I've been trying to save and reupload a model and whenever I do that the accuracy always goes down.
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(64, kernel_size=3, activation='relu', input_shape=(IMG_SIZE,IMG_SIZE,3)))
model.add(tf.keras.layers.Conv2D(32, kernel_size=3, activation='relu'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(len(SURFACE_TYPES), activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['acc'])
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=EPOCHS,
validation_steps=10)
Output:
Epoch 1/3 84/84 [==============================] - 2s 19ms/step - loss: 1.9663 - acc: 0.6258 - val_loss: 0.8703 - val_acc: 0.6867
Epoch 2/3 84/84 [==============================] - 1s 18ms/step - loss: 0.2865 - acc: 0.9105 - val_loss: 0.4494 - val_acc: 0.8667
Epoch 3/3 84/84 [==============================] - 1s 18ms/step - loss: 0.1409 - acc: 0.9574 - val_loss: 0.3614 - val_acc: 0.9000
This followed by running these commands to produce outputs result in the same training loss but different training accuracies. The weights and structures of the models are also identical.
model.save("my_model2.h5")
model2 = load_model("my_model2.h5")
model2.evaluate(train_ds)
model.evaluate(train_ds)
Output:
84/84 [==============================] - 1s 9ms/step - loss: 0.0854 - acc: 0.0877
84/84 [==============================] - 1s 9ms/step - loss: 0.0854 - acc: 0.9862
[0.08536089956760406, 0.9861862063407898]
