tf.keras.Model

TensorFlow 1 version

View source on GitHub

Model groups layers into an object with training and inference features.

View aliases

Main aliases

tf.keras.models.Model

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.Model, tf.compat.v1.keras.models.Model

tf.keras.Model(
    *args, **kwargs
)

There are two ways to instantiate a Model:

1 - With the "functional API", where you start from Input, you chain layer calls to specify the model's forward pass, and finally you create your model from inputs and outputs:

import tensorflow as tf

inputs = tf.keras.Input(shape=(3,))
x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)

2 - By subclassing the Model class: in that case, you should define your layers in __init__ and you should implement the model's forward pass in call.

import tensorflow as tf

class MyModel(tf.keras.Model):

  def __init__(self):
    super(MyModel, self).__init__()
    self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
    self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)

  def call(self, inputs):
    x = self.dense1(inputs)
    return self.dense2(x)

model = MyModel()

If you subclass Model, you can optionally have a training argument (boolean) in call, which you can use to specify a different behavior in training and inference:

import tensorflow as tf

class MyModel(tf.keras.Model):

  def __init__(self):
    super(MyModel, self).__init__()
    self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
    self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
    self.dropout = tf.keras.layers.Dropout(0.5)

  def call(self, inputs, training=False):
    x = self.dense1(inputs)
    if training:
      x = self.dropout(x, training=training)
    return self.dense2(x)

model = MyModel()

Attributes
`layers`
`metrics_names`	Returns the model's display labels for all outputs.
`run_eagerly`	Settable attribute indicating whether the model should run eagerly. Running eagerly means that your model will be run step by step, like Python code. Your model might run slower, but it should become easier for you to debug it by stepping into individual layer calls. By default, we will attempt to compile your model to a static graph to deliver the best execution performance.
`sample_weights`
`state_updates`	Returns the `updates` from all layers that are stateful. This is useful for separating training updates and state updates, e.g. when we need to update a layer's internal state during prediction.
`stateful`

Arguments
`optimizer`	String (name of optimizer) or optimizer instance. See `tf.keras.optimizers`.
`loss`	String (name of objective function), objective function or `tf.losses.Loss` instance. See `tf.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 tensor, 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.
`distribute`	NOT SUPPORTED IN TF 2.0, please create and compile the model under distribution strategy scope instead of passing it to compile.
`**kwargs`	Any additional arguments.

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 TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). A dict mapping input names to the corresponding array/tensors, if the model has named inputs. A `tf.data` dataset. A generator or `keras.utils.Sequence` instance.
`y`	Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with `x` (you cannot have Numpy inputs and tensor targets, or inversely). If `x` is a dataset, generator or `keras.utils.Sequence` instance, `y` should not be specified (since targets will be obtained from the iterator/dataset).
`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, dataset, 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()`. This argument is not supported when `x` is a dataset, instead pass sample weights as the third element of `x`.
`steps`	Integer or `None`. Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of `None`. If x is a `tf.data` dataset and `steps` is None, 'evaluate' will run until the dataset is exhausted. This argument is not supported with array inputs.
`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.

Arguments
`generator`	Generator yielding tuples (inputs, targets) or (inputs, targets, sample_weights) or an instance of `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 evaluation. 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`	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.
`verbose`	Verbosity mode, 0 or 1.

Raises
`RuntimeError`	If the model was never compiled.
`ValueError`	In case of mismatch between the provided input data and what the model expects.

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.
`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.
`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. Total number of steps (batches of samples) to yield from `generator` before stopping. 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_abc.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`	Dictionary mapping class indices to a weight for the 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`	Epoch at which to start training (useful for resuming a previous training run)

Arguments
`x`	Input samples. It could be: A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). A `tf.data` dataset. A generator or `keras.utils.Sequence` instance.
`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, dataset, 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`. If x is a `tf.data` dataset and `steps` is None, `predict` will run until the input dataset is exhausted.
`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.

Arguments
`generator`	Generator yielding batches of input samples or an instance of `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 prediction. 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`	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.
`verbose`	verbosity mode, 0 or 1.

Arguments
filepath: String, path to SavedModel or H5 file to save the model. overwrite: Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt. include_optimizer: If True, save optimizer's state together. save_format: Either 'tf' or 'h5', indicating whether to save the model to Tensorflow SavedModel or HDF5. The default is currently 'h5', but will switch to 'tf' in TensorFlow 2.0. The 'tf' option is currently disabled (use `tf.keras.experimental.export_saved_model` instead).
`signatures`	Signatures to save with the SavedModel. Applicable to the 'tf' format only. Please see the `signatures` argument in `tf.saved_model.save` for details.
`options`	Optional `tf.saved_model.SaveOptions` object that specifies options for saving to SavedModel.

Arguments
`filepath`	String, path to the file to save the weights to. When saving in TensorFlow format, this is the prefix used for checkpoint files (multiple files are generated). Note that the '.h5' suffix causes weights to be saved in HDF5 format.
`overwrite`	Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt.
`save_format`	Either 'tf' or 'h5'. A `filepath` ending in '.h5' or '.keras' will default to HDF5 if `save_format` is `None`. Otherwise `None` defaults to 'tf'.

Raises
`ImportError`	If h5py is not available when attempting to save in HDF5 format.
`ValueError`	For invalid/unknown format arguments.

Arguments
`name`	String, name of layer.
`index`	Integer, index of layer.

Arguments
`line_length`	Total length of printed lines (e.g. set this to adapt the display to different terminal window sizes).
`positions`	Relative or absolute positions of log elements in each line. If not provided, defaults to `[.33, .55, .67, 1.]`.
`print_fn`	Print function to use. Defaults to `print`. It will be called on each line of the summary. You can set it to a custom function in order to capture the string summary.

tf.keras.Model

View aliases

Attributes

Methods

compile

evaluate

evaluate_generator

fit

fit_generator

Example:

get_layer

load_weights

predict

predict_generator

predict_on_batch

reset_metrics

reset_states

save

The savefile includes:

Example:

save_weights

summary

test_on_batch

to_json

to_yaml

train_on_batch

`compile`

`evaluate`

`evaluate_generator`

`fit`

`fit_generator`

`get_layer`

`load_weights`

`predict`

`predict_generator`

`predict_on_batch`

`reset_metrics`

`reset_states`

`save`

`save_weights`

`summary`

`test_on_batch`

`to_json`

`to_yaml`

`train_on_batch`