使用MINST数据集训练模型生成loss和准确率趋势图后，使用模型文件推理（附代码和MINST数据集和推理手写图片）
使用训练脚本训练后保存模型文件并生成loss和准确率趋势图保存图片
训练代码如下
#coding: utf-8
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
input_x = tf.placeholder(tf.float32, [None, 28*28])/255
output_y = tf.placeholder(tf.int32, [None, 10])
input_x_images = tf.reshape(input_x, [-1, 28, 28, 1])
#获取数据
test_x = mnist.test.images[:3000]
test_y = mnist.test.labels[:3000]
#构建网络
conv1 = tf.layers.conv2d(inputs=input_x_images,
filters=32,
kernel_size=[5, 5],
strides=1,
padding='same',
activation=tf.nn.relu
)
pool1 = tf.layers.max_pooling2d(
inputs=conv1,
pool_size=[2,2],
strides=2
)
conv2 = tf.layers.conv2d(inputs=pool1,
filters=64,
kernel_size=[5, 5],
strides=1,
padding='same',
activation=tf.nn.relu
)
pool2 = tf.layers.max_pooling2d(
inputs=conv2,
pool_size=[2,2],
strides=2
)
flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
dense = tf.layers.dense(inputs=flat, units=1024, activation=tf.nn.relu)
dropout = tf.layers.dropout(inputs=dense, rate=0.5)
logits = tf.layers.dense(inputs=dropout, units=10)
loss = tf.losses.softmax_cross_entropy(onehot_labels=output_y, logits=logits)
train_op = tf.train.AdamOptimizer(learning_rate=0.005).minimize(loss)
accuracy = tf.metrics.accuracy(
labels=tf.argmax(output_y, axis=1),
predictions=tf.argmax(logits, axis=1),)[1]
with tf.Session() as sess:
init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init)
#初始化
x= []
x2=[]
y1=[]
y2=[]
saver = tf.train.Saver()
#迭代
for i in range(20000):
batch = mnist.train.next_batch(50)
train_loss, train_op_ = sess.run([loss, train_op], {input_x: batch[0], output_y: batch[1]})
if i % 100 == 0:
test_accuracy = sess.run(accuracy, {input_x: test_x, output_y: test_y})
print("Step=%d, Train loss=%.4f, [Test accuracy=%.2f]" % (i, train_loss, test_accuracy))
#画趋势图
plt.cla()
x.append(i)
y1.append(train_loss)
y2.append(test_accuracy)
plt.plot(x, y1, label='Loss rate', color='g')
plt.plot(x, y2, label='Accuracy rate', color='b')
plt.xlabel('Step')
plt.ylabel('Rate')
plt.title('Train table')
plt.legend()
plt.pause(1.0)
#保存趋势图
plt.savefig("./RATE.png")
#保存模型文件
saver.save(sess, './model.ckpt')使用生成的模型对手写图片识别
推理代码如下
#coding: utf-8
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from PIL import Image, ImageFilter
import tensorflow as tf
import matplotlib.pyplot as plt
def imageprepare(i):
im = Image.open('./mnistpic/mnist_test_'+ str(i) +'.png') #读取的图片所在路径，注意是28*28像素
im = im.convert('L')
im.show()
tv = list(im.getdata())
return tv
#构造相同网络
input_x = tf.placeholder(tf.float32, [None, 28*28])/255
output_y = tf.placeholder(tf.int32, [None, 10])
input_x_images = tf.reshape(input_x, [-1, 28, 28, 1])
conv1 = tf.layers.conv2d(inputs=input_x_images,
filters=32,
kernel_size=[5, 5],
strides=1,
padding='same',
activation=tf.nn.relu
)
pool1 = tf.layers.max_pooling2d(
inputs=conv1,
pool_size=[2,2],
strides=2
)
conv2 = tf.layers.conv2d(inputs=pool1,
filters=64,
kernel_size=[5, 5],
strides=1,
padding='same',
activation=tf.nn.relu
)
pool2 = tf.layers.max_pooling2d(
inputs=conv2,
pool_size=[2,2],
strides=2
)
flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
dense = tf.layers.dense(inputs=flat, units=1024, activation=tf.nn.relu)
dropout = tf.layers.dropout(inputs=dense, rate=0.5)
logits = tf.layers.dense(inputs=dropout, units=10)
loss = tf.losses.softmax_cross_entropy(onehot_labels=output_y, logits=logits)
train_op = tf.train.AdamOptimizer(learning_rate=0.005).minimize(loss)
accuracy = tf.metrics.accuracy(
labels=tf.argmax(output_y, axis=1),
predictions=tf.argmax(logits, axis=1),)[1]
#推理
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, "./model.ckpt") #使用原先生成的模型
for i in range(0,10):
result=imageprepare(i)
predictions=tf.argmax(logits, axis=1)
predint=predictions.eval(feed_dict={input_x: [result]}, session=sess)
print('识别结果:')
print(predint[0])