def __init__(self):
'Train Initialized'
tfHelper.log_level_decrease()
# self.k.initializers.Ones()
self.c.allOutput = tfHelper.get_all_allpout(self.folderPath)
# self.k.initializers.RandomUniform(minval=0.7, maxval=1, seed=None)
tfHelper.numpy_show_entire_array(self.c.imgWidth)
import tensorflow as tf
import numpy as np
# np.set_printoptions(linewidth=200)
from tfHelper import tfHelper
import data
k = tf.keras
tfHelper.log_level_decrease()
batch_size = 64
num_classes = 10
epochs = 100
imgWidth = 100
print("Load data ...")
# (x_train, y_train), (x_test, y_test) = data.load_data_train()
(x_train, y_train) = tfHelper.get_dataset_with_folder('classed/', 'L')
# print (x_train)
# print (y_train)
# X_pred, X_id, label = data.load_data_predict()
split = int(len(x_train) * 0.2)
x_test = x_train[:split]
x_train = x_train[split:]
y_test = y_train[:split]
y_train = y_train[split:]
def __init__(self):
'Train Initialized'
tfHelper.log_level_decrease()
self.k.initializers.Ones()
def __init__(self): 'tfHelper Initialized' tfHelper.log_level_decrease()