Tensorflow 2.0.0版。张力板教程不起作用。发出警告=>无列车图，仅用于验证

Question

我遵循张力板教程，它不工作。。。你知道吗

教程在这里：https://www.tensorflow.org/tensorboard/get_started

当我安装模型时，会出现以下警告： 警告：tensorflow：与批量更新（0.168042）相比，方法（在\u列\u批处理\u端）比较慢。检查您的回电。

只有在我使用tf.keras.回调.TensorBoard（用于记录培训）。你知道吗

然后，似乎张力板无法绘制如图托所示的训练图，而只能绘制验证图。你知道吗

我用TensorFlow2.0.0和TensorFlowGPU2.0.0进行了测试。同样的问题。你知道吗

C:\Users\X>conda list tensor
# packages in environment at C:\Users\X\Miniconda3:
#
# Name                    Version                   Build  Channel
tensorboard               2.0.0              pyhb38c66f_1
tensorflow                2.0.0           mkl_py37he1bbcac_0
tensorflow-base           2.0.0           mkl_py37hd1d5974_0
tensorflow-estimator      2.0.0              pyh2649769_0

(tf-gpu) >conda list tensor
# packages in environment at C:\Users\X\Miniconda3\envs\tf-gpu:
#
# Name                    Version                   Build  Channel
tensorboard               2.0.0              pyhb38c66f_1
tensorflow                2.0.0           gpu_py37h57d29ca_0
tensorflow-base           2.0.0           gpu_py37h390e234_0
tensorflow-estimator      2.0.0              pyh2649769_0
tensorflow-gpu            2.0.0                h0d30ee6_0

使用的代码：

mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

def create_model():
  return tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation='softmax')
  ])

model = create_model()
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# only change vs tutorial, because Windows
# log_dir="./logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
log_dir="logs\\fit\\" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, 
                                                      histogram_freq=1)

model.fit(x=x_train, 
          y=y_train, 
          epochs=5, 
          validation_data=(x_test, y_test), 
          callbacks=[tensorboard_callback])

Train on 60000 samples, validate on 10000 samples
Epoch 1/5
   32/60000 [..............................] - ETA: 22:46 - loss: 2.4036 - accuracy: 0.0625

WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.168042). Check your callbacks.

60000/60000 [==============================] - 9s 144us/sample - loss: 0.2177 - accuracy: 0.9348 - val_loss: 0.1144 - val_accuracy: 0.9650

Epoch 2/5
60000/60000 [==============================] - 7s 122us/sample - loss: 0.0961 - accuracy: 0.9707 - val_loss: 0.0727 - val_accuracy: 0.9776

Epoch 3/5
60000/60000 [==============================] - 8s 125us/sample - loss: 0.0677 - accuracy: 0.9785 - val_loss: 0.0744 - val_accuracy: 0.9755

Epoch 4/5
60000/60000 [==============================] - 7s 121us/sample - loss: 0.0529 - accuracy: 0.9830 - val_loss: 0.0746 - val_accuracy: 0.9763

Epoch 5/5
60000/60000 [==============================] - 7s 123us/sample - loss: 0.0441 - accuracy: 0.9859 - val_loss: 0.0675 - val_accuracy: 0.9815

<tensorflow.python.keras.callbacks.History at 0x15f0ff63548>

你知道吗？你知道吗

谢谢！你知道吗