text: from tensorflow.python.keras.utils.np_utils import to_categorical
  
  from net import  efficientnet
  import tensorflow as tf
  import tensorflow.keras as keras
  import cv2
  import numpy as np
  
  def load_mnist(image_size):
      (x_train,y_train),(x_test,y_test) = keras.datasets.mnist.load_data()
      train_image = [cv2.cvtColor(cv2.resize(img,(image_size,image_size)),cv2.COLOR_GRAY2BGR) for img in x_train]
      test_image = [cv2.cvtColor(cv2.resize(img,(image_size,image_size)),cv2.COLOR_GRAY2BGR) for img in x_test]
      train_image = np.asarray(train_image)
      test_image = np.asarray(test_image)
      # train_label = to_categorical(y_train)
      # test_label = to_categorical(y_test)
      print('finish loading data!')
      return train_image, y_train, test_image, y_test
  
  x, y, x_test, y_test = load_mnist(56)
  
  
  # 类别onehot编码
  from sklearn.preprocessing import LabelBinarizer
  encoder = LabelBinarizer()
  y = encoder.fit_transform(y)
  y_test = encoder.fit_transform(y_test)
  print(y)
  # x = x.reshape(-1, 28, 28, 1)
  # x_test = x_test.reshape(-1, 28, 28, 1)
  print(y.shape, y_test.shape)
  model = keras.applications.densenet.DenseNet121(weights=None, classes=10, input_shape=(56, 56, 3))
  model.summary()
  optimizer = tf.keras.optimizers.Adam(0.001)
  model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
  log_dir = ""
  # 使用命令tensorboard --logdir=. 即可查看tensorboard
  tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir,)
  history = model.fit(x, y, epochs=10, batch_size=8,
                      validation_data=(x_test, y_test), callbacks=[tensorboard_callback])
  print(history)