docstring fix for fit / fit_generator
* docstring fix for fit / fit_generator
See for details.
* shortened lines.
@@ -804,7 +804,11 @@ class Sequential(Model):
(if the model has multiple inputs).
y: labels, as a Numpy array.
batch_size: integer. Number of samples per gradient update.
- epochs: integer, the number of epochs to train the model.
+ epochs: integer. Number of epochs to train the model.
+ Note that in conjunction with initial_epoch, the parameter
+ epochs is to be understood as "final epoch". The model is
+ not trained for a number of steps given by epochs, but
+ until the epoch epochs is reached.
verbose: 0 for no logging to stdout,
1 for progress bar logging, 2 for one log line per epoch.
callbacks: list of `keras.callbacks.Callback` instances.
@@ -831,8 +835,8 @@ class Sequential(Model):
to apply a different weight to every timestep of every sample.
In this case you should make sure to specify
sample_weight_mode="temporal" in compile().
- initial_epoch: epoch at which to start training
- (useful for resuming a previous training run)
+ initial_epoch: Epoch at which to start training
+ (useful for resuming a previous training run).
# Returns
A `History` object. Its `History.history` attribute is
@@ -1055,6 +1059,10 @@ class Sequential(Model):
be equal to the number of unique samples of your dataset
divided by the batch size.
epochs: Integer, total number of iterations on the data.
+ Note that in conjunction with initial_epoch, the parameter
+ epochs is to be understood as "final epoch". The model is
+ not trained for n steps given by epochs, but until the
+ epoch epochs is reached.
verbose: Verbosity mode, 0, 1, or 2.
callbacks: List of callbacks to be called during training.
validation_data: This can be either
@@ -1079,7 +1087,7 @@ class Sequential(Model):
as they can't be passed
easily to children processes.
initial_epoch: Epoch at which to start training
- (useful for resuming a previous training run)
+ (useful for resuming a previous training run).
# Returns
A `History` object.
