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<h1 id="the-sequential-model-api">The Sequential model API</h1>
<p>To get started, read <a href="/getting-started/sequential-model-guide">this guide to the Keras Sequential model</a>.</p>
<hr />
<h2 id="sequential-model-methods">Sequential model methods</h2>
<h3 id="compile">compile</h3>
<pre><code class="python">compile(optimizer, loss=None, metrics=None, loss_weights=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None)
</code></pre>
<p>Configures the model for training.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>optimizer</strong>: String (name of optimizer) or optimizer instance.
See <a href="/optimizers">optimizers</a>.</li>
<li><strong>loss</strong>: String (name of objective function) or objective function or
<code>Loss</code> instance. See <a href="/losses">losses</a>.
If the model has multiple outputs, you can use a different loss
on each output by passing a dictionary or a list of losses.
The loss value that will be minimized by the model
will then be the sum of all individual losses.</li>
<li><strong>metrics</strong>: List of metrics to be evaluated by the model
during training and testing. Typically you will use
<code>metrics=['accuracy']</code>. To specify different metrics for different
outputs of a multi-output model, you could also pass a dictionary,
such as
<code>metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']}</code>.
You can also pass a list (len = len(outputs)) of lists of metrics
such as <code>metrics=[['accuracy'], ['accuracy', 'mse']]</code> or
<code>metrics=['accuracy', ['accuracy', 'mse']]</code>.</li>
<li><strong>loss_weights</strong>: Optional list or dictionary specifying scalar
coefficients (Python floats) to weight the loss contributions
of different model outputs.
The loss value that will be minimized by the model
will then be the <em>weighted sum</em> of all individual losses,
weighted by the <code>loss_weights</code> coefficients.
If a list, it is expected to have a 1:1 mapping
to the model's outputs. If a dict, it is expected to map
output names (strings) to scalar coefficients.</li>
<li><strong>sample_weight_mode</strong>: If you need to do timestep-wise
sample weighting (2D weights), set this to <code>"temporal"</code>.
<code>None</code> defaults to sample-wise weights (1D).
If the model has multiple outputs, you can use a different
<code>sample_weight_mode</code> on each output by passing a
dictionary or a list of modes.</li>
<li><strong>weighted_metrics</strong>: List of metrics to be evaluated and weighted
by sample_weight or class_weight during training and testing.</li>
<li><strong>target_tensors</strong>: By default, Keras will create placeholders for the
model's target, which will be fed with the target data during
training. If instead you would like to use your own
target tensors (in turn, Keras will not expect external
Numpy data for these targets at training time), you
can specify them via the <code>target_tensors</code> argument. It can be
a single tensor (for a single-output model), a list of tensors,
or a dict mapping output names to target tensors.</li>
<li><strong>**kwargs</strong>: When using the Theano/CNTK backends, these arguments
are passed into <code>K.function</code>.
When using the TensorFlow backend,
these arguments are passed into <code>tf.Session.run</code>.</li>
</ul>
<p><strong>Raises</strong></p>
<ul>
<li><strong>ValueError</strong>: In case of invalid arguments for
<code>optimizer</code>, <code>loss</code>, <code>metrics</code> or <code>sample_weight_mode</code>.</li>
</ul>
<hr />
<h3 id="fit">fit</h3>
<pre><code class="python">fit(x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False)
</code></pre>
<p>Trains the model for a fixed number of epochs (iterations on a dataset).</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>x</strong>: Input data. It could be:<ul>
<li>A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs).</li>
<li>A dict mapping input names to the corresponding
array/tensors, if the model has named inputs.</li>
<li>A generator or <code>keras.utils.Sequence</code> returning
<code>(inputs, targets)</code> or <code>(inputs, targets, sample weights)</code>.</li>
<li>None (default) if feeding from framework-native
tensors (e.g. TensorFlow data tensors).</li>
</ul>
</li>
<li><strong>y</strong>: Target data. Like the input data <code>x</code>,
it could be either Numpy array(s), framework-native tensor(s),
list of Numpy arrays (if the model has multiple outputs) or
None (default) if feeding from framework-native tensors
(e.g. TensorFlow data tensors).
If output layers in the model are named, you can also pass a
dictionary mapping output names to Numpy arrays.
If <code>x</code> is a generator, or <code>keras.utils.Sequence</code> instance,
<code>y</code> should not be specified (since targets will be obtained
from <code>x</code>).</li>
<li><strong>batch_size</strong>: Integer or <code>None</code>.
Number of samples per gradient update.
If unspecified, <code>batch_size</code> will default to 32.
Do not specify the <code>batch_size</code> if your data is in the
form of symbolic tensors, generators, or <code>Sequence</code> instances
(since they generate batches).</li>
<li><strong>epochs</strong>: Integer. Number of epochs to train the model.
An epoch is an iteration over the entire <code>x</code> and <code>y</code>
data provided.
Note that in conjunction with <code>initial_epoch</code>,
<code>epochs</code> is to be understood as "final epoch".
The model is not trained for a number of iterations
given by <code>epochs</code>, but merely until the epoch
of index <code>epochs</code> is reached.</li>
<li><strong>verbose</strong>: Integer. 0, 1, or 2. Verbosity mode.
0 = silent, 1 = progress bar, 2 = one line per epoch.</li>
<li><strong>callbacks</strong>: List of <code>keras.callbacks.Callback</code> instances.
List of callbacks to apply during training and validation
(if ).
See <a href="/callbacks">callbacks</a>.</li>
<li><strong>validation_split</strong>: Float between 0 and 1.
Fraction of the training data to be used as validation data.
The model will set apart this fraction of the training data,
will not train on it, and will evaluate
the loss and any model metrics
on this data at the end of each epoch.
The validation data is selected from the last samples
in the <code>x</code> and <code>y</code> data provided, before shuffling.
This argument is not supported when <code>x</code> is a generator or
<code>Sequence</code> instance.</li>
<li>
<p><strong>validation_data</strong>: Data on which to evaluate
the loss and any model metrics at the end of each epoch.
The model will not be trained on this data.
<code>validation_data</code> will override <code>validation_split</code>.
<code>validation_data</code> could be:
- tuple <code>(x_val, y_val)</code> of Numpy arrays or tensors
- tuple <code>(x_val, y_val, val_sample_weights)</code> of Numpy arrays
- dataset or a dataset iterator</p>
<p>For the first two cases, <code>batch_size</code> must be provided.
For the last case, <code>validation_steps</code> must be provided.</p>
</li>
<li>
<p><strong>shuffle</strong>: Boolean (whether to shuffle the training data
before each epoch) or str (for 'batch').
'batch' is a special option for dealing with the
limitations of HDF5 data; it shuffles in batch-sized chunks.
Has no effect when <code>steps_per_epoch</code> is not <code>None</code>.</p>
</li>
<li><strong>class_weight</strong>: Optional dictionary mapping class indices (integers)
to a weight (float) value, used for weighting the loss function
(during training only).
This can be useful to tell the model to
"pay more attention" to samples from
an under-represented class.</li>
<li><strong>sample_weight</strong>: Optional Numpy array of weights for
the training samples, used for weighting the loss function
(during training only). You can either pass a flat (1D)
Numpy array with the same length as the input samples
(1:1 mapping between weights and samples),
or in the case of temporal data,
you can pass a 2D array with shape
<code>(samples, sequence_length)</code>,
to apply a different weight to every timestep of every sample.
In this case you should make sure to specify
<code>sample_weight_mode="temporal"</code> in <code>compile()</code>. This argument
is not supported when <code>x</code> generator, or <code>Sequence</code> instance,
instead provide the sample_weights as the third element of <code>x</code>.</li>
<li><strong>initial_epoch</strong>: Integer.
Epoch at which to start training
(useful for resuming a previous training run).</li>
<li><strong>steps_per_epoch</strong>: Integer or <code>None</code>.
Total number of steps (batches of samples)
before declaring one epoch finished and starting the
next epoch. When training with input tensors such as
TensorFlow data tensors, the default <code>None</code> is equal to
the number of samples in your dataset divided by
the batch size, or 1 if that cannot be determined.</li>
<li><strong>validation_steps</strong>: Only relevant if <code>steps_per_epoch</code>
is specified. Total number of steps (batches of samples)
to validate before stopping.</li>
<li><strong>validation_steps</strong>: Only relevant if <code>validation_data</code> is provided
and is a generator. Total number of steps (batches of samples)
to draw before stopping when performing validation at the end
of every epoch.</li>
<li><strong>validation_freq</strong>: Only relevant if validation data is provided. Integer
or list/tuple/set. If an integer, specifies how many training
epochs to run before a new validation run is performed, e.g.
<code>validation_freq=2</code> runs validation every 2 epochs. If a list,
tuple, or set, specifies the epochs on which to run validation,
e.g. <code>validation_freq=[1, 2, 10]</code> runs validation at the end
of the 1st, 2nd, and 10th epochs.</li>
<li><strong>max_queue_size</strong>: Integer. Used for generator or <code>keras.utils.Sequence</code>
input only. Maximum size for the generator queue.
If unspecified, <code>max_queue_size</code> will default to 10.</li>
<li><strong>workers</strong>: Integer. Used for generator or <code>keras.utils.Sequence</code> input
only. Maximum number of processes to spin up
when using process-based threading. If unspecified, <code>workers</code>
will default to 1. If 0, will execute the generator on the main
thread.</li>
<li><strong>use_multiprocessing</strong>: Boolean. Used for generator or
<code>keras.utils.Sequence</code> input only. If <code>True</code>, use process-based
threading. If unspecified, <code>use_multiprocessing</code> will default to
<code>False</code>. Note that because this implementation relies on
multiprocessing, you should not pass non-picklable arguments to
the generator as they can't be passed easily to children processes.</li>
<li><strong>**kwargs</strong>: Used for backwards compatibility.</li>
</ul>
<p><strong>Returns</strong></p>
<p>A <code>History</code> object. Its <code>History.history</code> attribute is
a record of training loss values and metrics values
at successive epochs, as well as validation loss values
and validation metrics values (if applicable).</p>
<p><strong>Raises</strong></p>
<ul>
<li><strong>RuntimeError</strong>: If the model was never compiled.</li>
<li><strong>ValueError</strong>: In case of mismatch between the provided input data
and what the model expects.</li>
</ul>
<hr />
<h3 id="evaluate">evaluate</h3>
<pre><code class="python">evaluate(x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False)
</code></pre>
<p>Returns the loss value & metrics values for the model in test mode.</p>
<p>Computation is done in batches.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>x</strong>: Input data. It could be:<ul>
<li>A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs).</li>
<li>A dict mapping input names to the corresponding
array/tensors, if the model has named inputs.</li>
<li>A generator or <code>keras.utils.Sequence</code> returning
<code>(inputs, targets)</code> or <code>(inputs, targets, sample weights)</code>.</li>
<li>None (default) if feeding from framework-native
tensors (e.g. TensorFlow data tensors).</li>
</ul>
</li>
<li><strong>y</strong>: Target data. Like the input data <code>x</code>,
it could be either Numpy array(s), framework-native tensor(s),
list of Numpy arrays (if the model has multiple outputs) or
None (default) if feeding from framework-native tensors
(e.g. TensorFlow data tensors).
If output layers in the model are named, you can also pass a
dictionary mapping output names to Numpy arrays.
If <code>x</code> is a generator, or <code>keras.utils.Sequence</code> instance,
<code>y</code> should not be specified (since targets will be obtained
from <code>x</code>).</li>
<li><strong>batch_size</strong>: Integer or <code>None</code>.
Number of samples per gradient update.
If unspecified, <code>batch_size</code> will default to 32.
Do not specify the <code>batch_size</code> is your data is in the
form of symbolic tensors, generators, or
<code>keras.utils.Sequence</code> instances (since they generate batches).</li>
<li><strong>verbose</strong>: 0 or 1. Verbosity mode.
0 = silent, 1 = progress bar.</li>
<li><strong>sample_weight</strong>: Optional Numpy array of weights for
the test samples, used for weighting the loss function.
You can either pass a flat (1D)
Numpy array with the same length as the input samples
(1:1 mapping between weights and samples),
or in the case of temporal data,
you can pass a 2D array with shape
<code>(samples, sequence_length)</code>,
to apply a different weight to every timestep of every sample.
In this case you should make sure to specify
<code>sample_weight_mode="temporal"</code> in <code>compile()</code>.</li>
<li><strong>steps</strong>: Integer or <code>None</code>.
Total number of steps (batches of samples)
before declaring the evaluation round finished.
Ignored with the default value of <code>None</code>.</li>
<li><strong>callbacks</strong>: List of <code>keras.callbacks.Callback</code> instances.
List of callbacks to apply during evaluation.
See <a href="/callbacks">callbacks</a>.</li>
<li><strong>max_queue_size</strong>: Integer. Used for generator or <code>keras.utils.Sequence</code>
input only. Maximum size for the generator queue.
If unspecified, <code>max_queue_size</code> will default to 10.</li>
<li><strong>workers</strong>: Integer. Used for generator or <code>keras.utils.Sequence</code> input
only. Maximum number of processes to spin up when using
process-based threading. If unspecified, <code>workers</code> will default
to 1. If 0, will execute the generator on the main thread.</li>
<li><strong>use_multiprocessing</strong>: Boolean. Used for generator or
<code>keras.utils.Sequence</code> input only. If <code>True</code>, use process-based
threading. If unspecified, <code>use_multiprocessing</code> will default to
<code>False</code>. Note that because this implementation relies on
multiprocessing, you should not pass non-picklable arguments to
the generator as they can't be passed easily to children processes.</li>
</ul>
<p><strong>Raises</strong></p>
<ul>
<li><strong>ValueError</strong>: in case of invalid arguments.</li>
</ul>
<p><strong>Returns</strong></p>
<p>Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute <code>model.metrics_names</code> will give you
the display labels for the scalar outputs.</p>
<hr />
<h3 id="predict">predict</h3>
<pre><code class="python">predict(x, batch_size=None, verbose=0, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False)
</code></pre>
<p>Generates output predictions for the input samples.</p>
<p>Computation is done in batches.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>x</strong>: Input data. It could be:<ul>
<li>A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs).</li>
<li>A dict mapping input names to the corresponding
array/tensors, if the model has named inputs.</li>
<li>A generator or <code>keras.utils.Sequence</code> returning
<code>(inputs, targets)</code> or <code>(inputs, targets, sample weights)</code>.</li>
<li>None (default) if feeding from framework-native
tensors (e.g. TensorFlow data tensors).</li>
</ul>
</li>
<li><strong>batch_size</strong>: Integer or <code>None</code>.
Number of samples per gradient update.
If unspecified, <code>batch_size</code> will default to 32.
Do not specify the <code>batch_size</code> is your data is in the
form of symbolic tensors, generators, or
<code>keras.utils.Sequence</code> instances (since they generate batches).</li>
<li><strong>verbose</strong>: Verbosity mode, 0 or 1.</li>
<li><strong>steps</strong>: Total number of steps (batches of samples)
before declaring the prediction round finished.
Ignored with the default value of <code>None</code>.</li>
<li><strong>callbacks</strong>: List of <code>keras.callbacks.Callback</code> instances.
List of callbacks to apply during prediction.
See <a href="/callbacks">callbacks</a>.</li>
<li><strong>max_queue_size</strong>: Integer. Used for generator or <code>keras.utils.Sequence</code>
input only. Maximum size for the generator queue.
If unspecified, <code>max_queue_size</code> will default to 10.</li>
<li><strong>workers</strong>: Integer. Used for generator or <code>keras.utils.Sequence</code> input
only. Maximum number of processes to spin up when using
process-based threading. If unspecified, <code>workers</code> will default
to 1. If 0, will execute the generator on the main thread.</li>
<li><strong>use_multiprocessing</strong>: Boolean. Used for generator or
<code>keras.utils.Sequence</code> input only. If <code>True</code>, use process-based
threading. If unspecified, <code>use_multiprocessing</code> will default to
<code>False</code>. Note that because this implementation relies on
multiprocessing, you should not pass non-picklable arguments to
the generator as they can't be passed easily to children processes.</li>
</ul>
<p><strong>Returns</strong></p>
<p>Numpy array(s) of predictions.</p>
<p><strong>Raises</strong></p>
<ul>
<li><strong>ValueError</strong>: In case of mismatch between the provided
input data and the model's expectations,
or in case a stateful model receives a number of samples
that is not a multiple of the batch size.</li>
</ul>
<hr />
<h3 id="train_on_batch">train_on_batch</h3>
<pre><code class="python">train_on_batch(x, y, sample_weight=None, class_weight=None, reset_metrics=True)
</code></pre>
<p>Runs a single gradient update on a single batch of data.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>x</strong>: Numpy array of training data,
or list of Numpy arrays if the model has multiple inputs.
If all inputs in the model are named,
you can also pass a dictionary
mapping input names to Numpy arrays.</li>
<li><strong>y</strong>: Numpy array of target data,
or list of Numpy arrays if the model has multiple outputs.
If all outputs in the model are named,
you can also pass a dictionary
mapping output names to Numpy arrays.</li>
<li><strong>sample_weight</strong>: Optional array of the same length as x, containing
weights to apply to the model's loss for each sample.
In the case of temporal data, you can pass a 2D array
with shape (samples, sequence_length),
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().</li>
<li><strong>class_weight</strong>: Optional dictionary mapping
class indices (integers) to
a weight (float) to apply to the model's loss for the samples
from this class during training.
This can be useful to tell the model to "pay more attention" to
samples from an under-represented class.</li>
<li><strong>reset_metrics</strong>: If <code>True</code>, the metrics returned will be only for this
batch. If <code>False</code>, the metrics will be statefully accumulated across
batches.</li>
</ul>
<p><strong>Returns</strong></p>
<p>Scalar training loss
(if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute <code>model.metrics_names</code> will give you
the display labels for the scalar outputs.</p>
<hr />
<h3 id="test_on_batch">test_on_batch</h3>
<pre><code class="python">test_on_batch(x, y, sample_weight=None, reset_metrics=True)
</code></pre>
<p>Test the model on a single batch of samples.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>x</strong>: Numpy array of test data,
or list of Numpy arrays if the model has multiple inputs.
If all inputs in the model are named,
you can also pass a dictionary
mapping input names to Numpy arrays.</li>
<li><strong>y</strong>: Numpy array of target data,
or list of Numpy arrays if the model has multiple outputs.
If all outputs in the model are named,
you can also pass a dictionary
mapping output names to Numpy arrays.</li>
<li><strong>sample_weight</strong>: Optional array of the same length as x, containing
weights to apply to the model's loss for each sample.
In the case of temporal data, you can pass a 2D array
with shape (samples, sequence_length),
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().</li>
<li><strong>reset_metrics</strong>: If <code>True</code>, the metrics returned will be only for this
batch. If <code>False</code>, the metrics will be statefully accumulated across
batches.</li>
</ul>
<p><strong>Returns</strong></p>
<p>Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute <code>model.metrics_names</code> will give you
the display labels for the scalar outputs.</p>
<hr />
<h3 id="predict_on_batch">predict_on_batch</h3>
<pre><code class="python">predict_on_batch(x)
</code></pre>
<p>Returns predictions for a single batch of samples.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>x</strong>: Input samples, as a Numpy array.</li>
</ul>
<p><strong>Returns</strong></p>
<p>Numpy array(s) of predictions.</p>
<hr />
<h3 id="fit_generator">fit_generator</h3>
<pre><code class="python">fit_generator(generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None, validation_data=None, validation_steps=None, validation_freq=1, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0)
</code></pre>
<p>Trains the model on data generated batch-by-batch by a Python generator
(or an instance of <code>Sequence</code>).</p>
<p>The generator is run in parallel to the model, for efficiency.
For instance, this allows you to do real-time data augmentation
on images on CPU in parallel to training your model on GPU.</p>
<p>The use of <code>keras.utils.Sequence</code> guarantees the ordering
and guarantees the single use of every input per epoch when
using <code>use_multiprocessing=True</code>.</p>
<p><strong>Arguments</strong></p>
<ul>
<li>
<p><strong>generator</strong>: A generator or an instance of <code>Sequence</code>
(<code>keras.utils.Sequence</code>) object in order to avoid
duplicate data when using multiprocessing.
The output of the generator must be either</p>
<ul>
<li>a tuple <code>(inputs, targets)</code></li>
<li>a tuple <code>(inputs, targets, sample_weights)</code>.</li>
</ul>
<p>This tuple (a single output of the generator) makes a single
batch. Therefore, all arrays in this tuple must have the same
length (equal to the size of this batch). Different batches may
have different sizes. For example, the last batch of the epoch
is commonly smaller than the others, if the size of the dataset
is not divisible by the batch size.
The generator is expected to loop over its data
indefinitely. An epoch finishes when <code>steps_per_epoch</code>
batches have been seen by the model.</p>
</li>
<li>
<p><strong>steps_per_epoch</strong>: Integer.
Total number of steps (batches of samples)
to yield from <code>generator</code> before declaring one epoch
finished and starting the next epoch. It should typically
be equal to <code>ceil(num_samples / batch_size)</code>
Optional for <code>Sequence</code>: if unspecified, will use
the <code>len(generator)</code> as a number of steps.</p>
</li>
<li><strong>epochs</strong>: Integer. Number of epochs to train the model.
An epoch is an iteration over the entire data provided,
as defined by <code>steps_per_epoch</code>.
Note that in conjunction with <code>initial_epoch</code>,
<code>epochs</code> is to be understood as "final epoch".
The model is not trained for a number of iterations
given by <code>epochs</code>, but merely until the epoch
of index <code>epochs</code> is reached.</li>
<li><strong>verbose</strong>: Integer. 0, 1, or 2. Verbosity mode.
0 = silent, 1 = progress bar, 2 = one line per epoch.</li>
<li><strong>callbacks</strong>: List of <code>keras.callbacks.Callback</code> instances.
List of callbacks to apply during training.
See <a href="/callbacks">callbacks</a>.</li>
<li>
<p><strong>validation_data</strong>: This can be either</p>
<ul>
<li>a generator or a <code>Sequence</code> object for the validation data</li>
<li>tuple <code>(x_val, y_val)</code></li>
<li>tuple <code>(x_val, y_val, val_sample_weights)</code></li>
</ul>
<p>on which to evaluate
the loss and any model metrics at the end of each epoch.
The model will not be trained on this data.</p>
</li>
<li>
<p><strong>validation_steps</strong>: Only relevant if <code>validation_data</code>
is a generator. Total number of steps (batches of samples)
to yield from <code>validation_data</code> generator before stopping
at the end of every epoch. It should typically
be equal to the number of samples of your
validation dataset divided by the batch size.
Optional for <code>Sequence</code>: if unspecified, will use
the <code>len(validation_data)</code> as a number of steps.</p>
</li>
<li><strong>validation_freq</strong>: Only relevant if validation data is provided. Integer
or <code>collections.Container</code> instance (e.g. list, tuple, etc.). If an
integer, specifies how many training epochs to run before a new
validation run is performed, e.g. <code>validation_freq=2</code> runs
validation every 2 epochs. If a Container, specifies the epochs on
which to run validation, e.g. <code>validation_freq=[1, 2, 10]</code> runs
validation at the end of the 1st, 2nd, and 10th epochs.</li>
<li><strong>class_weight</strong>: Optional dictionary mapping class indices (integers)
to a weight (float) value, used for weighting the loss function
(during training only). This can be useful to tell the model to
"pay more attention" to samples
from an under-represented class.</li>
<li><strong>max_queue_size</strong>: Integer. Maximum size for the generator queue.
If unspecified, <code>max_queue_size</code> will default to 10.</li>
<li><strong>workers</strong>: Integer. Maximum number of processes to spin up
when using process-based threading.
If unspecified, <code>workers</code> will default to 1. If 0, will
execute the generator on the main thread.</li>
<li><strong>use_multiprocessing</strong>: Boolean.
If <code>True</code>, use process-based threading.
If unspecified, <code>use_multiprocessing</code> will default to <code>False</code>.
Note that because this implementation
relies on multiprocessing,
you should not pass non-picklable arguments to the generator
as they can't be passed easily to children processes.</li>
<li><strong>shuffle</strong>: Boolean. Whether to shuffle the order of the batches at
the beginning of each epoch. Only used with instances
of <code>Sequence</code> (<code>keras.utils.Sequence</code>).
Has no effect when <code>steps_per_epoch</code> is not <code>None</code>.</li>
<li><strong>initial_epoch</strong>: Integer.
Epoch at which to start training
(useful for resuming a previous training run).</li>
</ul>
<p><strong>Returns</strong></p>
<p>A <code>History</code> object. Its <code>History.history</code> attribute is
a record of training loss values and metrics values
at successive epochs, as well as validation loss values
and validation metrics values (if applicable).</p>
<p><strong>Raises</strong></p>
<ul>
<li><strong>ValueError</strong>: In case the generator yields data in an invalid format.</li>
</ul>
<p><strong>Example</strong></p>
<pre><code class="python">def generate_arrays_from_file(path):
while True:
with open(path) as f:
for line in f:
# create numpy arrays of input data
# and labels, from each line in the file
x1, x2, y = process_line(line)
yield ({'input_1': x1, 'input_2': x2}, {'output': y})
model.fit_generator(generate_arrays_from_file('/my_file.txt'),
steps_per_epoch=10000, epochs=10)
</code></pre>
<hr />
<h3 id="evaluate_generator">evaluate_generator</h3>
<pre><code class="python">evaluate_generator(generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)
</code></pre>
<p>Evaluates the model on a data generator.</p>
<p>The generator should return the same kind of data
as accepted by <code>test_on_batch</code>.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>generator</strong>: Generator yielding tuples (inputs, targets)
or (inputs, targets, sample_weights)
or an instance of Sequence (keras.utils.Sequence)
object in order to avoid duplicate data
when using multiprocessing.</li>
<li><strong>steps</strong>: Total number of steps (batches of samples)
to yield from <code>generator</code> before stopping.
Optional for <code>Sequence</code>: if unspecified, will use
the <code>len(generator)</code> as a number of steps.</li>
<li><strong>callbacks</strong>: List of <code>keras.callbacks.Callback</code> instances.
List of callbacks to apply during training.
See <a href="/callbacks">callbacks</a>.</li>
<li><strong>max_queue_size</strong>: maximum size for the generator queue</li>
<li><strong>workers</strong>: Integer. Maximum number of processes to spin up
when using process based threading.
If unspecified, <code>workers</code> will default to 1. If 0, will
execute the generator on the main thread.</li>
<li><strong>use_multiprocessing</strong>: if True, use process based threading.
Note that because
this implementation relies on multiprocessing,
you should not pass
non picklable arguments to the generator
as they can't be passed
easily to children processes.</li>
<li><strong>verbose</strong>: verbosity mode, 0 or 1.</li>
</ul>
<p><strong>Returns</strong></p>
<p>Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute <code>model.metrics_names</code> will give you
the display labels for the scalar outputs.</p>
<p><strong>Raises</strong></p>
<ul>
<li><strong>ValueError</strong>: In case the generator yields
data in an invalid format.</li>
</ul>
<hr />
<h3 id="predict_generator">predict_generator</h3>
<pre><code class="python">predict_generator(generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)
</code></pre>
<p>Generates predictions for the input samples from a data generator.</p>
<p>The generator should return the same kind of data as accepted by
<code>predict_on_batch</code>.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>generator</strong>: Generator yielding batches of input samples
or an instance of Sequence (keras.utils.Sequence)
object in order to avoid duplicate data
when using multiprocessing.</li>
<li><strong>steps</strong>: Total number of steps (batches of samples)
to yield from <code>generator</code> before stopping.
Optional for <code>Sequence</code>: if unspecified, will use
the <code>len(generator)</code> as a number of steps.</li>
<li><strong>callbacks</strong>: List of <code>keras.callbacks.Callback</code> instances.
List of callbacks to apply during training.
See <a href="/callbacks">callbacks</a>.</li>
<li><strong>max_queue_size</strong>: Maximum size for the generator queue.</li>
<li><strong>workers</strong>: Integer. Maximum number of processes to spin up
when using process based threading.
If unspecified, <code>workers</code> will default to 1. If 0, will
execute the generator on the main thread.</li>
<li><strong>use_multiprocessing</strong>: If <code>True</code>, use process based threading.
Note that because
this implementation relies on multiprocessing,
you should not pass
non picklable arguments to the generator
as they can't be passed
easily to children processes.</li>
<li><strong>verbose</strong>: verbosity mode, 0 or 1.</li>
</ul>
<p><strong>Returns</strong></p>
<p>Numpy array(s) of predictions.</p>
<p><strong>Raises</strong></p>
<ul>
<li><strong>ValueError</strong>: In case the generator yields
data in an invalid format.</li>
</ul>
<hr />
<h3 id="get_layer">get_layer</h3>
<pre><code class="python">get_layer(name=None, index=None)
</code></pre>
<p>Retrieves a layer based on either its name (unique) or index.</p>
<p>If <code>name</code> and <code>index</code> are both provided, <code>index</code> will take precedence.</p>
<p>Indices are based on order of horizontal graph traversal (bottom-up).</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>name</strong>: String, name of layer.</li>
<li><strong>index</strong>: Integer, index of layer.</li>
</ul>
<p><strong>Returns</strong></p>
<p>A layer instance.</p>
<p><strong>Raises</strong></p>
<ul>
<li><strong>ValueError</strong>: In case of invalid layer name or index.</li>
</ul>
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