Training fails if model is saved beforehand-CodePudding

I noticed that saving a Tensorflow model prior to training causes the training to perform poorly. Obviously the solution is to just save the model later, but I'm curious why this is happening in the first place.

The following code runs fine, and produces the following output:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

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


model = tf.keras.Sequential([
    layers.Flatten(input_shape=(28,28)),
    layers.Dense(16, activation='relu'),
    layers.Dense(16, activation='relu'),
    layers.Dense(10)
])

loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer='adam',
             loss=loss_fn,
             metrics=['accuracy'])

### model.save('my_model') ###
model.fit(x_train, y_train, epochs=3, verbose=1)

Epoch 1/3
1875/1875 [==============================] - 3s 1ms/step - loss: 0.4513 - accuracy: 0.8688
Epoch 2/3
1875/1875 [==============================] - 2s 1ms/step - loss: 0.2326 - accuracy: 0.9333
Epoch 3/3
1875/1875 [==============================] - 2s 1ms/step - loss: 0.1974 - accuracy: 0.9432

However when the second last line is commented out, the training fails badly:

INFO:tensorflow:Assets written to: my_model\assets
Epoch 1/3
1875/1875 [==============================] - 3s 1ms/step - loss: 0.4156 - accuracy: 0.0948
Epoch 2/3
1875/1875 [==============================] - 2s 1ms/step - loss: 0.2149 - accuracy: 0.1000
Epoch 3/3
1875/1875 [==============================] - 2s 1ms/step - loss: 0.1840 - accuracy: 0.0998

CodePudding user response：

It's a known bug of TensorFlow. Use metrics=['sparse_categorical_accuracy'] instead of metrics=['accuracy'] when compiling the model to avoid it.