Model class API
In the functional API, given some input tensor(s) and output tensor(s), you can instantiate a Model
via:
from keras.models import Model
from keras.layers import Input, Dense
a = Input(shape=(32,))
b = Dense(32)(a)
model = Model(inputs=a, outputs=b)
This model will include all layers required in the computation of b
given a
.
In the case of multiinput or multioutput models, you can use lists as well:
model = Model(inputs=[a1, a2], outputs=[b1, b2, b3])
For a detailed introduction of what Model
can do, read this guide to the Keras functional API.
Methods
compile
compile(self, optimizer, loss=None, metrics=None, loss_weights=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None)
Configures the model for training.
Arguments
 optimizer: String (name of optimizer) or optimizer instance. See optimizers.
 loss: String (name of objective function) or objective function. See losses. 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.
 metrics: List of metrics to be evaluated by the model
during training and testing.
Typically you will use
metrics=['accuracy']
. To specify different metrics for different outputs of a multioutput model, you could also pass a dictionary, such asmetrics={'output_a': 'accuracy'}
.  loss_weights: 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 weighted sum of all individual losses,
weighted by the
loss_weights
coefficients. If a list, it is expected to have a 1:1 mapping to the model's outputs. If a tensor, it is expected to map output names (strings) to scalar coefficients.  sample_weight_mode: If you need to do timestepwise
sample weighting (2D weights), set this to
"temporal"
.None
defaults to samplewise weights (1D). If the model has multiple outputs, you can use a differentsample_weight_mode
on each output by passing a dictionary or a list of modes.  weighted_metrics: List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing.
 target_tensors: 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
target_tensors
argument. It can be a single tensor (for a singleoutput model), a list of tensors, or a dict mapping output names to target tensors.  **kwargs: When using the Theano/CNTK backends, these arguments
are passed into
K.function
. When using the TensorFlow backend, these arguments are passed intotf.Session.run
.
Raises
 ValueError: In case of invalid arguments for
optimizer
,loss
,metrics
orsample_weight_mode
.
fit
fit(self, 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)
Trains the model for a given number of epochs (iterations on a dataset).
Arguments
 x: Numpy array of training data (if the model has a single input),
or list of Numpy arrays (if the model has multiple inputs).
If input layers in the model are named, you can also pass a
dictionary mapping input names to Numpy arrays.
x
can beNone
(default) if feeding from frameworknative tensors (e.g. TensorFlow data tensors).  y: Numpy array of target (label) data
(if the model has a single output),
or list of Numpy arrays (if the model has multiple outputs).
If output layers in the model are named, you can also pass a
dictionary mapping output names to Numpy arrays.
y
can beNone
(default) if feeding from frameworknative tensors (e.g. TensorFlow data tensors).  batch_size: Integer or
None
. Number of samples per gradient update. If unspecified,batch_size
will default to 32.  epochs: Integer. Number of epochs to train the model.
An epoch is an iteration over the entire
x
andy
data provided. Note that in conjunction withinitial_epoch
,epochs
is to be understood as "final epoch". The model is not trained for a number of iterations given byepochs
, but merely until the epoch of indexepochs
is reached.  verbose: Integer. 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch.
 callbacks: List of
keras.callbacks.Callback
instances. List of callbacks to apply during training. See callbacks.  validation_split: 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
x
andy
data provided, before shuffling.  validation_data: tuple
(x_val, y_val)
or tuple(x_val, y_val, val_sample_weights)
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.validation_data
will overridevalidation_split
.  shuffle: 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 batchsized chunks.
Has no effect when
steps_per_epoch
is notNone
.  class_weight: 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 underrepresented class.
 sample_weight: 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
(samples, sequence_length)
, to apply a different weight to every timestep of every sample. In this case you should make sure to specifysample_weight_mode="temporal"
incompile()
.  initial_epoch: Integer. Epoch at which to start training (useful for resuming a previous training run).
 steps_per_epoch: Integer or
None
. 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 defaultNone
is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined.  validation_steps: Only relevant if
steps_per_epoch
is specified. Total number of steps (batches of samples) to validate before stopping.
Returns
A History
object. Its History.history
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).
Raises
 RuntimeError: If the model was never compiled.
 ValueError: In case of mismatch between the provided input data and what the model expects.
evaluate
evaluate(self, x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None)
Returns the loss value & metrics values for the model in test mode.
Computation is done in batches.
Arguments
 x: Numpy array of test data (if the model has a single input),
or list of Numpy arrays (if the model has multiple inputs).
If input layers in the model are named, you can also pass a
dictionary mapping input names to Numpy arrays.
x
can beNone
(default) if feeding from frameworknative tensors (e.g. TensorFlow data tensors).  y: Numpy array of target (label) data
(if the model has a single output),
or list of Numpy arrays (if the model has multiple outputs).
If output layers in the model are named, you can also pass a
dictionary mapping output names to Numpy arrays.
y
can beNone
(default) if feeding from frameworknative tensors (e.g. TensorFlow data tensors).  batch_size: Integer or
None
. Number of samples per evaluation step. If unspecified,batch_size
will default to 32.  verbose: 0 or 1. Verbosity mode. 0 = silent, 1 = progress bar.
 sample_weight: 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
(samples, sequence_length)
, to apply a different weight to every timestep of every sample. In this case you should make sure to specifysample_weight_mode="temporal"
incompile()
.  steps: Integer or
None
. Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value ofNone
.
Returns
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 model.metrics_names
will give you
the display labels for the scalar outputs.
predict
predict(self, x, batch_size=None, verbose=0, steps=None)
Generates output predictions for the input samples.
Computation is done in batches.
Arguments
 x: The input data, as a Numpy array (or list of Numpy arrays if the model has multiple inputs).
 batch_size: Integer. If unspecified, it will default to 32.
 verbose: Verbosity mode, 0 or 1.
 steps: Total number of steps (batches of samples)
before declaring the prediction round finished.
Ignored with the default value of
None
.
Returns
Numpy array(s) of predictions.
Raises
 ValueError: 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.
train_on_batch
train_on_batch(self, x, y, sample_weight=None, class_weight=None)
Runs a single gradient update on a single batch of data.
Arguments
 x: 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.
 y: 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.
 sample_weight: 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().
 class_weight: 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 underrepresented class.
Returns
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 model.metrics_names
will give you
the display labels for the scalar outputs.
test_on_batch
test_on_batch(self, x, y, sample_weight=None)
Test the model on a single batch of samples.
Arguments
 x: 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.
 y: 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.
 sample_weight: 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().
Returns
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 model.metrics_names
will give you
the display labels for the scalar outputs.
predict_on_batch
predict_on_batch(self, x)
Returns predictions for a single batch of samples.
Arguments
 x: Input samples, as a Numpy array.
Returns
Numpy array(s) of predictions.
fit_generator
fit_generator(self, generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None, validation_data=None, validation_steps=None, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0)
Trains the model on data generated batchbybatch by a Python generator (or an instance of Sequence
).
The generator is run in parallel to the model, for efficiency. For instance, this allows you to do realtime data augmentation on images on CPU in parallel to training your model on GPU.
The use of keras.utils.Sequence
guarantees the ordering
and guarantees the single use of every input per epoch when
using use_multiprocessing=True
.
Arguments

generator: A generator or an instance of
Sequence
(keras.utils.Sequence
) object in order to avoid duplicate data when using multiprocessing. The output of the generator must be either a tuple
(inputs, targets)
 a tuple
(inputs, targets, sample_weights)
.
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
steps_per_epoch
batches have been seen by the model.  a tuple

steps_per_epoch: Integer. Total number of steps (batches of samples) to yield from
generator
before declaring one epoch finished and starting the next epoch. It should typically be equal to the number of samples of your dataset divided by the batch size. Optional forSequence
: if unspecified, will use thelen(generator)
as a number of steps.  epochs: Integer. Number of epochs to train the model.
An epoch is an iteration over the entire data provided,
as defined by
steps_per_epoch
. Note that in conjunction withinitial_epoch
,epochs
is to be understood as "final epoch". The model is not trained for a number of iterations given byepochs
, but merely until the epoch of indexepochs
is reached.  verbose: Integer. 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch.
 callbacks: List of
keras.callbacks.Callback
instances. List of callbacks to apply during training. See callbacks. 
validation_data: This can be either
 a generator or a
Sequence
object for the validation data  tuple
(x_val, y_val)
 tuple
(x_val, y_val, val_sample_weights)
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.
 a generator or a

validation_steps: Only relevant if
validation_data
is a generator. Total number of steps (batches of samples) to yield fromvalidation_data
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 forSequence
: if unspecified, will use thelen(validation_data)
as a number of steps.  class_weight: 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 underrepresented class.
 max_queue_size: Integer. Maximum size for the generator queue.
If unspecified,
max_queue_size
will default to 10.  workers: Integer. Maximum number of processes to spin up
when using processbased threading.
If unspecified,
workers
will default to 1. If 0, will execute the generator on the main thread.  use_multiprocessing: Boolean.
If
True
, use processbased threading. If unspecified,use_multiprocessing
will default toFalse
. Note that because this implementation relies on multiprocessing, you should not pass nonpicklable arguments to the generator as they can't be passed easily to children processes.  shuffle: Boolean. Whether to shuffle the order of the batches at
the beginning of each epoch. Only used with instances
of
Sequence
(keras.utils.Sequence
). Has no effect whensteps_per_epoch
is notNone
.  initial_epoch: Integer. Epoch at which to start training (useful for resuming a previous training run).
Returns
A History
object. Its History.history
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).
Raises
 ValueError: In case the generator yields data in an invalid format.
Example
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)
evaluate_generator
evaluate_generator(self, generator, steps=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)
Evaluates the model on a data generator.
The generator should return the same kind of data
as accepted by test_on_batch
.
Arguments
 generator: 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.
 steps: Total number of steps (batches of samples)
to yield from
generator
before stopping. Optional forSequence
: if unspecified, will use thelen(generator)
as a number of steps.  max_queue_size: maximum size for the generator queue
 workers: Integer. Maximum number of processes to spin up
when using process based threading.
If unspecified,
workers
will default to 1. If 0, will execute the generator on the main thread.  use_multiprocessing: 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.
 verbose: verbosity mode, 0 or 1.
Returns
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 model.metrics_names
will give you
the display labels for the scalar outputs.
Raises
 ValueError: In case the generator yields data in an invalid format.
predict_generator
predict_generator(self, generator, steps=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)
Generates predictions for the input samples from a data generator.
The generator should return the same kind of data as accepted by
predict_on_batch
.
Arguments
 generator: Generator yielding batches of input samples or an instance of Sequence (keras.utils.Sequence) object in order to avoid duplicate data when using multiprocessing.
 steps: Total number of steps (batches of samples)
to yield from
generator
before stopping. Optional forSequence
: if unspecified, will use thelen(generator)
as a number of steps.  max_queue_size: Maximum size for the generator queue.
 workers: Integer. Maximum number of processes to spin up
when using process based threading.
If unspecified,
workers
will default to 1. If 0, will execute the generator on the main thread.  use_multiprocessing: 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.  verbose: verbosity mode, 0 or 1.
Returns
Numpy array(s) of predictions.
Raises
 ValueError: In case the generator yields data in an invalid format.
get_layer
get_layer(self, name=None, index=None)
Retrieves a layer based on either its name (unique) or index.
If name
and index
are both provided, index
will take precedence.
Indices are based on order of horizontal graph traversal (bottomup).
Arguments
 name: String, name of layer.
 index: Integer, index of layer.
Returns
A layer instance.
Raises
 ValueError: In case of invalid layer name or index.