PythonMachineLearningExamples/n0_network.py at master · rrlyman/PythonMachineLearningExamples · GitHub

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#import tensorflow as tf
fromtensorflow.compatimportv1astf
tf.compat.v1.disable_eager_execution()
importnumpyasnp
fromcollectionsimportnamedtuple
importdatetime
importocr_utils

classbase_network(object):
''' definition of the network
 '''


deffit(self, truthed_data, nEpochs=5000):

perfect_count=10
foriinrange(nEpochs):

batch=truthed_data.next_batch(100)
# assign feature data to each placeholder
# the batch list is returned in the same order as the features requested
feed= {self._keep_prob: 0.5}
forjinrange(truthed_data.num_features):
feed[self._ph[j]] =batch[j]

ifi%100==0:

feed[self._keep_prob] =1.0
result=self._sess.run([self._merged, self._accuracy ], feed_dict=feed)
summary_str=result[0]

self._writer.add_summary(summary_str, i)
train_accuracy=result[1]
iftrain_accuracy<= (1.0-1e-5 ):
perfect_count=10;
else:
perfect_count-=1
ifperfect_count==0:
break;

print ("step %d, training accuracy %g"%(i, train_accuracy),flush=True)
self._sess.run(self._train_step,feed_dict=feed)


deftest(self, truthed_data, title=''):

# assign feature data to each placeholder
error_images=np.empty((0,self._nRows,self._nCols))

test_accuracy=0
m=0

foriinrange(int(len(truthed_data.features[0])/100)):

batch=truthed_data.next_batch(100)
# assign feature data to each placeholder
# the batch list is returned in the same order as the features requested
feed= {self._keep_prob: 1.0}
forjinrange(truthed_data.num_features):
feed[self._ph[j]] =batch[j]


result=self._sess.run([self._accuracy, self._x_image, self._correct_prediction], feed_dict=feed)

test_accuracy+=result[0]
error_images=np.append(error_images, result[1][:,:,:,0][result[2]==False],axis=0)
m+=1
try:
print ("test accuracy {} for : {}".format(test_accuracy/m, title),flush=True)
ocr_utils.montage(error_images,title='TensorFlow {} Error Images'.format(title))
except:
ifm==0:
print ("test accuracy 1",flush=True)
else:
print ("test accuracy {}".format(test_accuracy/m),flush=True)
ocr_utils.montage(error_images,title='TensorFlow Error Images')


defpredict(self, truthed_features):
feed={self._keep_prob: 1.0}
# assign feature data to each placeholder
error_images=np.empty((truthed_features.num_rows,truthed_features.num_columns))

test_accuracy=0
m=0

forjinrange(1,truthed_features.num_features):
feed[self._ph[j]] =truthed_features.features[j]
result=self._sess.run([self._prediction], feed_dict=feed)

returnresult[0]