Category: Keras Faq’s

Making a RNN stateful means that the states for the samples of each batch will be reused as initial states for the samples in the next batch. When using stateful RNNs, it is therefore assumed that: To use statefulness in RNNs, you need to: To reset the states accumulated: Example: Note that the methods predict, fit, train_on_batch, etc.…

How can I obtain the output of an intermediate layer (feature extraction)? In the Functional API and Sequential API, if a layer has been called exactly once, you can retrieve its output via layer.output and its input via layer.input. This enables you do quickly instantiate feature-extraction models, like this one: Naturally, this is not possible with models that…

Let’s answer with an extract from Deep Learning with Python, Second Edition: Both y = model.predict(x) and y = model(x) (where x is an array of input data) mean “run the model on x and retrieve the output y.” Yet they aren’t exactly the same thing. predict() loops over the data in batches (in fact, you can specify the batch size via predict(x, batch_size=64)), and it extracts…

You have two options: 1) Subclass the Model class and override the train_step (and test_step) methods This is a better option if you want to use custom update rules but still want to leverage the functionality provided by fit(), such as callbacks, efficient step fusing, etc. Note that this pattern does not prevent you from building models with the Functional API,…

Loss values and metric values are reported via the default progress bar displayed by calls to fit(). However, staring at changing ascii numbers in a console is not an optimal metric-monitoring experience. We recommend the use of TensorBoard, which will display nice-looking graphs of your training and validation metrics, regularly updated during training, which you can access…

If you pass your data as NumPy arrays and if the shuffle argument in model.fit() is set to True (which is the default), the training data will be globally randomly shuffled at each epoch. If you pass your data as a tf.data.Dataset object and if the shuffle argument in model.fit() is set to True, the dataset will be locally shuffled (buffered shuffling). When using tf.data.Dataset objects, prefer shuffling your data…

If you set the validation_split argument in model.fit to e.g. 0.1, then the validation data used will be the last 10% of the data. If you set it to 0.25, it will be the last 25% of the data, etc. Note that the data isn’t shuffled before extracting the validation split, so the validation is literally just the last x% of samples in…

training is a boolean argument in call that determines whether the call should be run in inference mode or training mode. For example, in training mode, a Dropout layer applies random dropout and rescales the output. In inference mode, the same layer does nothing. Example: trainable is a boolean layer attribute that determines the trainable weights of the layer should be…

Setting the trainable attribute All layers & models have a layer.trainable boolean attribute: On all layers & models, the trainable attribute can be set (to True or False). When set to False, the layer.trainable_weights attribute is empty: Setting the trainable attribute on a layer recursively sets it on all children layers (contents of self.layers). 1) When training with fit(): To do fine-tuning with fit(), you would: Like this: You…

You can use an EarlyStopping callback: