def standardizeImages(self):
print "Standardizing Images..."
self.trainingDataXStandardized = []
self.testingDataXStandardized = []
with tf.Session() as sess:
for i in range(self.trainingDataX.shape[0]):
print str(i)+"/"+str(self.trainingDataX.shape[0])
self.trainingDataXStandardized.append(tf.image.per_image_standardization(self.trainingDataX[i]).eval())
for i in range(self.testingDataX.shape[0]):
print str(i)+"/"+str(self.testingDataX.shape[0])
self.testingDataXStandardized.append(tf.image.per_image_standardization(self.testingDataX[i]).eval())
#self.trainingDataX = tf.map_fn(lambda img:tf.image.per_image_standardization(img), self.trainingDataX, dtype=tf.float32)
#self.testingDataX = tf.map_fn(lambda img:tf.image.per_image_standardization(img), self.testingDataX, dtype=tf.float32)
#print self.trainingDataXStandardized[0]
self.trainingDataX = np.array(self.trainingDataXStandardized)
self.testingDataX = np.array(self.testingDataXStandardized)
print self.testingDataX.shape
print self.trainingDataX.shape
#with tf.Session() as sess:
# self.trainingDataX = self.trainingDataX.eval()
# self.testingDataX = self.testingDataX.eval()
print "Images standardized...Saving them..."
self.__save("preparedDataStandardized.pkl")
genericDataSetLoader.py 文件源码
python
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