# tf.keras.experimental.LinearModel |---|---| | [![](https://www.tensorflow.org/images/tf_logo_32px.png) TensorFlow 2 version](https://www.tensorflow.org/api_docs/python/tf/keras/experimental/LinearModel) | [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/premade/linear.py#L32-L164) | Linear Model for regression and classification problems. Inherits From: [`Model`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/Model) #### View aliases **Compat aliases for migration** See [Migration guide](https://www.tensorflow.org/guide/migrate) for more details. [`tf.compat.v1.keras.experimental.LinearModel`](https://www.tensorflow.org/api_docs/python/tf/keras/experimental/LinearModel), \`tf.compat.v2.keras.experimental.LinearModel\`
tf.keras.experimental.LinearModel( units=1, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, **kwargs ) This model approximates the following function: $$y = \\beta + \\sum_{i=1}\^{N} w_{i} \* x_{i}$$ where $$\\beta$$ is the bias and $$w_{i}$$ is the weight for each feature. #### Example: model = LinearModel() model.compile(optimizer='sgd', loss='mse') model.fit(x, y, epochs) This model accepts sparse float inputs as well: #### Example: model = LinearModel() opt = tf.keras.optimizers.Adam() loss_fn = tf.keras.losses.MeanSquaredError() with tf.GradientTape() as tape: output = model(sparse_input) loss = tf.reduce_mean(loss_fn(target, output)) grads = tape.gradient(loss, model.weights) opt.apply_gradients(zip(grads, model.weights))


| ## Args || |---|---| | `units` | Positive integer, output dimension without the batch size. | | `activation` | Activation function to use. If you don't specify anything, no activation is applied. | | `use_bias` | whether to calculate the bias/intercept for this model. If set to False, no bias/intercept will be used in calculations, e.g., the data is already centered. | | `kernel_initializer` | Initializer for the `kernel` weights matrices. | | `bias_initializer` | Initializer for the bias vector. | | `kernel_regularizer` | regularizer for kernel vectors. | | `bias_regularizer` | regularizer for bias vector. | | `**kwargs` | The keyword arguments that are passed on to BaseLayer.**init**. |



| ## 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` |

|
## Methods ### `compile` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/training.py#L184-L400) compile( optimizer='rmsprop', loss=None, metrics=None, loss_weights=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None, distribute=None, **kwargs ) Configures the model for training.


| Arguments || |---|---| | `optimizer` | String (name of optimizer) or optimizer instance. See [`tf.keras.optimizers`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/optimizers). | | `loss` | String (name of objective function), objective function or [`tf.losses.Loss`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/Loss) instance. See [`tf.losses`](https://www.tensorflow.org/versions/r1.15/api_docs/python/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. |



| Raises || |---|---| | `ValueError` | In case of invalid arguments for `optimizer`, `loss`, `metrics` or `sample_weight_mode`. |
### `evaluate` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/training.py#L729-L832) 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 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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/data) dataset. - A generator or [`keras.utils.Sequence`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/callbacks/Callback) instances. List of callbacks to apply during evaluation. See [callbacks](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks). | | `max_queue_size` | Integer. Used for generator or [`keras.utils.Sequence`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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 || |---|---| | 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 of invalid arguments. |
### `evaluate_generator` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/training.py#L1298-L1363) 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 data as accepted by `test_on_batch`.


| Arguments || |---|---| | `generator` | Generator yielding tuples (inputs, targets) or (inputs, targets, sample_weights) or an instance of [`keras.utils.Sequence`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/callbacks/Callback) instances. List of callbacks to apply during evaluation. See [callbacks](https://www.tensorflow.org/api_docs/python/tf/keras/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. |



| 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 of invalid arguments. |



| Raises || |---|---| | `ValueError` | In case the generator yields data in an invalid format. |
### `fit` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/training.py#L534-L727) 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, **kwargs ) 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 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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/data) dataset. Should return a tuple of either `(inputs, targets)` or `(inputs, targets, sample_weights)`. - A generator or [`keras.utils.Sequence`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/utils/Sequence) returning `(inputs, targets)` or `(inputs, targets, sample weights)`. | | `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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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, datasets, generators, or [`keras.utils.Sequence`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/utils/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` | 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (eg, in a production environment). | | `callbacks` | List of [`keras.callbacks.Callback`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/callbacks/Callback) instances. List of callbacks to apply during training. See [`tf.keras.callbacks`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/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 dataset, generator or [`keras.utils.Sequence`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/utils/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 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` is a dataset, generator, or [`keras.utils.Sequence`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/utils/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. If x is a [`tf.data`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/data) dataset, and 'steps_per_epoch' is None, the epoch will run until the input dataset is exhausted. This argument is not supported with array inputs. | | `validation_steps` | Only relevant if `validation_data` is provided and is a [`tf.data`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/data) dataset. Total number of steps (batches of samples) to draw before stopping when performing validation at the end of every epoch. If validation_data is a [`tf.data`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/data) dataset and 'validation_steps' is None, validation will run until the `validation_data` dataset is exhausted. | | `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. | | `max_queue_size` | Integer. Used for generator or [`keras.utils.Sequence`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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 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. |
### `fit_generator` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/training.py#L1162-L1296) 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 ) Fits the model on data yielded 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. The use of [`keras.utils.Sequence`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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) |


| Returns || |---|---| | A `History` object. ||
#### 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 x1, x2, y = process_line(line) yield ({'input_1': x1, 'input_2': x2}, {'output': y}) f.close() model.fit_generator(generate_arrays_from_file('/my_file.txt'), steps_per_epoch=10000, epochs=10) Raises: ValueError: In case the generator yields data in an invalid format. ### `get_layer` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/network.py#L500-L531) 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. |
### `load_weights` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/training.py#L175-L182) load_weights( filepath, by_name=False ) Loads all layer weights, either from a TensorFlow or an HDF5 file. ### `predict` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/training.py#L834-L908) 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 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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/data) dataset. - A generator or [`keras.utils.Sequence`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/data) dataset and `steps` is None, `predict` will run until the input dataset is exhausted. | | `callbacks` | List of [`keras.callbacks.Callback`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/callbacks/Callback) instances. List of callbacks to apply during prediction. See [callbacks](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks). | | `max_queue_size` | Integer. Used for generator or [`keras.utils.Sequence`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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. |
### `predict_generator` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/training.py#L1365-L1420) 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 by `predict_on_batch`.


| Arguments || |---|---| | `generator` | Generator yielding batches of input samples or an instance of [`keras.utils.Sequence`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/callbacks/Callback) instances. List of callbacks to apply during prediction. See [callbacks](https://www.tensorflow.org/api_docs/python/tf/keras/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. |



| Returns || |---|---| | Numpy array(s) of predictions. ||



| Raises || |---|---| | `ValueError` | In case the generator yields data in an invalid format. |
### `predict_on_batch` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/training.py#L1114-L1160) predict_on_batch( x ) Returns predictions for a single batch of samples.


| 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 [`tf.data`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/data) dataset. |


| Returns || |---|---| | Numpy array(s) of predictions. ||



| Raises || |---|---| | `ValueError` | In case of mismatch between given number of inputs and expectations of the model. |
### `reset_metrics` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/training.py#L910-L918) reset_metrics() Resets the state of metrics. ### `reset_states` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/network.py#L443-L446) reset_states() ### `save` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/network.py#L1122-L1171) save( filepath, overwrite=True, include_optimizer=True, save_format=None, signatures=None ) Saves the model to Tensorflow SavedModel or a single HDF5 file. #### The savefile includes: - The model architecture, allowing to re-instantiate the model. - The model weights. - The state of the optimizer, allowing to resume training exactly where you left off. This allows you to save the entirety of the state of a model in a single file. Saved models can be reinstantiated via [`keras.models.load_model`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/models/load_model). The model returned by `load_model` is a compiled model ready to be used (unless the saved model was never compiled in the first place).
| 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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/saved_model/save) for details. |
#### Example: from keras.models import load_model model.save('my_model.h5') # creates a HDF5 file 'my_model.h5' del model # deletes the existing model # returns a compiled model # identical to the previous one model = load_model('my_model.h5') ### `save_weights` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/network.py#L1173-L1292) save_weights( filepath, overwrite=True, save_format=None ) Saves all layer weights. Either saves in HDF5 or in TensorFlow format based on the `save_format` argument. When saving in HDF5 format, the weight file has: - `layer_names` (attribute), a list of strings (ordered names of model layers). - For every layer, a `group` named `layer.name` - For every such layer group, a group attribute `weight_names`, a list of strings (ordered names of weights tensor of the layer). - For every weight in the layer, a dataset storing the weight value, named after the weight tensor. When saving in TensorFlow format, all objects referenced by the network are saved in the same format as [`tf.train.Checkpoint`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/train/Checkpoint), including any `Layer` instances or `Optimizer` instances assigned to object attributes. For networks constructed from inputs and outputs using `tf.keras.Model(inputs, outputs)`, `Layer` instances used by the network are tracked/saved automatically. For user-defined classes which inherit from [`tf.keras.Model`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/Model), `Layer` instances must be assigned to object attributes, typically in the constructor. See the documentation of [`tf.train.Checkpoint`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/train/Checkpoint) and [`tf.keras.Model`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/Model) for details. While the formats are the same, do not mix `save_weights` and [`tf.train.Checkpoint`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/train/Checkpoint). Checkpoints saved by [`Model.save_weights`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/Model#save_weights) should be loaded using [`Model.load_weights`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/Model#load_weights). Checkpoints saved using [`tf.train.Checkpoint.save`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/train/Checkpoint#save) should be restored using the corresponding [`tf.train.Checkpoint.restore`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/train/Checkpoint#restore). Prefer [`tf.train.Checkpoint`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/train/Checkpoint) over `save_weights` for training checkpoints. The TensorFlow format matches objects and variables by starting at a root object, `self` for `save_weights`, and greedily matching attribute names. For [`Model.save`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/Model#save) this is the `Model`, and for [`Checkpoint.save`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/train/Checkpoint#save) this is the `Checkpoint` even if the `Checkpoint` has a model attached. This means saving a [`tf.keras.Model`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/Model) using `save_weights` and loading into a [`tf.train.Checkpoint`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/train/Checkpoint) with a `Model` attached (or vice versa) will not match the `Model`'s variables. See the [guide to training checkpoints](https://www.tensorflow.org/alpha/guide/checkpoints) for details on the TensorFlow format.


| 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. |
### `summary` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/network.py#L1434-L1461) summary( line_length=None, positions=None, print_fn=None ) Prints a string summary of the network.


| 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. |



| Raises || |---|---| | `ValueError` | if `summary()` is called before the model is built. |
### `test_on_batch` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/training.py#L1026-L1112) test_on_batch( x, y=None, sample_weight=None, reset_metrics=True ) Test the model on a single batch of samples.


| 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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/data) dataset. | | `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 `y` should not be specified (since targets will be obtained from the iterator). | | `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(). This argument is not supported when `x` is a dataset. | | `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 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 of invalid user-provided arguments. |
### `to_json` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/network.py#L1392-L1407) to_json( **kwargs ) Returns a JSON string containing the network configuration. To load a network from a JSON save file, use [`keras.models.model_from_json(json_string, custom_objects={})`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/models/model_from_json).


| Arguments || |---|---| | `**kwargs` | Additional keyword arguments to be passed to `json.dumps()`. |



| Returns || |---|---| | A JSON string. ||
### `to_yaml` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/network.py#L1409-L1432) to_yaml( **kwargs ) Returns a yaml string containing the network configuration. To load a network from a yaml save file, use [`keras.models.model_from_yaml(yaml_string, custom_objects={})`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/models/model_from_yaml). `custom_objects` should be a dictionary mapping the names of custom losses / layers / etc to the corresponding functions / classes.


| Arguments || |---|---| | `**kwargs` | Additional keyword arguments to be passed to `yaml.dump()`. |



| Returns || |---|---| | A YAML string. ||



| Raises || |---|---| | `ImportError` | if yaml module is not found. |
### `train_on_batch` [View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/engine/training.py#L920-L1024) train_on_batch( x, y=None, sample_weight=None, class_weight=None, reset_metrics=True ) Runs a single gradient update on a single batch of data.


| 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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/data) dataset. | | `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, `y` should not be specified (since targets will be obtained from the iterator). | | `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(). This argument is not supported when `x` is a dataset. | | `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 outputs and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs. ||



| Raises || |---|---| | `ValueError` | In case of invalid user-provided arguments. |