def cnnExecuter(mode, dto_data_set, outputer):
#####################################################
# variables
#####################################################
kstd.echoStart(mode)
kstd.echoBlanks(2)
x_size = dto_data_set.pixel_size
x_1d = tf.placeholder(tf.float32, shape=[None, x_size])
y_size = dto_data_set.num_of_label_kind
y_ans = tf.placeholder(tf.float32, shape=[None, y_size])
if mode == MODE_LEARNING:
cnnLearning(dto_data_set, outputer, x_1d, y_ans)
elif mode == MODE_RE_LEARNING:
cnnReLearning(dto_data_set, outputer, x_1d, y_ans)
if mode == MODE_VALIDATION:
cnnValidation(dto_data_set, outputer, x_1d, y_ans)
if mode == MODE_PREDICTION:
cnnPrediction(dto_data_set, outputer, x_1d, y_ans)
kstd.echoBlanks(2)
kstd.echoIsAlready(mode)
def cnnPrediction(dto_data_set, outputer, x_1d, y_ans):
process = "cnnPrediction"
kstd.echoStart(process)
kstd.echoBlanks(2)
x_2d = tf.reshape(x_1d, [-1, dto_data_set.wigth, dto_data_set.height, 1])
#####################################################
# model learning
#####################################################
y_cnn, y_acc = __cnnModel(x_2d, False)
with tf.Session() as sess:
saver = tf.train.Saver()
saver.restore(sess, outputer.learned_parameter_file_path)
x_target = dto_data_set.dtoNT_flat_img.getVariable()
y_predicted = y_cnn.eval(feed_dict={x_1d: x_target})
dtoNT = kstd.DtoNpTable(dto_data_set.num_of_label_kind)
dtoNT.addNpArray(y_predicted)
dto_data_set.addValueTable(dtoNT)
cnn.crateNLLabelFromValue(dto_data_set)
outputer.PredictionResultSave(dto_data_set)
sess.close()
def cnnValidation(dto_data_set, outputer, x_1d, y_ans):
process = "cnnValidation"
kstd.echoStart(process)
kstd.echoBlanks(2)
x_2d = tf.reshape(x_1d, [-1, dto_data_set.wigth, dto_data_set.height, 1])
#####################################################
# model learning
#####################################################
y_cnn, y_acc = __cnnModel(x_2d, False)
train_step, cross_entropy, accuracy = __cnnTrainingUnit(
y_ans, y_cnn, y_acc)
with tf.Session() as sess:
#sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, outputer.learned_parameter_file_path)
valid_x = dto_data_set.dtoNT_flat_img.getVariable()
valid_y = dto_data_set.dtoNT_label.getVariable()
train_accuracy = accuracy.eval(feed_dict={
x_1d: valid_x,
y_ans: valid_y
})
print("validation : %0.2f" % train_accuracy)
sess.close()
outputer.validationOutput(train_accuracy)
def cnnReLearning(dto_data_set, outputer, x_1d, y_ans):
process = "cnnReLearning"
kstd.echoStart(process)
kstd.echoBlanks(2)
x_2d = tf.reshape(x_1d, [-1, dto_data_set.wigth, dto_data_set.height, 1])
#####################################################
# model learning
#####################################################
y_cnn, y_acc = __cnnModel(x_2d, True)
train_step, cross_entropy, accuracy = __cnnTrainingUnit(
y_ans, y_cnn, y_acc)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, outputer.learned_parameter_file_path)
__cnnLearningUnit(dto_data_set, outputer, x_1d, y_ans, train_step,
cross_entropy, accuracy)
saver.save(sess, outputer.learned_parameter_file_path)
sess.close()
def __cnnLearningUnit(dto_data_set, outputer, x_1d, y_ans, train_step,
cross_entropy, accuracy):
process = "learning unit"
kstd.echoBlanks(5)
kstd.echoStart(process)
timer = kstd.timeCalculater()
dto_data_set.fixDataSet()
for ii in range(prop.LEARNING_ITERATION):
batch_x, batch_y = dto_data_set.getBatchSet(prop.BATCH_SIZE)
train_step.run(feed_dict={x_1d: batch_x, y_ans: batch_y})
if (ii + 1) % prop.CYCLE_LOG_OUTPUT == 0:
train_accuracy = accuracy.eval(feed_dict={
x_1d: batch_x,
y_ans: batch_y
})
train_entropy = cross_entropy.eval(feed_dict={
x_1d: batch_x,
y_ans: batch_y
})
outputer.logOutput(
getProgressMessage(ii, train_accuracy, train_entropy, timer))
timer.lap()
#if (ii + 1 ) % prop.CYCLE_PARA_OUTPUT == 0:
# saver.save(sess, outputer.learned_parameter_file_path)
kstd.echoFinish(process)
if mode == MODE_VALIDATION:
cnnValidation(dto_data_set, outputer, x_1d, y_ans)
if mode == MODE_PREDICTION:
cnnPrediction(dto_data_set, outputer, x_1d, y_ans)
kstd.echoBlanks(2)
kstd.echoIsAlready(mode)
if __name__ == "__main__":
argvs = sys.argv # コマンドライン引数を格納したリストの取得
argc = len(argvs) # 引数の個数
if not argc > 0:
kstd.echoBlanks(2)
kstd.echoBar()
print("input 1:Learing 2:re-Learning 3:Validation 4:Prediction")
elif argc > 1:
outputer = cnn.OutputForTFCNN()
case = 0
file_path = fi.filePath(case)
path = file_path.learned_param
outputer.setLearnedParameterFilePath(path)
path = file_path.predicted_value
outputer.setPredictedValueFilePath(path)