使用檢查點在Tensorflow Mnist模型上測試圖像

Question

我是TensorFlow的新手。我得到了mnist火車樣本，我想通過生成檢查點來測試圖像。我參考了Tensorflow文檔並生成了檢查點並試圖測試樣本通過訪問softmax圖層。圖像number-9softmax給了我一個無效的單熱編碼數組，如'array（[[0.,0.1,0,0,0,0,0,0。 ，0,0,0,0]，dtype = float32）'，當我試圖訪問softmax使用

softmax = graph.get_tensor_by_name（'SOFTMAX：0'）。

我試過用不同的圖像進行測試，但沒有給出適當的結果。

1.我推測，softmax會給我一些概率。我是對嗎？

2.Am我正確保存模型？

3.Am我訪問正確的圖層來測試輸入嗎？

4.我的測試/培訓代碼中是否還有其他內容？

對不起，在這裏發佈的一切。

這是我的火車代碼：

from __future__ import division, print_function, unicode_literals 
import tensorflow as tf 
from time import time 
import numpy as np 
import os 
import scipy.ndimage as ndimage 
from scipy import misc 

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

logs_train_dir = '/home/test/Logs' 

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

def bias_variable(shape,name): 
    initial = tf.constant(0.1, shape=shape) 
    return tf.Variable(initial,name=name+'_bias') 

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

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

# correct labels 

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

# reshape the input data to image dimensions 

x = tf.placeholder(tf.float32, [None, 784],name='X')#Input Tensor 
x_image = tf.reshape(x, [-1, 28, 28, 1],name='X_Image') 

# build the network 

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,name='h_conv1') 
h_pool1 = max_pool_2x2(h_conv1,'h_pool1') 
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,name='h_conv2') 
h_pool2 = max_pool_2x2(h_conv2,'W_conv2') 
W_fc1 = weight_variable([7 * 7 * 64, 1024],name='wc1') 
b_fc1 = bias_variable([1024],name='b_fc1') 
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(tf.float32,name='KEEP_PROB') 
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) 
W_fc2 = weight_variable([1024, 10],name='w_fc2') 
b_fc2 = bias_variable([10],name='b_fc2') 
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2,name='SOFTMAX')#Softmax Tensor 

# define the loss function 
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]),name='CROSS_ENTROPY') 
loss_summary = tf.summary.scalar('loss_sc',cross_entropy) 

# define training step and accuracy 

train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) 
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1),name='CORRECT_PRED') 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32),name='ACCURACY') 
accuracy_summary = tf.summary.scalar('accuracy_sc', accuracy) 

# create a saver 
saver = tf.train.Saver() 

# initialize the graph 
init = tf.global_variables_initializer() 
summary_op = tf.summary.merge_all() 

sess = tf.Session() 
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph) 
sess.run(init) 

# train 

print("Startin Burn-In...") 
for i in range(500): 
    input_images, correct_predictions = mnist.train.next_batch(50) 
    if i % 100 == 0: 
     train_accuracy = sess.run(accuracy, feed_dict={x: input_images, y_: correct_predictions, keep_prob: 1.0}) 
     print("step %d, training accuracy_a %g" % (i, train_accuracy)) 

    sess.run(train_step, feed_dict={x: input_images, y_: correct_predictions, keep_prob: 0.5}) 

print("Starting the training...") 
start_time = time() 
for i in range(20000): 
    input_images, correct_predictions = mnist.train.next_batch(50) 
    if i % 100 == 0: 
     train_accuracy = sess.run(accuracy, feed_dict={x: input_images, y_: correct_predictions, keep_prob: 1.0}) 
     print("step %d, training accuracy_b %g" % (i, train_accuracy)) 
    sess.run(train_step, feed_dict={x: input_images, y_: correct_predictions, keep_prob: 0.5}) 

    summary_str = sess.run(summary_op,feed_dict={x: input_images, y_: correct_predictions, keep_prob: 0.5}) 
    train_writer.add_summary(summary_str, i) 

    print('SAVING CHECKPOINTS......i is ',i) 

    if i % 1000 == 0 or (i+1) == 20000: 
     checkpoint_path = os.path.join(logs_train_dir,'cnn_new_model.ckpt') 
     print('checkpoint_path is ',checkpoint_path) 
     saver.save(sess,checkpoint_path,global_step=i) 

print("The training took %.4f seconds." % (time() - start_time)) 
# validate 
print("test accuracy %g" % sess.run(accuracy, feed_dict={ 
x: mnist.test.images, 
y_: mnist.test.labels, 
keep_prob: 1.0})) 

精度爲0.97。

這是我的測試代碼：

import numpy as np 
import tensorflow as tf 
import scipy.ndimage as ndimage 
from scipy import misc 
import cv2 as cv 

def get_test_image():  
    image = cv.imread('/home/test/Downloads/9.png', 0) 
    resized = cv.resize(image, (28,28), interpolation = cv.INTER_AREA) 
    image = np.array(resized) 
    flat = np.ndarray.flatten(image)  
    reshaped_image = np.reshape(flat,(1, 784)) 
return reshaped_image 


def evaluate_one_image(): 

    image_array = get_test_image() 
    image_array = image_array.astype(np.float32) 
    logs_train_dir ='/home/test/Logs'  
    model_path = logs_train_dir+"/cnn_new_model.ckpt-19999" 
    detection_graph = tf.Graph() 

    with tf.Session(graph=detection_graph) as sess: 
     # Load the graph with the trained states 
     loader = tf.train.import_meta_graph(model_path+'.meta') 
     loader.restore(sess, model_path) 

     # Get the tensors by their variable name 

     image_tensor = detection_graph.get_tensor_by_name('X:0') 
     softmax = detection_graph.get_tensor_by_name('SOFTMAX:0') 
     keep_prob = detection_graph.get_tensor_by_name('KEEP_PROB:0')  

     # Make prediction 

     softmax = sess.run(softmax, feed_dict={image_tensor: image_array,keep_prob:0.75}) 

     print('softmax is ', cost_val,'\n\n') 
     print('softmax maximum val is ', np.argmax(cost_val)) 

evaluate_one_image() 

所以，當我用數字9的圖像進行測試，它給了我下面的輸出：

SOFTMAX是[0，1， 0。，0，0，0，0，0，0，0。]]

SOFTMAX最大val爲1

我不知道，我哪裏出錯了。任何幫助都會非常有用，並且非常感謝。

Answer 1

1. 評估/預測期間不使用壓差。所以，你需要設置keep_prob=1

2. 檢查輸入圖像image_array的像素值，像素值應在範圍[0, 1]，否則你需要通過減去圖像均值和由圖像STD劃分正常化的像素值

對於函數加載圖像，你可以添加以下行正常化

def get_test_image(): 
    ... 
    image = np.array(resized) 
    mean = image.mean() 
    std = image.std() 
    image = np.subtract(image, mean) 
    image = np.divide(image, std) 
    image = np.clip(image, 0, 1.000001) 
    ...