
# coding: utf-8

# # 自定义数据集上的CNN训练

# In[1]:

import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
get_ipython().magic(u'matplotlib inline')
print ("当前TensorFlow版本为 [%s]" % (tf.__version__))
print ("所有包装载完毕")


# ## 数据载入
# ### 此处custom_data.npz为basic_gendataset.ipynb文件生成

# In[2]:

# LOAD DATA
cwd = os.getcwd()
loadpath = cwd + "/data/custom_data.npz"
l = np.load(loadpath)
print (l.files)

# PARSE LOADED DATA
trainimg   = l['trainimg']
trainlabel = l['trainlabel']
testimg    = l['testimg']
testlabel  = l['testlabel']
imgsize    = l['imgsize']
categories = l['categories']
use_gray   = l['use_gray']
if use_gray:
    nimgch = 1
else:
    nimgch = 3
ntrain = trainimg.shape[0]
nclass = trainlabel.shape[1]
dim    = trainimg.shape[1]
ntest  = testimg.shape[0]
print ("%d TRAIN IMAGES" % (ntrain))
print ("%d TEST IMAGES" % (ntest))
print ("%d DIMENSIONAL INPUT" % (dim))
print ("%d CLASSES" % (nclass))
print (imgsize)
print (categories)


# ## 定义网络

# In[3]:

# NETWORK TOPOLOGIES
n_input    = dim
n_channel  = 64 
n_classes  = nclass  

# INPUTS AND OUTPUTS
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
    
# NETWORK PARAMETERS
stddev = 0.1
weights = {
    'c1': tf.Variable(tf.random_normal([7, 7, nimgch, n_channel], stddev=stddev)),
    'c2': tf.Variable(tf.random_normal([7, 7, n_channel, 128], stddev=stddev)),
    'd1': tf.Variable(tf.random_normal([dim//16*128, n_classes], stddev=stddev))
}
biases = {
    'c1': tf.Variable(tf.random_normal([n_channel], stddev=stddev)),
    'c2': tf.Variable(tf.random_normal([128], stddev=stddev)),
    'd1': tf.Variable(tf.random_normal([n_classes], stddev=stddev))
}
print ("网络定义完毕")


# ## 定义模型

# In[4]:

# MODEL
def CNN(_x, _w, _b):
    # RESHAPE
    _x_r = tf.reshape(_x, shape=[-1, imgsize[0], imgsize[1], nimgch])
    # CONVOLUTION
    _conv1 = tf.nn.conv2d(_x_r, _w['c1'], strides=[1, 1, 1, 1], padding='SAME')
    # ADD BIAS
    _conv2 = tf.nn.bias_add(_conv1, _b['c1'])
    # RELU
    _conv3 = tf.nn.relu(_conv2)
    # MAX-POOL
    _pool  = tf.nn.max_pool(_conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    
    temp = tf.nn.conv2d(_pool, _w['c2'], strides=[1, 1, 1, 1], padding='SAME')
    temp = tf.nn.bias_add(temp, _b['c2'])
    temp = tf.nn.relu(temp)
    temp = tf.nn.max_pool(temp, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    
    # VECTORIZE
    _dense = tf.reshape(temp, [-1, _w['d1'].get_shape().as_list()[0]])
    # DENSE
    _logit = tf.add(tf.matmul(_dense, _w['d1']), _b['d1'])
    _out = {
        'x_r': _x_r, 'conv1': _conv1, 'conv2': _conv2, 'conv3': _conv3
        , 'pool': _pool, 'dense': _dense, 'logit': _logit
    }
    return _out

# PREDICTION
cnnout = CNN(x, weights, biases)

# LOSS AND OPTIMIZER
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
        labels=y, logits=cnnout['logit']))
optm = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost) 
corr = tf.equal(tf.argmax(cnnout['logit'], 1), tf.argmax(y, 1))    
accr = tf.reduce_mean(tf.cast(corr, "float"))

# INITIALIZER
init = tf.global_variables_initializer()
print ("模型函数定义完毕")


# ## 运行

# In[5]:

# PARAMETERS
training_epochs = 50
batch_size      = 100
disp_each       = 10
plot_each       = 10
# LAUNCH THE GRAPH
sess = tf.Session()
sess.run(init)
# OPTIMIZE
for epoch in range(training_epochs):
    total_batch = int(ntrain/batch_size)
    randpermlist = np.random.permutation(ntrain)
    sun_cost = 0.
    for i in range(total_batch):
        randidx  = randpermlist[i*batch_size:min((i+1)*batch_size, ntrain-1)]
        batch_xs = trainimg[randidx, :]
        batch_ys = trainlabel[randidx, :]                
        feeds = {x: batch_xs, y: batch_ys}
        sess.run(optm, feed_dict=feeds)
        sun_cost += sess.run(cost, feed_dict=feeds)
    avg_cost = sun_cost / total_batch
        
    # DISPLAY
    if (epoch+1) % disp_each == 0 or epoch == training_epochs-1:
        print ("Epoch: %03d/%03d cost: %.9f" % (epoch+1, training_epochs, avg_cost))
        feeds = {x: batch_xs, y: batch_ys}
        train_acc = sess.run(accr, feed_dict=feeds)
        print (" TRAIN ACCURACY: %.3f" % (train_acc))
        feeds = {x: testimg, y: testlabel}
        test_acc = sess.run(accr, feed_dict=feeds)
        print (" TEST ACCURACY: %.3f" % (test_acc))
        
    # PLOT
    if (epoch+1) % plot_each == 0 or epoch == training_epochs-1:
        ntemp = int(ntest/batch_size) + 1 
        missidxlist = []
        for ii in range(ntemp):
            iii = np.arange(ii*batch_size, min(ntest, (ii+1)*batch_size))
            feeds = {x: testimg[iii, :], y: testlabel[iii, :]}
            missidx = np.where(sess.run(corr, feed_dict=feeds) == 0)[0]
            missidxlist = np.concatenate((missidxlist, iii[missidx]))
        nmiss = len(missidxlist)
        print (" %d MISSED" % (nmiss))
        # PLOT MISSCLASSIFIED (UPTO THREE)
        missidxlist = np.random.permutation(missidxlist.astype(int))
        selmissidx  = missidxlist[:min(3, nmiss)].astype(int)
        feeds_miss  = {x: testimg[selmissidx, :], y: testlabel[selmissidx, :]}
        pred_miss   = sess.run(cnnout['logit'], feed_dict=feeds_miss)
        answ_miss   = np.argmax(pred_miss, 1)
        answ_corr   = np.argmax(testlabel[selmissidx, :], 1)
        for idx in range(len(selmissidx)):
            cidx = selmissidx[idx]
            anw  = answ_miss[idx]
            ref  = answ_corr[idx]
            if use_gray != 0:
                currimg = np.reshape(testimg[cidx, :], (imgsize[0], imgsize[1]))
                plt.imshow(currimg, cmap='gray')
            else:
                currimg = np.reshape(testimg[cidx, :], (imgsize[0], imgsize[1], -1))
                plt.imshow(currimg)
            anw_category = categories[anw]
            ref_category = categories[ref]
            title_string = ("IDX: %d CURR ANWR: [%s] TRUE LABEL: [%s]" 
                % (cidx, anw_category, ref_category))
            plt.title(title_string) 
            plt.show() 
print ("优化完毕")


794330c7b40e1f8f7d370a9db0dbc46ef60ee296
<mask token>

<mask token>


def CNN(_x, _w, _b):
    _x_r = tf.reshape(_x, shape=[-1, imgsize[0], imgsize[1], nimgch])
    _conv1 = tf.nn.conv2d(_x_r, _w['c1'], strides=[1, 1, 1, 1], padding='SAME')
    _conv2 = tf.nn.bias_add(_conv1, _b['c1'])
    _conv3 = tf.nn.relu(_conv2)
    _pool = tf.nn.max_pool(_conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
        padding='SAME')
    temp = tf.nn.conv2d(_pool, _w['c2'], strides=[1, 1, 1, 1], padding='SAME')
    temp = tf.nn.bias_add(temp, _b['c2'])
    temp = tf.nn.relu(temp)
    temp = tf.nn.max_pool(temp, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
        padding='SAME')
    _dense = tf.reshape(temp, [-1, _w['d1'].get_shape().as_list()[0]])
    _logit = tf.add(tf.matmul(_dense, _w['d1']), _b['d1'])
    _out = {'x_r': _x_r, 'conv1': _conv1, 'conv2': _conv2, 'conv3': _conv3,
        'pool': _pool, 'dense': _dense, 'logit': _logit}
    return _out


<mask token>

<mask token>
get_ipython().magic(u'matplotlib inline')
print('当前TensorFlow版本为 [%s]' % tf.__version__)
print('所有包装载完毕')
<mask token>
print(l.files)
<mask token>
if use_gray:
    nimgch = 1
else:
    nimgch = 3
<mask token>
print('%d TRAIN IMAGES' % ntrain)
print('%d TEST IMAGES' % ntest)
print('%d DIMENSIONAL INPUT' % dim)
print('%d CLASSES' % nclass)
print(imgsize)
print(categories)
<mask token>
print('网络定义完毕')


def CNN(_x, _w, _b):
    _x_r = tf.reshape(_x, shape=[-1, imgsize[0], imgsize[1], nimgch])
    _conv1 = tf.nn.conv2d(_x_r, _w['c1'], strides=[1, 1, 1, 1], padding='SAME')
    _conv2 = tf.nn.bias_add(_conv1, _b['c1'])
    _conv3 = tf.nn.relu(_conv2)
    _pool = tf.nn.max_pool(_conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
        padding='SAME')
    temp = tf.nn.conv2d(_pool, _w['c2'], strides=[1, 1, 1, 1], padding='SAME')
    temp = tf.nn.bias_add(temp, _b['c2'])
    temp = tf.nn.relu(temp)
    temp = tf.nn.max_pool(temp, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
        padding='SAME')
    _dense = tf.reshape(temp, [-1, _w['d1'].get_shape().as_list()[0]])
    _logit = tf.add(tf.matmul(_dense, _w['d1']), _b['d1'])
    _out = {'x_r': _x_r, 'conv1': _conv1, 'conv2': _conv2, 'conv3': _conv3,
        'pool': _pool, 'dense': _dense, 'logit': _logit}
    return _out


<mask token>
print('模型函数定义完毕')
<mask token>
sess.run(init)
for epoch in range(training_epochs):
    total_batch = int(ntrain / batch_size)
    randpermlist = np.random.permutation(ntrain)
    sun_cost = 0.0
    for i in range(total_batch):
        randidx = randpermlist[i * batch_size:min((i + 1) * batch_size, 
            ntrain - 1)]
        batch_xs = trainimg[randidx, :]
        batch_ys = trainlabel[randidx, :]
        feeds = {x: batch_xs, y: batch_ys}
        sess.run(optm, feed_dict=feeds)
        sun_cost += sess.run(cost, feed_dict=feeds)
    avg_cost = sun_cost / total_batch
    if (epoch + 1) % disp_each == 0 or epoch == training_epochs - 1:
        print('Epoch: %03d/%03d cost: %.9f' % (epoch + 1, training_epochs,
            avg_cost))
        feeds = {x: batch_xs, y: batch_ys}
        train_acc = sess.run(accr, feed_dict=feeds)
        print(' TRAIN ACCURACY: %.3f' % train_acc)
        feeds = {x: testimg, y: testlabel}
        test_acc = sess.run(accr, feed_dict=feeds)
        print(' TEST ACCURACY: %.3f' % test_acc)
    if (epoch + 1) % plot_each == 0 or epoch == training_epochs - 1:
        ntemp = int(ntest / batch_size) + 1
        missidxlist = []
        for ii in range(ntemp):
            iii = np.arange(ii * batch_size, min(ntest, (ii + 1) * batch_size))
            feeds = {x: testimg[iii, :], y: testlabel[iii, :]}
            missidx = np.where(sess.run(corr, feed_dict=feeds) == 0)[0]
            missidxlist = np.concatenate((missidxlist, iii[missidx]))
        nmiss = len(missidxlist)
        print(' %d MISSED' % nmiss)
        missidxlist = np.random.permutation(missidxlist.astype(int))
        selmissidx = missidxlist[:min(3, nmiss)].astype(int)
        feeds_miss = {x: testimg[selmissidx, :], y: testlabel[selmissidx, :]}
        pred_miss = sess.run(cnnout['logit'], feed_dict=feeds_miss)
        answ_miss = np.argmax(pred_miss, 1)
        answ_corr = np.argmax(testlabel[selmissidx, :], 1)
        for idx in range(len(selmissidx)):
            cidx = selmissidx[idx]
            anw = answ_miss[idx]
            ref = answ_corr[idx]
            if use_gray != 0:
                currimg = np.reshape(testimg[cidx, :], (imgsize[0], imgsize[1])
                    )
                plt.imshow(currimg, cmap='gray')
            else:
                currimg = np.reshape(testimg[cidx, :], (imgsize[0], imgsize
                    [1], -1))
                plt.imshow(currimg)
            anw_category = categories[anw]
            ref_category = categories[ref]
            title_string = 'IDX: %d CURR ANWR: [%s] TRUE LABEL: [%s]' % (cidx,
                anw_category, ref_category)
            plt.title(title_string)
            plt.show()
print('优化完毕')

import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
get_ipython().magic(u'matplotlib inline')
print('当前TensorFlow版本为 [%s]' % tf.__version__)
print('所有包装载完毕')
cwd = os.getcwd()
loadpath = cwd + '/data/custom_data.npz'
l = np.load(loadpath)
print(l.files)
trainimg = l['trainimg']
trainlabel = l['trainlabel']
testimg = l['testimg']
testlabel = l['testlabel']
imgsize = l['imgsize']
categories = l['categories']
use_gray = l['use_gray']
if use_gray:
    nimgch = 1
else:
    nimgch = 3
ntrain = trainimg.shape[0]
nclass = trainlabel.shape[1]
dim = trainimg.shape[1]
ntest = testimg.shape[0]
print('%d TRAIN IMAGES' % ntrain)
print('%d TEST IMAGES' % ntest)
print('%d DIMENSIONAL INPUT' % dim)
print('%d CLASSES' % nclass)
print(imgsize)
print(categories)
n_input = dim
n_channel = 64
n_classes = nclass
x = tf.placeholder('float', [None, n_input])
y = tf.placeholder('float', [None, n_classes])
stddev = 0.1
weights = {'c1': tf.Variable(tf.random_normal([7, 7, nimgch, n_channel],
    stddev=stddev)), 'c2': tf.Variable(tf.random_normal([7, 7, n_channel, 
    128], stddev=stddev)), 'd1': tf.Variable(tf.random_normal([dim // 16 * 
    128, n_classes], stddev=stddev))}
biases = {'c1': tf.Variable(tf.random_normal([n_channel], stddev=stddev)),
    'c2': tf.Variable(tf.random_normal([128], stddev=stddev)), 'd1': tf.
    Variable(tf.random_normal([n_classes], stddev=stddev))}
print('网络定义完毕')


def CNN(_x, _w, _b):
    _x_r = tf.reshape(_x, shape=[-1, imgsize[0], imgsize[1], nimgch])
    _conv1 = tf.nn.conv2d(_x_r, _w['c1'], strides=[1, 1, 1, 1], padding='SAME')
    _conv2 = tf.nn.bias_add(_conv1, _b['c1'])
    _conv3 = tf.nn.relu(_conv2)
    _pool = tf.nn.max_pool(_conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
        padding='SAME')
    temp = tf.nn.conv2d(_pool, _w['c2'], strides=[1, 1, 1, 1], padding='SAME')
    temp = tf.nn.bias_add(temp, _b['c2'])
    temp = tf.nn.relu(temp)
    temp = tf.nn.max_pool(temp, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
        padding='SAME')
    _dense = tf.reshape(temp, [-1, _w['d1'].get_shape().as_list()[0]])
    _logit = tf.add(tf.matmul(_dense, _w['d1']), _b['d1'])
    _out = {'x_r': _x_r, 'conv1': _conv1, 'conv2': _conv2, 'conv3': _conv3,
        'pool': _pool, 'dense': _dense, 'logit': _logit}
    return _out


cnnout = CNN(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,
    logits=cnnout['logit']))
optm = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
corr = tf.equal(tf.argmax(cnnout['logit'], 1), tf.argmax(y, 1))
accr = tf.reduce_mean(tf.cast(corr, 'float'))
init = tf.global_variables_initializer()
print('模型函数定义完毕')
training_epochs = 50
batch_size = 100
disp_each = 10
plot_each = 10
sess = tf.Session()
sess.run(init)
for epoch in range(training_epochs):
    total_batch = int(ntrain / batch_size)
    randpermlist = np.random.permutation(ntrain)
    sun_cost = 0.0
    for i in range(total_batch):
        randidx = randpermlist[i * batch_size:min((i + 1) * batch_size, 
            ntrain - 1)]
        batch_xs = trainimg[randidx, :]
        batch_ys = trainlabel[randidx, :]
        feeds = {x: batch_xs, y: batch_ys}
        sess.run(optm, feed_dict=feeds)
        sun_cost += sess.run(cost, feed_dict=feeds)
    avg_cost = sun_cost / total_batch
    if (epoch + 1) % disp_each == 0 or epoch == training_epochs - 1:
        print('Epoch: %03d/%03d cost: %.9f' % (epoch + 1, training_epochs,
            avg_cost))
        feeds = {x: batch_xs, y: batch_ys}
        train_acc = sess.run(accr, feed_dict=feeds)
        print(' TRAIN ACCURACY: %.3f' % train_acc)
        feeds = {x: testimg, y: testlabel}
        test_acc = sess.run(accr, feed_dict=feeds)
        print(' TEST ACCURACY: %.3f' % test_acc)
    if (epoch + 1) % plot_each == 0 or epoch == training_epochs - 1:
        ntemp = int(ntest / batch_size) + 1
        missidxlist = []
        for ii in range(ntemp):
            iii = np.arange(ii * batch_size, min(ntest, (ii + 1) * batch_size))
            feeds = {x: testimg[iii, :], y: testlabel[iii, :]}
            missidx = np.where(sess.run(corr, feed_dict=feeds) == 0)[0]
            missidxlist = np.concatenate((missidxlist, iii[missidx]))
        nmiss = len(missidxlist)
        print(' %d MISSED' % nmiss)
        missidxlist = np.random.permutation(missidxlist.astype(int))
        selmissidx = missidxlist[:min(3, nmiss)].astype(int)
        feeds_miss = {x: testimg[selmissidx, :], y: testlabel[selmissidx, :]}
        pred_miss = sess.run(cnnout['logit'], feed_dict=feeds_miss)
        answ_miss = np.argmax(pred_miss, 1)
        answ_corr = np.argmax(testlabel[selmissidx, :], 1)
        for idx in range(len(selmissidx)):
            cidx = selmissidx[idx]
            anw = answ_miss[idx]
            ref = answ_corr[idx]
            if use_gray != 0:
                currimg = np.reshape(testimg[cidx, :], (imgsize[0], imgsize[1])
                    )
                plt.imshow(currimg, cmap='gray')
            else:
                currimg = np.reshape(testimg[cidx, :], (imgsize[0], imgsize
                    [1], -1))
                plt.imshow(currimg)
            anw_category = categories[anw]
            ref_category = categories[ref]
            title_string = 'IDX: %d CURR ANWR: [%s] TRUE LABEL: [%s]' % (cidx,
                anw_category, ref_category)
            plt.title(title_string)
            plt.show()
print('优化完毕')


# coding: utf-8

# # 自定义数据集上的CNN训练

# In[1]:

import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
get_ipython().magic(u'matplotlib inline')
print ("当前TensorFlow版本为 [%s]" % (tf.__version__))
print ("所有包装载完毕")


# ## 数据载入
# ### 此处custom_data.npz为basic_gendataset.ipynb文件生成

# In[2]:

# LOAD DATA
cwd = os.getcwd()
loadpath = cwd + "/data/custom_data.npz"
l = np.load(loadpath)
print (l.files)

# PARSE LOADED DATA
trainimg   = l['trainimg']
trainlabel = l['trainlabel']
testimg    = l['testimg']
testlabel  = l['testlabel']
imgsize    = l['imgsize']
categories = l['categories']
use_gray   = l['use_gray']
if use_gray:
    nimgch = 1
else:
    nimgch = 3
ntrain = trainimg.shape[0]
nclass = trainlabel.shape[1]
dim    = trainimg.shape[1]
ntest  = testimg.shape[0]
print ("%d TRAIN IMAGES" % (ntrain))
print ("%d TEST IMAGES" % (ntest))
print ("%d DIMENSIONAL INPUT" % (dim))
print ("%d CLASSES" % (nclass))
print (imgsize)
print (categories)


# ## 定义网络

# In[3]:

# NETWORK TOPOLOGIES
n_input    = dim
n_channel  = 64 
n_classes  = nclass  

# INPUTS AND OUTPUTS
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
    
# NETWORK PARAMETERS
stddev = 0.1
weights = {
    'c1': tf.Variable(tf.random_normal([7, 7, nimgch, n_channel], stddev=stddev)),
    'c2': tf.Variable(tf.random_normal([7, 7, n_channel, 128], stddev=stddev)),
    'd1': tf.Variable(tf.random_normal([dim//16*128, n_classes], stddev=stddev))
}
biases = {
    'c1': tf.Variable(tf.random_normal([n_channel], stddev=stddev)),
    'c2': tf.Variable(tf.random_normal([128], stddev=stddev)),
    'd1': tf.Variable(tf.random_normal([n_classes], stddev=stddev))
}
print ("网络定义完毕")


# ## 定义模型

# In[4]:

# MODEL
def CNN(_x, _w, _b):
    # RESHAPE
    _x_r = tf.reshape(_x, shape=[-1, imgsize[0], imgsize[1], nimgch])
    # CONVOLUTION
    _conv1 = tf.nn.conv2d(_x_r, _w['c1'], strides=[1, 1, 1, 1], padding='SAME')
    # ADD BIAS
    _conv2 = tf.nn.bias_add(_conv1, _b['c1'])
    # RELU
    _conv3 = tf.nn.relu(_conv2)
    # MAX-POOL
    _pool  = tf.nn.max_pool(_conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    
    temp = tf.nn.conv2d(_pool, _w['c2'], strides=[1, 1, 1, 1], padding='SAME')
    temp = tf.nn.bias_add(temp, _b['c2'])
    temp = tf.nn.relu(temp)
    temp = tf.nn.max_pool(temp, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    
    # VECTORIZE
    _dense = tf.reshape(temp, [-1, _w['d1'].get_shape().as_list()[0]])
    # DENSE
    _logit = tf.add(tf.matmul(_dense, _w['d1']), _b['d1'])
    _out = {
        'x_r': _x_r, 'conv1': _conv1, 'conv2': _conv2, 'conv3': _conv3
        , 'pool': _pool, 'dense': _dense, 'logit': _logit
    }
    return _out

# PREDICTION
cnnout = CNN(x, weights, biases)

# LOSS AND OPTIMIZER
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
        labels=y, logits=cnnout['logit']))
optm = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost) 
corr = tf.equal(tf.argmax(cnnout['logit'], 1), tf.argmax(y, 1))    
accr = tf.reduce_mean(tf.cast(corr, "float"))

# INITIALIZER
init = tf.global_variables_initializer()
print ("模型函数定义完毕")


# ## 运行

# In[5]:

# PARAMETERS
training_epochs = 50
batch_size      = 100
disp_each       = 10
plot_each       = 10
# LAUNCH THE GRAPH
sess = tf.Session()
sess.run(init)
# OPTIMIZE
for epoch in range(training_epochs):
    total_batch = int(ntrain/batch_size)
    randpermlist = np.random.permutation(ntrain)
    sun_cost = 0.
    for i in range(total_batch):
        randidx  = randpermlist[i*batch_size:min((i+1)*batch_size, ntrain-1)]
        batch_xs = trainimg[randidx, :]
        batch_ys = trainlabel[randidx, :]                
        feeds = {x: batch_xs, y: batch_ys}
        sess.run(optm, feed_dict=feeds)
        sun_cost += sess.run(cost, feed_dict=feeds)
    avg_cost = sun_cost / total_batch
        
    # DISPLAY
    if (epoch+1) % disp_each == 0 or epoch == training_epochs-1:
        print ("Epoch: %03d/%03d cost: %.9f" % (epoch+1, training_epochs, avg_cost))
        feeds = {x: batch_xs, y: batch_ys}
        train_acc = sess.run(accr, feed_dict=feeds)
        print (" TRAIN ACCURACY: %.3f" % (train_acc))
        feeds = {x: testimg, y: testlabel}
        test_acc = sess.run(accr, feed_dict=feeds)
        print (" TEST ACCURACY: %.3f" % (test_acc))
        
    # PLOT
    if (epoch+1) % plot_each == 0 or epoch == training_epochs-1:
        ntemp = int(ntest/batch_size) + 1 
        missidxlist = []
        for ii in range(ntemp):
            iii = np.arange(ii*batch_size, min(ntest, (ii+1)*batch_size))
            feeds = {x: testimg[iii, :], y: testlabel[iii, :]}
            missidx = np.where(sess.run(corr, feed_dict=feeds) == 0)[0]
            missidxlist = np.concatenate((missidxlist, iii[missidx]))
        nmiss = len(missidxlist)
        print (" %d MISSED" % (nmiss))
        # PLOT MISSCLASSIFIED (UPTO THREE)
        missidxlist = np.random.permutation(missidxlist.astype(int))
        selmissidx  = missidxlist[:min(3, nmiss)].astype(int)
        feeds_miss  = {x: testimg[selmissidx, :], y: testlabel[selmissidx, :]}
        pred_miss   = sess.run(cnnout['logit'], feed_dict=feeds_miss)
        answ_miss   = np.argmax(pred_miss, 1)
        answ_corr   = np.argmax(testlabel[selmissidx, :], 1)
        for idx in range(len(selmissidx)):
            cidx = selmissidx[idx]
            anw  = answ_miss[idx]
            ref  = answ_corr[idx]
            if use_gray != 0:
                currimg = np.reshape(testimg[cidx, :], (imgsize[0], imgsize[1]))
                plt.imshow(currimg, cmap='gray')
            else:
                currimg = np.reshape(testimg[cidx, :], (imgsize[0], imgsize[1], -1))
                plt.imshow(currimg)
            anw_category = categories[anw]
            ref_category = categories[ref]
            title_string = ("IDX: %d CURR ANWR: [%s] TRUE LABEL: [%s]" 
                % (cidx, anw_category, ref_category))
            plt.title(title_string) 
            plt.show() 
print ("优化完毕")

