(mnist.train.images.shape,mnist.train.labels.shape)

sess=tf.InteractiveSession()

x=tf.placeholder(tf.float32,[None,784])

w=tf.Variable(tf.zeros([784,10]))

b=tf.Variable(tf.zeros([10]))

y=tf.nn.softmax(tf.matmul(x,w)+b)

y_=tf.placeholder(tf.float32,[None,10])

corss_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),reduction_indices=[1]))

train=tf.train.GradientDescentOptimizer(0.5).minimize(corss_entropy)

tf.global_variables_initializer().run()

for i in range(1000):

    batch_xs,batch_ys=mnist.train.next_batch(100)

    train.run({x:batch_xs,y_:batch_ys})

correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))

accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

print(accuracy.eval({x:mnist.test.images,y_:mnist.test.labels}))

多层神经网络（全连接 MLP）

这里使用2个隐藏层

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist=input_data.read_data_sets("mnist/",one_hot=True)

sess=tf.InteractiveSession()

in_units=784

h1_units=300

h2_units=200

w1=tf.Variable(tf.truncated_normal([in_units,h1_units],stddev=0.1))

w0=tf.Variable(tf.truncated_normal([h1_units,h2_units],stddev=0.1))

b1=tf.Variable(tf.zeros([h1_units]))

b0=tf.Variable(tf.zeros([h2_units]))

w2=tf.Variable(tf.zeros([h2_units,10]))

b2=tf.Variable(tf.zeros([10]))

x=tf.placeholder(tf.float32,[None,in_units])

keep_prob=tf.placeholder(tf.float32)

hidden1=tf.nn.relu(tf.matmul(x,w1)+b1)

hidden1_drop=tf.nn.dropout(hidden1,keep_prob)

hidden0=tf.nn.relu(tf.matmul(hidden1,w0)+b0)
#
hidden0_drop=tf.nn.dropout(hidden0,keep_prob)

y=tf.nn.softmax(tf.matmul(hidden0,w2)+b2)
#
y_=tf.placeholder(tf.float32,[None,10])

corss_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),reduction_indices=[1]))

train=tf.train.AdagradOptimizer(0.1).minimize(corss_entropy)

tf.global_variables_initializer().run()

for i in range(3000):

    batch_xs,batch_ys=mnist.train.next_batch(100)

    train.run({x:batch_xs,y_:batch_ys,keep_prob:0.75})

correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))

accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

print(accuracy.eval({x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0}))

卷积神经网络（卷积）

这里训练好后固化模型供以后使用

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist=input_data.read_data_sets("mnist/",one_hot=True)

sess=tf.InteractiveSession()

def weight_variable(shape,name):

    initial=tf.truncated_normal(shape,stddev=0.1)

    return tf.Variable(initial,name=name)

def bias_variable(shape,name):

    initial=tf.constant(0.1,shape=shape)

    return tf.Variable(initial,name=name)

def conv2d(x,W):

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

def max_pool(x):

    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding="SAME")

x=tf.placeholder(tf.float32,[None,784],name='x')

y_=tf.placeholder(tf.float32,[None,10],name='y')
#
x_image=tf.reshape(x,[-1,28,28,1])

w_conv1=weight_variable([5,5,1,32],'w_conv1')

b_conv1=bias_variable([32],'b_conv1')

h_conv1=tf.nn.relu(conv2d(x_image,w_conv1)+b_conv1)

h_pool1=max_pool(h_conv1)
#
w_conv2=weight_variable([5,5,32,64],'w_conv2')
#
b_conv2=bias_variable([64],'b_conv2')
#
h_conv2=tf.nn.relu(conv2d(h_pool1,w_conv2)+b_conv2)
#
h_pool2=max_pool(h_conv2)

w_fc1=weight_variable([7*7*64,1024],'w_fc1')

b_fc1=bias_variable([1024],'b_fc1')

h_pool_flat=tf.reshape(h_pool2,[-1,7*7*64])

h_fc1=tf.nn.relu(tf.matmul(h_pool_flat,w_fc1)+b_fc1)

keep_prob=tf.placeholder(tf.float32,name='keep_prob')

h_drop=tf.nn.dropout(h_fc1,keep_prob)

w_fc2=weight_variable([1024,10],'w_fc2')

b_fc2=bias_variable([10],'b_fc2')

y_conv=tf.nn.softmax(tf.matmul(h_drop,w_fc2)+b_fc2)

corss_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y_conv),reduction_indices=[1]))

train=tf.train.AdamOptimizer(1e-4).minimize(corss_entropy)

tf.global_variables_initializer().run()

correct_prediction=tf.equal(tf.argmax(y_conv,1),tf.argmax(y_,1))

accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

for i in range(10000):

    batch_xs,batch_ys=mnist.train.next_batch(50)

    if i%100==0:

        train_accuracy=accuracy.eval({x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0})

        print(i,train_accuracy)

    train.run({x:batch_xs,y_:batch_ys,keep_prob:0.5})

saver = tf.train.Saver()

saver.save(sess,'save/model.ckpt',global_step=1000)

print(accuracy.eval({x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0}))

使用刚刚训练好的模型

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist=input_data.read_data_sets("mnist/",one_hot=True)

sess=tf.InteractiveSession()

def conv2d(x,W):

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

def max_pool(x):

    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding="SAME")

y_=tf.placeholder(tf.float32,[None,10],name='y')

x=tf.placeholder(tf.float32,[None,784],name='x')

keep_prob=tf.placeholder(tf.float32,name='keep_prob')

#First let's load meta graph and restore weights

saver = tf.train.import_meta_graph('save/model.ckpt-1000.meta')

saver.restore(sess,tf.train.latest_checkpoint('save/'))

graph = tf.get_default_graph()

x_image=tf.reshape(x,[-1,28,28,1])

w_conv1=graph.get_tensor_by_name('w_conv1:0')

b_conv1=graph.get_tensor_by_name('b_conv1:0')

h_conv1=tf.nn.relu(conv2d(x_image,w_conv1)+b_conv1)

w_conv2=graph.get_tensor_by_name('w_conv2:0')

b_conv2=graph.get_tensor_by_name('b_conv2:0')

h_pool1=max_pool(h_conv1)

h_conv2=tf.nn.relu(conv2d(h_pool1,w_conv2)+b_conv2)

h_pool2=max_pool(h_conv2)

h_pool_flat=tf.reshape(h_pool2,[-1,7*7*64])

w_fc1=graph.get_tensor_by_name('w_fc1:0')

b_fc1=graph.get_tensor_by_name('b_fc1:0')

h_fc1=tf.nn.relu(tf.matmul(h_pool_flat,w_fc1)+b_fc1)

h_drop=tf.nn.dropout(h_fc1,keep_prob)

b_fc2=graph.get_tensor_by_name('b_fc2:0')

w_fc2=graph.get_tensor_by_name('w_fc2:0')

y_conv=tf.nn.softmax(tf.matmul(h_drop,w_fc2)+b_fc2)

correct_prediction=tf.equal(tf.argmax(y_conv,1),tf.argmax(y_,1))

accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

train_accuracy = accuracy.eval({x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})

print(train_accuracy)

识别自己手写的图片

代码照之前稍做改动

图像为28*28像素

import tensorflow as tf

from PIL import Image, ImageFilter

import matplotlib.pyplot as plt

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

sess=tf.InteractiveSession(config=tf.ConfigProto(allow_soft_placement=True))

def imageprepare():

    im = Image.open('8.png') #读取的图片所在路径，注意是28*28像素

    plt.imshow(im)  #显示需要识别的图片

    plt.show()

    im = im.convert('L')

    tv = list(im.getdata())

    tva = [(255-x)*1.0/255.0 for x in tv]

    return tva

result=imageprepare()

def conv2d(x,W):

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

def max_pool(x):

    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding="SAME")

y_=tf.placeholder(tf.float32,[None,10],name='y')

x=tf.placeholder(tf.float32,[None,784],name='x')

keep_prob=tf.placeholder(tf.float32,name='keep_prob')

#First let's load meta graph and restore weights

saver = tf.train.import_meta_graph('save/model.ckpt-1000.meta')

saver.restore(sess,tf.train.latest_checkpoint('save/'))

graph = tf.get_default_graph()

x_image=tf.reshape(x,[-1,28,28,1])

w_conv1=graph.get_tensor_by_name('w_conv1:0')

b_conv1=graph.get_tensor_by_name('b_conv1:0')

h_conv1=tf.nn.relu(conv2d(x_image,w_conv1)+b_conv1)

w_conv2=graph.get_tensor_by_name('w_conv2:0')

b_conv2=graph.get_tensor_by_name('b_conv2:0')

h_pool1=max_pool(h_conv1)

h_conv2=tf.nn.relu(conv2d(h_pool1,w_conv2)+b_conv2)

h_pool2=max_pool(h_conv2)

h_pool_flat=tf.reshape(h_pool2,[-1,7*7*64])

w_fc1=graph.get_tensor_by_name('w_fc1:0')

b_fc1=graph.get_tensor_by_name('b_fc1:0')

h_fc1=tf.nn.relu(tf.matmul(h_pool_flat,w_fc1)+b_fc1)

h_drop=tf.nn.dropout(h_fc1,keep_prob)

b_fc2=graph.get_tensor_by_name('b_fc2:0')

w_fc2=graph.get_tensor_by_name('w_fc2:0')

y_conv=tf.nn.softmax(tf.matmul(h_drop,w_fc2)+b_fc2)

prediction=tf.argmax(y_conv,1)

predint = prediction.eval(feed_dict={x: [result], keep_prob: 1.0}, session=sess)

print(predint[0])
# correct_prediction=tf.equal(tf.argmax(y_conv,1),tf.argmax(y_,1))
# #
# accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
# #
# train_accuracy = accuracy.eval({x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})
#
# print(train_accuracy)

1: https://cv-tricks.com/tensorflow-tutorial/save-restore-tensorflow-models-quick-complete-tutorial/
2: 黄文坚《TensorFlow 实战》
3：https://blog.csdn.net/qq_38269418/article/details/78991649

TensorFlow 训练 mnist模型

TensorFlow的mnist例子

单层网络（梯度下降）

多层神经网络（全连接 MLP）

卷积神经网络（卷积）

使用刚刚训练好的模型

识别自己手写的图片

猜你喜欢

TensorFlow 训练 mnist模型

TensorFlow的mnist例子

单层网络 （梯度下降）

多层神经网络（全连接 MLP）

卷积神经网络（卷积）

使用刚刚训练好的模型

识别自己手写的图片

猜你喜欢

单层网络（梯度下降）