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()