checkpointing

I'll run some larger models and want to try intermediate results. Therefore, I try to use checkpoints to save the best model after each epoch. This is my code: model = Sequential() model.add(LSTM(700, input_shape=(X_modified.shape[1], X_modified.shape[2]), return_sequences=True)) model.add(Dropout(0.2)) model.add(LSTM(700, return_sequences=True)) model.add(Dropout(0.2)) model.add(LSTM(700)) model.add(Dropout(0.2)) model.add(Dense(Y_modified.shape[1], activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # Save the checkpoint in the

I am using pre-trained VGG-16 model for image classification. I am adding custom last layer as the number of my classification classes are 10. I am training the model for 200 epochs. My question is: is there any way if I randomly stop (by closing python window) the training at some epoch, let's say epoch no. 50 and resume from there? I have read about saving and reloading model but my understanding is that works for our custom models only instead of pre-trained models like VGG-16. You can use ModelCheckpoint callback to save your model regularly. To use it, pass a callbacks parameter to the

I am building docker from this version of this source code: https://github.com/boucher/docker/tree/cr-combined after cloning the code : git clone -b cr-combined --single-branch https://github.com/boucher/docker.git cd docker #make build #make binary And then copied the resulting file @./bundles/../docker to the usr/bin directory After reopening the terminal and starting the docker engine again. its shows that i am using my own built version but This version should have two main docker commands that won't show up in my built one 1- checkpoint 2- restore could you please help me and tell me