predict_proba

(Deprecated) Generates probability or class probability predictions for the input samples.

These functions were removed in Tensorflow version 2.6. See details for how to update your code:

 
predict_proba(object, x, batch_size = NULL, verbose = 0, steps = NULL) 
 
predict_classes(object, x, batch_size = NULL, verbose = 0, steps = NULL)

Arguments	Description
object	Keras model object
x	Input data (vector, matrix, or array). You can also pass a `tfdataset` or a generator returning a list with `(inputs, targets)` or `(inputs, targets, sample_weights)`.
batch_size	Integer. If unspecified, it will default to 32.
verbose	Verbosity mode, 0, 1, 2, or “auto”. “auto” defaults to 1 for for most cases and defaults to `verbose=2` when used with ParameterServerStrategy or with interactive logging disabled.
steps	Total number of steps (batches of samples) before declaring the evaluation round finished. The default `NULL` is equal to the number of samples in your dataset divided by the batch size.

How to update your code: predict_proba(): use predict() directly. predict_classes():

If your model does multi-class classification: (e.g. if it uses a softmax last-layer activation).

```

model %>% predict(x) %>% k_argmax()


- if your model does binary classification (e.g. if it uses a `sigmoid` last-layer activation).

 model %>% predict(x) %>% `>`(0.5) %>% k_cast("int32")

``` The input samples are processed batch by batch.