The Sequential model API

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

Useful attributes of Model

model.layers is a list of the layers added to the model.

Sequential model methods

compile

compile(self, optimizer, loss, metrics=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None)

Configures the model for training.

Arguments

optimizer: String (name of optimizer) or optimizer object. 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 multi-output model, you could also pass a dictionary, such as metrics={'output_a': 'accuracy'}.
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 a placeholder 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 tensor (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 should be a single tensor (for a single-output Sequential model).
**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.

Example

model = Sequential()
model.add(Dense(32, input_shape=(500,)))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

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 fixed number of epochs (iterations on a dataset).

Arguments

x: Numpy array of training data. If the input layer in the model is named, you can also pass a dictionary mapping the input name to a Numpy array. x can be None (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors).
y: Numpy array of target (label) data. If the output layer in the model is named, you can also pass a dictionary mapping the output name to a Numpy array. y can be 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, it will default to 32.
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: 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 and y 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. This will override validation_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 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().
initial_epoch: Epoch at which to start training (useful for resuming a previous training run).
steps_per_epoch: 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.

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)

Computes the loss on some input data, batch by batch.

Arguments

x: input data, as a Numpy array or list of Numpy arrays (if the model has multiple inputs). x can be None (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors).
y: labels, as a Numpy array. y can be None (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors).
batch_size: Integer. If unspecified, it will default to 32.
verbose: verbosity mode, 0 or 1.
sample_weight: sample weights, as a Numpy array.
steps: Integer or None. Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of None.

Returns

Scalar test loss (if the model has no metrics) or list of scalars (if the model computes other metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

Raises

RuntimeError: if the model was never compiled.

predict

predict(self, x, batch_size=None, verbose=0, steps=None)

Generates output predictions for the input samples.

The input samples are processed batch by batch.

Arguments

x: the input data, as a Numpy array.
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

A Numpy array of predictions.

train_on_batch

train_on_batch(self, x, y, class_weight=None, sample_weight=None)

Single gradient update over one batch of samples.

Arguments

x: input data, as a Numpy array or list of Numpy arrays (if the model has multiple inputs).
y: labels, as a Numpy array.
class_weight: dictionary mapping classes to a weight value, used for scaling the loss function (during training only).
sample_weight: sample weights, as a Numpy array.

Returns

Scalar training loss (if the model has no metrics) or list of scalars (if the model computes other metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

Raises

RuntimeError: if the model was never compiled.

test_on_batch

test_on_batch(self, x, y, sample_weight=None)

Evaluates the model over a single batch of samples.

Arguments

x: input data, as a Numpy array or list of Numpy arrays (if the model has multiple inputs).
y: labels, as a Numpy array.
sample_weight: sample weights, as a Numpy array.

Returns

Scalar test loss (if the model has no metrics) or list of scalars (if the model computes other metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

Raises

RuntimeError: if the model was never compiled.

predict_on_batch

predict_on_batch(self, x)

Returns predictions for a single batch of samples.

Arguments

x: input data, as a Numpy array or list of Numpy arrays (if the model has multiple inputs).

Returns

A Numpy array 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)

Fits the model on data generated batch-by-batch by a Python generator.

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.

Arguments

generator: A generator. The output of the generator must be either
a tuple (inputs, targets)
a tuple (inputs, targets, sample_weights). All arrays should contain the same number of samples. The generator is expected to loop over its data indefinitely. An epoch finishes when steps_per_epoch batches have been seen by the model.
steps_per_epoch: 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 for Sequence: if unspecified, will use the len(generator) as a number of steps.
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
A generator for the validation data
A tuple (inputs, targets)
A tuple (inputs, targets, sample_weights).
validation_steps: Only relevant if validation_data is a generator. Number of steps to yield from validation generator 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.
class_weight: Dictionary mapping class indices to a weight for the class.
max_queue_size: Maximum size for the generator queue
workers: Maximum number of processes to spin up
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.
shuffle: Whether to shuffle the order of the batches at the beginning of each epoch. Only used with instances of Sequence (keras.utils.Sequence).
initial_epoch: Epoch at which to start training (useful for resuming a previous training run).

Returns

A History object.

Raises

RuntimeError: if the model was never compiled.

Example

def generate_arrays_from_file(path):
    while 1:
        f = open(path)
        for line in f:
            # create Numpy arrays of input data
            # and labels, from each line in the file
            x, y = process_line(line)
            yield (x, y)
        f.close()

model.fit_generator(generate_arrays_from_file('/my_file.txt'),
                    steps_per_epoch=1000, epochs=10)

evaluate_generator

evaluate_generator(self, generator, steps=None, max_queue_size=10, workers=1, use_multiprocessing=False)

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)
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.
max_queue_size: maximum size for the generator queue
workers: maximum number of processes to spin up
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.

Returns

Scalar test loss (if the model has no metrics) or list of scalars (if the model computes other metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

Raises

RuntimeError: if the model was never compiled.

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.
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.
max_queue_size: maximum size for the generator queue
workers: maximum number of processes to spin up
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

A Numpy array of predictions.

get_layer

get_layer(self, name=None, index=None)

Retrieve a layer that is part of the model.

Returns a layer based on either its name (unique) or its index in the graph. 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.