TensorFlow - 手写数字识别 (模型训练完成后的使用)
TensorFlow - 手写数字识别 (模型训练完成后的使用)
flyfish
当训练好模型之后，开始使用模型
模型所在路径\venv\ckpt_dir
import tensorflow as tf
import os
import numpy as np
from PIL import Image#pillow(PIL)
x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
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)
#权重初始化
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')
#第一层卷积
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
#d第二层卷积
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
#全连接层
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)
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
#输出层
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
#训练和评估模型
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
ckpt_dir = "./ckpt_dir"
saver = tf.train.Saver()
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(ckpt_dir)
if ckpt and ckpt.model_checkpoint_path:
print(ckpt.model_checkpoint_path)
saver.restore(sess, ckpt.model_checkpoint_path) # restore all variables
else:
raise FileNotFoundError("未找到模型")#raise 引发异常
image_path="D:\\1.png"
img = Image.open(image_path).convert('L')#灰度图(L)
img_shape = np.reshape(img, 784)
real_x = np.array([1-img_shape])# 0-255 uint8 8位无符号整数，取值：[0, 255] 如果采用1-大数变成小数
y = sess.run(y_conv, feed_dict={x: real_x,keep_prob: 1.0}) #y类似一个二维表，因为只有一张图片所以只有一行，y[0]包含10个值，
print('Predict digit', np.argmax(y[0]))#找出最大的值识别结果
./ckpt_dir\model.ckpt-10800
Predict digit 1
