Executing model.fit multiple times-CodePudding

The code and output when I execute once:

model.fit(X,y,validation_split=0.2, epochs=10, batch_size= 100)
Epoch 1/10
8/8 [==============================] - 1s 31ms/step - loss: 0.6233 - accuracy: 0.6259 - val_loss: 0.6333 - val_accuracy: 0.6461
Epoch 2/10
8/8 [==============================] - 0s 5ms/step - loss: 0.5443 - accuracy: 0.7722 - val_loss: 0.4803 - val_accuracy: 0.7978
Epoch 3/10
8/8 [==============================] - 0s 4ms/step - loss: 0.5385 - accuracy: 0.7904 - val_loss: 0.4465 - val_accuracy: 0.8202
Epoch 4/10
8/8 [==============================] - 0s 5ms/step - loss: 0.5014 - accuracy: 0.7932 - val_loss: 0.5228 - val_accuracy: 0.7753
Epoch 5/10
8/8 [==============================] - 0s 4ms/step - loss: 0.5283 - accuracy: 0.7736 - val_loss: 0.4284 - val_accuracy: 0.8315
Epoch 6/10
8/8 [==============================] - 0s 4ms/step - loss: 0.4936 - accuracy: 0.7989 - val_loss: 0.4309 - val_accuracy: 0.8539
Epoch 7/10
8/8 [==============================] - 0s 4ms/step - loss: 0.4700 - accuracy: 0.8045 - val_loss: 0.4622 - val_accuracy: 0.8146
Epoch 8/10
8/8 [==============================] - 0s 4ms/step - loss: 0.4732 - accuracy: 0.8087 - val_loss: 0.4159 - val_accuracy: 0.8202
Epoch 9/10
8/8 [==============================] - 0s 5ms/step - loss: 0.5623 - accuracy: 0.7764 - val_loss: 0.7438 - val_accuracy: 0.8090
Epoch 10/10
8/8 [==============================] - 0s 4ms/step - loss: 0.5886 - accuracy: 0.7806 - val_loss: 0.5889 - val_accuracy: 0.6798

Output when I execute the same line of code again in jupyter lab:

Epoch 1/10
8/8 [==============================] - 0s 9ms/step - loss: 0.5269 - accuracy: 0.7496 - val_loss: 0.4568 - val_accuracy: 0.8371
Epoch 2/10
8/8 [==============================] - 0s 4ms/step - loss: 0.4688 - accuracy: 0.8087 - val_loss: 0.4885 - val_accuracy: 0.7753
Epoch 3/10
8/8 [==============================] - 0s 4ms/step - loss: 0.4597 - accuracy: 0.8017 - val_loss: 0.4638 - val_accuracy: 0.7865
Epoch 4/10
8/8 [==============================] - 0s 4ms/step - loss: 0.4741 - accuracy: 0.7890 - val_loss: 0.4277 - val_accuracy: 0.8258
Epoch 5/10
8/8 [==============================] - 0s 5ms/step - loss: 0.4840 - accuracy: 0.8003 - val_loss: 0.4712 - val_accuracy: 0.7978
Epoch 6/10
8/8 [==============================] - 0s 4ms/step - loss: 0.4488 - accuracy: 0.8087 - val_loss: 0.4825 - val_accuracy: 0.7809
Epoch 7/10
8/8 [==============================] - 0s 5ms/step - loss: 0.4432 - accuracy: 0.8087 - val_loss: 0.4865 - val_accuracy: 0.8090
Epoch 8/10
8/8 [==============================] - 0s 4ms/step - loss: 0.4299 - accuracy: 0.8059 - val_loss: 0.4458 - val_accuracy: 0.8371
Epoch 9/10
8/8 [==============================] - 0s 4ms/step - loss: 0.4358 - accuracy: 0.8172 - val_loss: 0.5232 - val_accuracy: 0.8034
Epoch 10/10
8/8 [==============================] - 0s 5ms/step - loss: 0.4697 - accuracy: 0.8059 - val_loss: 0.4421 - val_accuracy: 0.8202

It continues the previous fit, and my question is: how can I make it start from the beginning again? without having to create a new model, so the second time I execute the line of code is independent of the first one

CodePudding user response：

This is a little bit tricky without being able to see the code to initialise the model, and not sure why you'd need to reset the weights without re-initialising the model.

If you save the weights of your model before training, you can then then reset to those initial weights before you train again.

modelWeights = model.get_weights()

model.set_weights(modelWeights)