path = file_path.predicted_value
outputer.setPredictedValueFilePath(path)
path = file_path.output_dir
outputer.setSummaryDirPath(path)
#######################################################
# each data settings
#######################################################
from tensorflow.examples.tutorials.mnist import input_data
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
kstd.echoBar()
kstd.echoBlank()
print("data attr")
kstd.echoBlank()
print(type(x_train)) # numpy.ndarray
print(type(y_train)) # numpy.ndarray
print(type(x_test)) # numpy.ndarray
print(type(y_test)) # numpy.ndarray
print(x_train.shape) # (60000,28,28)
print(y_train.shape) # (60000,)
print(x_test.shape) # (10000,28,28)
print(y_test.shape) # (10000,)
print(np.max(x_train)) # 255
print(np.min(x_train)) # 0
print(np.max(y_train)) # 9
print(np.min(y_train)) # 0
kstd.echoBlank()
def valCheck(self):
kstd.echoBlank()
print("image height : " + str(self.height) )
print("image wigth : " + str(self.wigth) )
def echoProcessTo(str):
kstd.echoBlank()
print("process to " + str)