PyCharm+Tensorflow CNN调用训练好的模型进行预测 (五)

通过Pycharm+tensorflow CNN 学习(四)可以训练得到模型,并将模型进行保存。本次博文中主要是调用训练好的进行预测。
该模型训练所使用的数据集是MNIST集。在对图片中数字进行识别前,需要对图片进行预处理,即将图片转变为MNIST数据集中图片的格式。预处理程序可以点击此处查看

调用模型进行预测的代码如下:

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import cv2
import numpy as np
#import imutils
# from scikit-image import data,segmentation,measure,morphology,color
from PIL import Image

im = Image.open('D:\\deng\\ppp\\888.png')
data = list(im.getdata())
result = [(255-x)*1.0/255.0 for x in data]
print(result)

sess = tf.Session()


def compute_accuracy(v_xs, v_ys):
    global prediction
    y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})            #(10000,10)
    correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1))      #比较是否相等,返回bool
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))          #将比较结果转换成tf.float32,并计算平均值
    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, 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')


keep_prob = tf.placeholder(tf.float32)
with tf.name_scope('inputs'):
    xs = tf.placeholder(tf.float32, [None, 784], name='x_input')  #28x28
    ys = tf.placeholder(tf.float32, [None, 10], name='y_input')
x_image = tf.reshape(xs, [-1, 28, 28, 1])
#print("n_samples:", x_image.shape)

#conv1 layer
W_conv1 = weight_variable([5, 5, 1, 32])  #patch 5x5 in size=1, out size 32
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 28x28x32
h_pool1 = max_pool_2x2(h_conv1)                          # size 14x14x32

#conv2 layer
W_conv2 = weight_variable([5, 5, 32, 64])  #patch 5x5 in size=32, out size 64
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 14x14x64
h_pool2 = max_pool_2x2(h_conv2)                          # size 7x7x64

#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)


cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),
                                              reduction_indices=[1]))       #loss
tf.summary.scalar('loss', cross_entropy)

with tf.name_scope('train'):
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

init = tf.global_variables_initializer()


#summary writer goes in here
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter('D:/deng/logs/train', sess.graph)
test_writer = tf.summary.FileWriter('D:/deng/logs/test', sess.graph)


sess.run(init)
saver = tf.train.Saver()
#与训练过程代码进行对比,主要不同的地方在这里
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    saver.restore(sess, "D:/deng/model/model.ckpt")#这里使用了之前保存的模型参数

    prediction = tf.argmax(prediction, 1)
    predint = prediction.eval(feed_dict={xs: [result], keep_prob: 1.0}, session=sess)

    print("recognize result: %d" %predint[0])

sess.close()

这个代码实测有效,只不过代码太过冗长了。本人会继续学习,尝试以最短的代码调用训练好的模型进行预测。若有什么建议或者错误之处,还请看到博文的朋友能够在评论区指出。感谢!

参考:
TensorFlow下利用MNIST训练模型识别手写数字

方法2:
相对于方法1,更加强大,并且简洁。感觉太爽了~

import tensorflow as tf
from PIL import Image

im = Image.open('D:\\deng\\ppp\\333.png')
data = list(im.getdata())
result = [(255-x)*1.0/255.0 for x in data]
print(result)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    saver = tf.train.import_meta_graph('D:/deng/model2/model.ckpt.meta')
    saver.restore(sess, "D:/deng/model2/model.ckpt")  # 这里使用了之前保存的模型参数
    pred = tf.get_collection('network-output')[0]
    prediction = tf.argmax(pred, 1)
    graph = tf.get_default_graph()
    xs = graph.get_operation_by_name('x_input').outputs[0]
    keep_prob = graph.get_operation_by_name('keep_prob').outputs[0]
    #keep_prob = graph.get_operation_by_name('y_inout').outputs[0]
    predint = prediction.eval(feed_dict={xs: [result], keep_prob: 1.0}, session=sess)

    print("recognize result: %d" % predint[0])

训练模型的代码如下:

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
sess = tf.Session()


def compute_accuracy(v_xs, v_ys):
    global prediction
    y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})
    correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, 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')


keep_prob = tf.placeholder(tf.float32, name='keep_prob')
# with tf.name_scope('inputs'):
xs = tf.placeholder(tf.float32, [None, 784], name='x_input')
ys = tf.placeholder(tf.float32, [None, 10], name='y_input')
x_image = tf.reshape(xs, [-1, 28, 28, 1])
#print("n_samples:", x_image.shape)

#conv1 layer
with tf.name_scope('conv1_layer'):
    with tf.name_scope('W_conv1'):
        W_conv1 = weight_variable([5, 5, 1, 32])
        tf.summary.histogram('conv1/wights', W_conv1)
    with tf.name_scope('b_conv1'):
        b_conv1 = bias_variable([32])
        tf.summary.histogram('conv1/biases', b_conv1)
    with tf.name_scope('conv1-wx_plus_b'):
        h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    with tf.name_scope('conv1_pooling'):
        h_pool1 = max_pool_2x2(h_conv1)

#conv2 layer
with tf.name_scope('conv2_layer'):
    with tf.name_scope('W_conv2'):
        W_conv2 = weight_variable([5, 5, 32, 64])
        tf.summary.histogram('conv2/wights', W_conv2)
    with tf.name_scope('b_conv2'):
        b_conv2 = bias_variable([64])
        tf.summary.histogram('conv2/biases', b_conv2)
    with tf.name_scope('conv2-wx_plus_b'):
        h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    with tf.name_scope('conv2_pooling'):
        h_pool2 = max_pool_2x2(h_conv2)

#func1 layer
with tf.name_scope('full-connected1'):
    with tf.name_scope('W_fc1'):
        W_fc1 = weight_variable([7*7*64, 1024])
        tf.summary.histogram('fc1/wights', W_fc1)
    with tf.name_scope('b_fc1'):
        b_fc1 = bias_variable([1024])
        tf.summary.histogram('fc1/biases', b_fc1)
    with tf.name_scope('fc1-wx_plus_b'):
        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
with tf.name_scope('full-connected2'):
    with tf.name_scope('W_fc2'):
        W_fc2 = weight_variable([1024, 10])
        tf.summary.histogram('fc2/wights', W_fc2)
    with tf.name_scope('b_fc2'):
        b_fc2 = bias_variable([10])
        tf.summary.histogram('fc2/biases', b_fc2)
    with tf.name_scope('fc2-wx_plus_b'):
        prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
        tf.add_to_collection('network-output', prediction)


cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),
                                              reduction_indices=[1]))
tf.summary.scalar('loss', cross_entropy)

with tf.name_scope('train'):
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

init = tf.global_variables_initializer()


#summary writer goes in here
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter('D:/deng/logs/train', sess.graph)
#test_writer = tf.summary.FileWriter('D:/deng/logs/test', sess.graph)


sess.run(init)
saver = tf.train.Saver()

for i in range(2000):
    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:
        result = sess.run(merged, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 1})
        train_writer.add_summary(result, i)
        print(compute_accuracy(
            mnist.test.images, mnist.test.labels
        ))

saver = tf.train.Saver()
model_path = "D:/deng/model2/model.ckpt"
saver.save(sess, model_path)
sess.close()

参考:
Tensorflow实现在训练好的模型上进行测试

你可能感兴趣的:(PyCharm+Tensorflow CNN调用训练好的模型进行预测 (五))