The Sequential model API
- Sequential model methods

The Sequential model API

To get started, read this guide to the Keras Sequential model.

Sequential model methods

compile

compile(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 or Loss instance. 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 multi-output model, you could also pass a dictionary, such as metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']}. You can also pass a list (len = len(outputs)) of lists of metrics such as metrics=[['accuracy'], ['accuracy', 'mse']] or metrics=['accuracy', ['accuracy', 'mse']].
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 dict, it is expected to map output names (strings) to scalar coefficients.
sample_weight_mode: If you need to do timestep-wise sample weighting (2D weights), set this to "temporal". None defaults to sample-wise weights (1D). If the model has multiple outputs, you can use a different sample_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 single-output 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 into tf.Session.run.

Raises

ValueError: In case of invalid arguments for optimizer, loss, metrics or sample_weight_mode.

fit

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)

Trains the model for a fixed number of epochs (iterations on a dataset).

Arguments

x: Input data. It could be:
- A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
- A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
- A generator or keras.utils.Sequence returning (inputs, targets) or (inputs, targets, sample weights).
- None (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors).
y: Target data. Like the input data x, 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 x is a generator, or keras.utils.Sequence instance, y should not be specified (since targets will be obtained from x).
batch_size: Integer or None. Number of samples per gradient update. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of symbolic tensors, generators, or Sequence instances (since they generate batches).
epochs: Integer. Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided. Note that in conjunction with initial_epoch, epochs is to be understood as "final epoch". The model is not trained for a number of iterations given by epochs, but merely until the epoch of index epochs 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 and validation (if ). 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 and y data provided, before shuffling. This argument is not supported when x is a generator or Sequence instance.
validation_data: 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. validation_data will override validation_split. validation_data could be: - tuple (x_val, y_val) of Numpy arrays or tensors - tuple (x_val, y_val, val_sample_weights) of Numpy arrays - dataset or a dataset iterator

For the first two cases, batch_size must be provided.For the last case, validation_steps must be provided.

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 batch-sized chunks. Has no effect when steps_per_epoch is not None.
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 under-represented 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 specify sample_weight_mode="temporal" in compile(). This argument is not supported when x generator, or Sequence instance, instead provide the sample_weights as the third element of x.
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 default None 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.
validation_steps: Only relevant if validation_data 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.
validation_freq: 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. validation_freq=2 runs validation every 2 epochs. If a list, tuple, or set, specifies the epochs on which to run validation, e.g. validation_freq=[1, 2, 10] runs validation at the end of the 1st, 2nd, and 10th epochs.
max_queue_size: Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.
workers: Integer. Used for generator or keras.utils.Sequence input only. 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: Boolean. Used for generator or keras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. 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.
**kwargs: Used for backwards compatibility.

Returns

A History object. Its History.history attribute isa record of training loss values and metrics valuesat successive epochs, as well as validation loss valuesand 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(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)

Returns the loss value & metrics values for the model in test mode.

Computation is done in batches.

Arguments

x: Input data. It could be:
- A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
- A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
- A generator or keras.utils.Sequence returning (inputs, targets) or (inputs, targets, sample weights).
- None (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors).
y: Target data. Like the input data x, 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 x is a generator, or keras.utils.Sequence instance, y should not be specified (since targets will be obtained from x).
batch_size: Integer or None. Number of samples per gradient update. If unspecified, batch_size will default to 32. Do not specify the batch_size is your data is in the form of symbolic tensors, generators, or keras.utils.Sequence instances (since they generate batches).
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 specify sample_weight_mode="temporal" in compile().
steps: Integer or None. Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of None.
callbacks: List of keras.callbacks.Callback instances. List of callbacks to apply during evaluation. See callbacks.
max_queue_size: Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.
workers: Integer. Used for generator or keras.utils.Sequence input only. 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: Boolean. Used for generator or keras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. 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.

Raises

ValueError: in case of invalid arguments.

Returns

Scalar test loss (if the model has a single output and no metrics)or list of scalars (if the model has multiple outputsand/or metrics). The attribute model.metrics_names will give youthe display labels for the scalar outputs.

predict

predict(x, batch_size=None, verbose=0, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False)

Generates output predictions for the input samples.

Computation is done in batches.

Arguments

x: Input data. It could be:
- A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
- A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
- A generator or keras.utils.Sequence returning (inputs, targets) or (inputs, targets, sample weights).
- None (default) if feeding from framework-native 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. Do not specify the batch_size is your data is in the form of symbolic tensors, generators, or keras.utils.Sequence instances (since they generate batches).
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.
callbacks: List of keras.callbacks.Callback instances. List of callbacks to apply during prediction. See callbacks.
max_queue_size: Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.
workers: Integer. Used for generator or keras.utils.Sequence input only. 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: Boolean. Used for generator or keras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. 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.

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(x, y, sample_weight=None, class_weight=None, reset_metrics=True)

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 under-represented class.
reset_metrics: If True, the metrics returned will be only for this batch. If False, the metrics will be statefully accumulated across batches.

Returns

Scalar training loss(if the model has a single output and no metrics)or list of scalars (if the model has multiple outputsand/or metrics). The attribute model.metrics_names will give youthe display labels for the scalar outputs.

test_on_batch

test_on_batch(x, y, sample_weight=None, reset_metrics=True)

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().
reset_metrics: If True, the metrics returned will be only for this batch. If False, the metrics will be statefully accumulated across batches.

Returns

predict_on_batch

predict_on_batch(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(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)

Trains the model on data generated batch-by-batch 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 real-time data augmentationon images on CPU in parallel to training your model on GPU.

The use of keras.utils.Sequence guarantees the orderingand guarantees the single use of every input per epoch whenusing 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 singlebatch. Therefore, all arrays in this tuple must have the samelength (equal to the size of this batch). Different batches mayhave different sizes. For example, the last batch of the epochis commonly smaller than the others, if the size of the datasetis not divisible by the batch size.The generator is expected to loop over its dataindefinitely. An epoch finishes when steps_per_epochbatches have been seen by the model.
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 ceil(num_samples / batch_size) Optional for Sequence: if unspecified, will use the len(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 with initial_epoch, epochs is to be understood as "final epoch". The model is not trained for a number of iterations given by epochs, but merely until the epoch of index epochs 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 evaluatethe loss and any model metrics at the end of each epoch.The model will not be trained on this data.
validation_steps: Only relevant if validation_data is a generator. Total number of steps (batches of samples) to yield from validation_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 for Sequence: if unspecified, will use the len(validation_data) as a number of steps.
validation_freq: Only relevant if validation data is provided. Integer or collections.Container 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. validation_freq=2 runs validation every 2 epochs. If a Container, specifies the epochs on which to run validation, e.g. validation_freq=[1, 2, 10] runs validation at the end of the 1st, 2nd, and 10th epochs.
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 under-represented 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 process-based threading. If unspecified, workers will default to 1. If 0, will execute the generator on the main thread.
use_multiprocessing: Boolean. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. 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.
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 when steps_per_epoch is not None.
initial_epoch: Integer. Epoch at which to start training (useful for resuming a previous training run).

Returns

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(generator, steps=None, callbacks=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 dataas 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 for Sequence: if unspecified, will use the len(generator) as a number of steps.
callbacks: List of keras.callbacks.Callback instances. List of callbacks to apply during training. See callbacks.
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

Raises

ValueError: In case the generator yields data in an invalid format.

predict_generator

predict_generator(generator, steps=None, callbacks=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 bypredict_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 for Sequence: if unspecified, will use the len(generator) as a number of steps.
callbacks: List of keras.callbacks.Callback instances. List of callbacks to apply during training. See callbacks.
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(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 (bottom-up).

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.