[PYTHON/TENSORFLOW] 컨볼루션 신경망 만들기 (MNIST)

■ 컨볼루션 신경망을 만드는 방법을 보여준다.

▶ 예제 코드 (PY)


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

imageSize            = 28
batchSize            = 100
outputLayerNodeCount = 10
learningRate         = 0.001
epochCount           = 10
dropoutRate          = 0.8

mnistDatasets = mnist.input_data.read_data_sets("data", one_hot = True)

inputLayerTensor    = tf.placeholder(tf.float32, [None, imageSize, imageSize, 1])
correctOutputTensor = tf.placeholder(tf.float32, [None, outputLayerNodeCount])
dropoutRateTensor   = tf.placeholder(tf.float32)

convolutionLayer1WeightVariable   = tf.Variable(tf.random_normal([4, 4, 1, 16              ], stddev = 0.01))
convolutionLayer2WeightVariable   = tf.Variable(tf.random_normal([4, 4, 16, 32             ], stddev = 0.01))
fullyConnectedLayerWeightVariable = tf.Variable(tf.random_normal([7 * 7 * 32, 256          ], stddev = 0.01))
outputLayerWeightVariable         = tf.Variable(tf.random_normal([256, outputLayerNodeCount], stddev = 0.01))

convolutionLayer1OutputTensor   = tf.nn.conv2d(inputLayerTensor, convolutionLayer1WeightVariable, strides = [1, 1, 1, 1], padding = "SAME")
convolutionLayer1OutputTensor   = tf.nn.relu(convolutionLayer1OutputTensor)
convolutionLayer1OutputTensor   = tf.nn.max_pool(convolutionLayer1OutputTensor, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = "SAME")
convolutionLayer2OutputTensor   = tf.nn.conv2d(convolutionLayer1OutputTensor, convolutionLayer2WeightVariable, strides = [1, 1, 1, 1], padding = "SAME")
convolutionLayer2OutputTensor   = tf.nn.relu(convolutionLayer2OutputTensor)
convolutionLayer2OutputTensor   = tf.nn.max_pool(convolutionLayer2OutputTensor, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = "SAME")
fullyConnectedLayerOutputTensor = tf.reshape(convolutionLayer2OutputTensor, [-1, 7 * 7 * 32])
fullyConnectedLayerOutputTensor = tf.matmul(fullyConnectedLayerOutputTensor, fullyConnectedLayerWeightVariable)
fullyConnectedLayerOutputTensor = tf.nn.relu(fullyConnectedLayerOutputTensor)
fullyConnectedLayerOutputTensor = tf.nn.dropout(fullyConnectedLayerOutputTensor, dropoutRateTensor)
outputLayerOutputTensor         = tf.matmul(fullyConnectedLayerOutputTensor, outputLayerWeightVariable)

costTensor = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = outputLayerOutputTensor, labels = correctOutputTensor))

optimizerOperation = tf.train.AdamOptimizer(learningRate).minimize(costTensor)

with tf.Session() as session:
    session.run(tf.global_variables_initializer())
    totalBatch = int(mnistDatasets.train.num_examples / batchSize)
    for epoch in range(epochCount):
        totalCost = 0
        for batch in range(totalBatch):
            batchInputNDArray, batchCorrectOutputNDArray = mnistDatasets.train.next_batch(batchSize)
            batchInputNDArray = batchInputNDArray.reshape(-1, imageSize, imageSize, 1)
            _, cost = session.run([optimizerOperation, costTensor], feed_dict = {inputLayerTensor : batchInputNDArray, correctOutputTensor : batchCorrectOutputNDArray,\
                dropoutRateTensor : dropoutRate})
            totalCost += cost
        print("반복 : ", "%04d" % (epoch + 1), "평균 비용 : ", "{:.4f}".format(totalCost / totalBatch))
    print("학습 완료!")

    scoreTensor    = tf.equal(tf.argmax(outputLayerOutputTensor, 1), tf.argmax(correctOutputTensor, 1))
    accuracyTensor = tf.reduce_mean(tf.cast(scoreTensor, tf.float32))

    print("정확도 : ", session.run(accuracyTensor, feed_dict = {inputLayerTensor : mnistDatasets.test.images.reshape(-1, imageSize, imageSize, 1),\
        correctOutputTensor : mnistDatasets.test.labels, dropoutRateTensor : 1}))

import tensorflow as tf

import tensorflow.examples.tutorials.mnist as mnist

imageSize = 28

batchSize = 100

outputLayerNodeCount = 10

learningRate = 0.001

epochCount = 10

dropoutRate = 0.8

mnistDatasets = mnist.input_data.read_data_sets("data", one_hot = True)

inputLayerTensor = tf.placeholder(tf.float32, [None, imageSize, imageSize, 1])

correctOutputTensor = tf.placeholder(tf.float32, [None, outputLayerNodeCount])

dropoutRateTensor = tf.placeholder(tf.float32)

convolutionLayer1WeightVariable = tf.Variable(tf.random_normal([4, 4, 1, 16 ], stddev = 0.01))

convolutionLayer2WeightVariable = tf.Variable(tf.random_normal([4, 4, 16, 32 ], stddev = 0.01))

fullyConnectedLayerWeightVariable = tf.Variable(tf.random_normal([7 * 7 * 32, 256 ], stddev = 0.01))

outputLayerWeightVariable = tf.Variable(tf.random_normal([256, outputLayerNodeCount], stddev = 0.01))

convolutionLayer1OutputTensor = tf.nn.conv2d(inputLayerTensor, convolutionLayer1WeightVariable, strides = [1, 1, 1, 1], padding = "SAME")

convolutionLayer1OutputTensor = tf.nn.relu(convolutionLayer1OutputTensor)

convolutionLayer1OutputTensor = tf.nn.max_pool(convolutionLayer1OutputTensor, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = "SAME")

convolutionLayer2OutputTensor = tf.nn.conv2d(convolutionLayer1OutputTensor, convolutionLayer2WeightVariable, strides = [1, 1, 1, 1], padding = "SAME")

convolutionLayer2OutputTensor = tf.nn.relu(convolutionLayer2OutputTensor)

convolutionLayer2OutputTensor = tf.nn.max_pool(convolutionLayer2OutputTensor, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = "SAME")

fullyConnectedLayerOutputTensor = tf.reshape(convolutionLayer2OutputTensor, [-1, 7 * 7 * 32])

fullyConnectedLayerOutputTensor = tf.matmul(fullyConnectedLayerOutputTensor, fullyConnectedLayerWeightVariable)

fullyConnectedLayerOutputTensor = tf.nn.relu(fullyConnectedLayerOutputTensor)

fullyConnectedLayerOutputTensor = tf.nn.dropout(fullyConnectedLayerOutputTensor, dropoutRateTensor)

outputLayerOutputTensor = tf.matmul(fullyConnectedLayerOutputTensor, outputLayerWeightVariable)

costTensor = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = outputLayerOutputTensor, labels = correctOutputTensor))

optimizerOperation = tf.train.AdamOptimizer(learningRate).minimize(costTensor)

with tf.Session() as session:

session.run(tf.global_variables_initializer())

totalBatch = int(mnistDatasets.train.num_examples / batchSize)

for epoch in range(epochCount):

totalCost = 0

for batch in range(totalBatch):

batchInputNDArray, batchCorrectOutputNDArray = mnistDatasets.train.next_batch(batchSize)

batchInputNDArray = batchInputNDArray.reshape(-1, imageSize, imageSize, 1)

_, cost = session.run([optimizerOperation, costTensor], feed_dict = {inputLayerTensor : batchInputNDArray, correctOutputTensor : batchCorrectOutputNDArray,\

dropoutRateTensor : dropoutRate})

totalCost += cost

print("반복 : ", "%04d" % (epoch + 1), "평균 비용 : ", "{:.4f}".format(totalCost / totalBatch))

print("학습 완료!")

scoreTensor = tf.equal(tf.argmax(outputLayerOutputTensor, 1), tf.argmax(correctOutputTensor, 1))

accuracyTensor = tf.reduce_mean(tf.cast(scoreTensor, tf.float32))

print("정확도 : ", session.run(accuracyTensor, feed_dict = {inputLayerTensor : mnistDatasets.test.images.reshape(-1, imageSize, imageSize, 1),\

correctOutputTensor : mnistDatasets.test.labels, dropoutRateTensor : 1}))