keras_model.py

import numpy as np
import tensorflow as tf
import time
import logging
import os

from collections.abc import Sequence as SequenceCollection

from deepchem.data import Dataset, NumpyDataset
from deepchem.metrics import Metric
from deepchem.models.losses import Loss
from deepchem.models.models import Model
from deepchem.models.optimizers import Adam, Optimizer, LearningRateSchedule
from deepchem.trans import Transformer, undo_transforms
from deepchem.utils.evaluate import GeneratorEvaluator

from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
from deepchem.utils.typing import LossFn, OneOrMany
from deepchem.models.wandblogger import WandbLogger

try:
    import wandb
    wandb.ensure_configured()
    if wandb.api.api_key is None:
        _has_wandb = False
        wandb.termwarn(
            "W&B installed but not logged in.  Run `wandb login` or set the WANDB_API_KEY env variable."
        )
    else:
        _has_wandb = True
except (ImportError, AttributeError):
    _has_wandb = False

logger = logging.getLogger(__name__)


class KerasModel(Model):
    """This is a DeepChem model implemented by a Keras model.

    This class provides several advantages over using the Keras
    model's fitting and prediction methods directly.

    1. It provides better integration with the rest of DeepChem,
        such as direct support for Datasets and Transformers.

    2. It defines the loss in a more flexible way.  In particular,
        Keras does not support multidimensional weight matrices,
        which makes it impossible to implement most multitask
        models with Keras.

    3. It provides various additional features not found in the
        Keras model class, such as uncertainty prediction and
        saliency mapping.

    Here is a simple example of code that uses KerasModel to train
    a Keras model on a DeepChem dataset.

    >> keras_model = tf.keras.Sequential([
    >>    tf.keras.layers.Dense(1000, activation='tanh'),
    >>    tf.keras.layers.Dense(1)
    >> ])
    >> model = KerasModel(keras_model, loss=dc.models.losses.L2Loss())
    >> model.fit(dataset)

    The loss function for a model can be defined in two different
    ways.  For models that have only a single output and use a
    standard loss function, you can simply provide a
    dc.models.losses.Loss object.  This defines the loss for each
    sample or sample/task pair.  The result is automatically
    multiplied by the weights and averaged over the batch.  Any
    additional losses computed by model layers, such as weight
    decay penalties, are also added.

    For more complicated cases, you can instead provide a function
    that directly computes the total loss.  It must be of the form
    f(outputs, labels, weights), taking the list of outputs from
    the model, the expected values, and any weight matrices.  It
    should return a scalar equal to the value of the loss function
    for the batch.  No additional processing is done to the
    result; it is up to you to do any weighting, averaging, adding
    of penalty terms, etc.

    You can optionally provide an output_types argument, which
    describes how to interpret the model's outputs.  This should
    be a list of strings, one for each output. You can use an
    arbitrary output_type for a output, but some output_types are
    special and will undergo extra processing:

    - 'prediction': This is a normal output, and will be returned by predict().
        If output types are not specified, all outputs are assumed
        to be of this type.

    - 'loss': This output will be used in place of the normal
        outputs for computing the loss function.  For example,
        models that output probability distributions usually do it
        by computing unbounded numbers (the logits), then passing
        them through a softmax function to turn them into
        probabilities.  When computing the cross entropy, it is more
        numerically stable to use the logits directly rather than
        the probabilities.  You can do this by having the model
        produce both probabilities and logits as outputs, then
        specifying output_types=['prediction', 'loss'].  When
        predict() is called, only the first output (the
        probabilities) will be returned.  But during training, it is
        the second output (the logits) that will be passed to the
        loss function.

    - 'variance': This output is used for estimating the
        uncertainty in another output.  To create a model that can
        estimate uncertainty, there must be the same number of
        'prediction' and 'variance' outputs.  Each variance output
        must have the same shape as the corresponding prediction
        output, and each element is an estimate of the variance in
        the corresponding prediction.  Also be aware that if a model
        supports uncertainty, it MUST use dropout on every layer,
        and dropout most be enabled during uncertainty prediction.
        Otherwise, the uncertainties it computes will be inaccurate.

    - other: Arbitrary output_types can be used to extract outputs
        produced by the model, but will have no additional
        processing performed.
    """

    def __init__(self,
                 model: tf.keras.Model,
                 loss: Union[Loss, LossFn],
                 output_types: Optional[List[str]] = None,
                 batch_size: int = 100,
                 model_dir: Optional[str] = None,
                 learning_rate: Union[float, LearningRateSchedule] = 0.001,
                 optimizer: Optional[Optimizer] = None,
                 tensorboard: bool = False,
                 wandb: bool = False,
                 log_frequency: int = 100,
                 wandb_logger: Optional[WandbLogger] = None,
                 **kwargs) -> None:
        """Create a new KerasModel.

        Parameters
        ----------
        model: tf.keras.Model
            the Keras model implementing the calculation
        loss: dc.models.losses.Loss or function
            a Loss or function defining how to compute the training loss for each
            batch, as described above
        output_types: list of strings
            the type of each output from the model, as described above
        batch_size: int
            default batch size for training and evaluating
        model_dir: str
            the directory on disk where the model will be stored.  If this is None,
            a temporary directory is created.
        learning_rate: float or LearningRateSchedule
            the learning rate to use for fitting.  If optimizer is specified, this is
            ignored.
        optimizer: Optimizer
            the optimizer to use for fitting.  If this is specified, learning_rate is
            ignored.
        tensorboard: bool
            whether to log progress to TensorBoard during training
        wandb: bool
            whether to log progress to Weights & Biases during training (deprecated)
        log_frequency: int
            The frequency at which to log data. Data is logged using
            `logging` by default. If `tensorboard` is set, data is also
            logged to TensorBoard. If `wandb` is set, data is also logged
            to Weights & Biases. Logging happens at global steps. Roughly,
            a global step corresponds to one batch of training. If you'd
            like a printout every 10 batch steps, you'd set
            `log_frequency=10` for example.
        wandb_logger: WandbLogger
            the Weights & Biases logger object used to log data and metrics
        """
        super(KerasModel, self).__init__(model=model,
                                         model_dir=model_dir,
                                         **kwargs)
        self.loss = loss  # not used
        self.learning_rate = learning_rate  # not used
        self.output_types = output_types  # not used
        if isinstance(loss, Loss):
            self._loss_fn: LossFn = _StandardLoss(model, loss)
        else:
            self._loss_fn = loss
        self.batch_size = batch_size
        if optimizer is None:
            self.optimizer: Optimizer = Adam(learning_rate=learning_rate)
        else:
            self.optimizer = optimizer
        self.tensorboard = tensorboard

        # W&B flag support (DEPRECATED)
        if wandb:
            logger.warning(
                "`wandb` argument is deprecated. Please use `wandb_logger` instead. "
                "This argument will be removed in a future release of DeepChem."
            )
        if wandb and not _has_wandb:
            logger.warning(
                "You set wandb to True but W&B is not installed. To use wandb logging, "
                "run `pip install wandb; wandb login`")
        self.wandb = wandb and _has_wandb

        self.wandb_logger = wandb_logger
        # If `wandb=True` and no logger is provided, initialize default logger
        if self.wandb and (self.wandb_logger is None):
            self.wandb_logger = WandbLogger()

        # Setup and initialize W&B logging
        if (self.wandb_logger
                is not None) and (not self.wandb_logger.initialized):
            self.wandb_logger.setup()

        # Update config with KerasModel params
        wandb_logger_config = dict(loss=loss,
                                   output_types=output_types,
                                   batch_size=batch_size,
                                   model_dir=model_dir,
                                   learning_rate=learning_rate,
                                   optimizer=optimizer,
                                   tensorboard=tensorboard,
                                   log_frequency=log_frequency)
        wandb_logger_config.update(**kwargs)

        if self.wandb_logger is not None:
            self.wandb_logger.update_config(wandb_logger_config)

        # Backwards compatibility
        if "tensorboard_log_frequency" in kwargs:
            logger.warning(
                "tensorboard_log_frequency is deprecated. Please use log_frequency instead. This argument will be removed in a future release of DeepChem."
            )
            self.log_frequency = kwargs["tensorboard_log_frequency"]
        else:
            self.log_frequency = log_frequency
        if self.tensorboard:
            self._summary_writer = tf.summary.create_file_writer(self.model_dir)
        if output_types is None:
            self._prediction_outputs = None
            self._loss_outputs = None
            self._variance_outputs = None
            self._other_outputs = None
        else:
            self._prediction_outputs = []
            self._loss_outputs = []
            self._variance_outputs = []
            self._other_outputs = []
            for i, type in enumerate(output_types):
                if type == 'prediction':
                    self._prediction_outputs.append(i)
                elif type == 'loss':
                    self._loss_outputs.append(i)
                elif type == 'variance':
                    self._variance_outputs.append(i)
                else:
                    self._other_outputs.append(i)
            if len(self._loss_outputs) == 0:
                self._loss_outputs = self._prediction_outputs
        self._built = False
        self._inputs_built = False
        self._training_ops_built = False
        self._output_functions: Dict[Any, Any] = {}
        self._gradient_fn_for_vars: Dict[Any, Any] = {}

    def _ensure_built(self) -> None:
        """The first time this is called, create internal data structures."""
        if self._built:
            return
        self._built = True
        self._global_step = tf.Variable(0, trainable=False)
        self._tf_optimizer = self.optimizer._create_tf_optimizer(
            self._global_step)
        self._checkpoint = tf.train.Checkpoint(optimizer=self._tf_optimizer,
                                               model=self.model)

    def _create_inputs(self, example_inputs: List) -> None:
        """The first time this is called, create tensors representing the inputs and outputs."""
        if self._inputs_built:
            return
        self._ensure_built()
        self._inputs_built = True
        if (self.model.inputs is not None) and len(self.model.inputs) > 0:
            self._input_shapes = [t.shape for t in self.model.inputs]
            self._input_dtypes = [
                t.dtype.as_numpy_dtype for t in self.model.inputs
            ]
        else:
            self._input_shapes = [(None,) + i.shape[1:] for i in example_inputs]
            self._input_dtypes = [
                np.float32 if x.dtype == np.float64 else x.dtype
                for x in example_inputs
            ]

    def _create_training_ops(self, example_batch: Tuple[List, List,
                                                        List]) -> None:
        """The first time this is called, create tensors used in optimization."""
        if self._training_ops_built:
            return
        self._create_inputs(example_batch[0])
        self._training_ops_built = True
        self._label_dtypes = [
            np.float32 if x.dtype == np.float64 else x.dtype
            for x in example_batch[1]
        ]
        self._weights_dtypes = [
            np.float32 if x.dtype == np.float64 else x.dtype
            for x in example_batch[2]
        ]

    def fit(self,
            dataset: Dataset,
            nb_epoch: int = 10,
            max_checkpoints_to_keep: int = 5,
            checkpoint_interval: int = 1000,
            deterministic: bool = False,
            restore: bool = False,
            variables: Optional[List[tf.Variable]] = None,
            loss: Optional[LossFn] = None,
            callbacks: Union[Callable, List[Callable]] = [],
            all_losses: Optional[List[float]] = None) -> float:
        """Train this model on a dataset.

        Parameters
        ----------
        dataset: Dataset
            the Dataset to train on
        nb_epoch: int
            the number of epochs to train for
        max_checkpoints_to_keep: int
            the maximum number of checkpoints to keep.  Older checkpoints are discarded.
        checkpoint_interval: int
            the frequency at which to write checkpoints, measured in training steps.
            Set this to 0 to disable automatic checkpointing.
        deterministic: bool
            if True, the samples are processed in order.  If False, a different random
            order is used for each epoch.
        restore: bool
            if True, restore the model from the most recent checkpoint and continue training
            from there.  If False, retrain the model from scratch.
        variables: list of tf.Variable
            the variables to train.  If None (the default), all trainable variables in
            the model are used.
        loss: function
            a function of the form f(outputs, labels, weights) that computes the loss
            for each batch.  If None (the default), the model's standard loss function
            is used.
        callbacks: function or list of functions
            one or more functions of the form f(model, step) that will be invoked after
            every step.  This can be used to perform validation, logging, etc.
        all_losses: Optional[List[float]], optional (default None)
            If specified, all logged losses are appended into this list. Note that
            you can call `fit()` repeatedly with the same list and losses will
            continue to be appended.

        Returns
        -------
        float
            The average loss over the most recent checkpoint interval
        """
        return self.fit_generator(
            self.default_generator(dataset,
                                   epochs=nb_epoch,
                                   deterministic=deterministic),
            max_checkpoints_to_keep, checkpoint_interval, restore, variables,
            loss, callbacks, all_losses)

    def fit_generator(self,
                      generator: Iterable[Tuple[Any, Any, Any]],
                      max_checkpoints_to_keep: int = 5,
                      checkpoint_interval: int = 1000,
                      restore: bool = False,
                      variables: Optional[List[tf.Variable]] = None,
                      loss: Optional[LossFn] = None,
                      callbacks: Union[Callable, List[Callable]] = [],
                      all_losses: Optional[List[float]] = None) -> float:
        """Train this model on data from a generator.

        Parameters
        ----------
        generator: generator
            this should generate batches, each represented as a tuple of the form
            (inputs, labels, weights).
        max_checkpoints_to_keep: int
            the maximum number of checkpoints to keep.  Older checkpoints are discarded.
        checkpoint_interval: int
            the frequency at which to write checkpoints, measured in training steps.
            Set this to 0 to disable automatic checkpointing.
        restore: bool
            if True, restore the model from the most recent checkpoint and continue training
            from there.  If False, retrain the model from scratch.
        variables: list of tf.Variable
            the variables to train.  If None (the default), all trainable variables in
            the model are used.
        loss: function
            a function of the form f(outputs, labels, weights) that computes the loss
            for each batch.  If None (the default), the model's standard loss function
            is used.
        callbacks: function or list of functions
            one or more functions of the form f(model, step) that will be invoked after
            every step.  This can be used to perform validation, logging, etc.
        all_losses: Optional[List[float]], optional (default None)
            If specified, all logged losses are appended into this list. Note that
            you can call `fit()` repeatedly with the same list and losses will
            continue to be appended.

        Returns
        -------
        float
            The average loss over the most recent checkpoint interval
        """
        if not isinstance(callbacks, SequenceCollection):
            callbacks = [callbacks]
        self._ensure_built()
        if checkpoint_interval > 0:
            manager = tf.train.CheckpointManager(self._checkpoint,
                                                 self.model_dir,
                                                 max_checkpoints_to_keep)
        avg_loss = 0.0
        last_avg_loss = 0.0
        averaged_batches = 0
        if loss is None:
            loss = self._loss_fn
        var_key = None
        if variables is not None:
            var_key = tuple(v.ref() for v in variables)

            # The optimizer creates internal variables the first time apply_gradients()
            # is called for a new set of variables.  If that happens inside a function
            # annotated with tf.function it throws an exception, so call it once here.

            zero_grads = [tf.zeros(v.shape) for v in variables]
            self._tf_optimizer.apply_gradients(zip(zero_grads, variables))
        if var_key not in self._gradient_fn_for_vars:
            self._gradient_fn_for_vars[var_key] = self._create_gradient_fn(
                variables)
        apply_gradient_for_batch = self._gradient_fn_for_vars[var_key]
        time1 = time.time()

        # Main training loop.

        for batch in generator:
            self._create_training_ops(batch)
            if restore:
                self.restore()
                restore = False
            inputs, labels, weights = self._prepare_batch(batch)

            # Execute the loss function, accumulating the gradients.

            if len(inputs) == 1:
                inputs = inputs[0]

            batch_loss = apply_gradient_for_batch(inputs, labels, weights, loss)
            current_step = self._global_step.numpy()

            avg_loss += batch_loss

            # Report progress and write checkpoints.
            averaged_batches += 1
            should_log = (current_step % self.log_frequency == 0)
            if should_log:
                avg_loss = float(avg_loss) / averaged_batches
                logger.info('Ending global_step %d: Average loss %g' %
                            (current_step, avg_loss))
                if all_losses is not None:
                    all_losses.append(avg_loss)
                # Capture the last avg_loss in case of return since we're resetting to
                # 0 now
                last_avg_loss = avg_loss
                avg_loss = 0.0
                averaged_batches = 0

            if checkpoint_interval > 0 and current_step % checkpoint_interval == checkpoint_interval - 1:
                manager.save()
            for c in callbacks:
                c(self, current_step)
            if self.tensorboard and should_log:
                self._log_scalar_to_tensorboard('loss', batch_loss,
                                                current_step)
            if (self.wandb_logger is not None) and should_log:
                all_data = dict({'train/loss': batch_loss})
                self.wandb_logger.log_data(all_data, step=current_step)

        # Report final results.
        if averaged_batches > 0:
            avg_loss = float(avg_loss) / averaged_batches
            logger.info('Ending global_step %d: Average loss %g' %
                        (current_step, avg_loss))
            if all_losses is not None:
                all_losses.append(avg_loss)
            last_avg_loss = avg_loss

        if checkpoint_interval > 0:
            manager.save()

        time2 = time.time()
        logger.info("TIMING: model fitting took %0.3f s" % (time2 - time1))
        return last_avg_loss

    def _create_gradient_fn(self,
                            variables: Optional[List[tf.Variable]]) -> Callable:
        """Create a function that computes gradients and applies them to the model.
        Because of the way TensorFlow function tracing works, we need to create a
        separate function for each new set of variables.
        """

        @tf.function(experimental_relax_shapes=True)
        def apply_gradient_for_batch(inputs, labels, weights, loss):
            with tf.GradientTape() as tape:
                outputs = self.model(inputs, training=True)
                if tf.is_tensor(outputs):
                    outputs = [outputs]
                if self._loss_outputs is not None:
                    outputs = [outputs[i] for i in self._loss_outputs]
                batch_loss = loss(outputs, labels, weights)
            if variables is None:
                vars = self.model.trainable_variables
            else:
                vars = variables
            grads = tape.gradient(batch_loss, vars)
            self._tf_optimizer.apply_gradients(zip(grads, vars))
            self._global_step.assign_add(1)
            return batch_loss

        return apply_gradient_for_batch

    def fit_on_batch(self,
                     X: Sequence,
                     y: Sequence,
                     w: Sequence,
                     variables: Optional[List[tf.Variable]] = None,
                     loss: Optional[LossFn] = None,
                     callbacks: Union[Callable, List[Callable]] = [],
                     checkpoint: bool = True,
                     max_checkpoints_to_keep: int = 5) -> float:
        """Perform a single step of training.

        Parameters
        ----------
        X: ndarray
            the inputs for the batch
        y: ndarray
            the labels for the batch
        w: ndarray
            the weights for the batch
        variables: list of tf.Variable
            the variables to train.  If None (the default), all trainable variables in
            the model are used.
        loss: function
            a function of the form f(outputs, labels, weights) that computes the loss
            for each batch.  If None (the default), the model's standard loss function
            is used.
        callbacks: function or list of functions
            one or more functions of the form f(model, step) that will be invoked after
            every step.  This can be used to perform validation, logging, etc.
        checkpoint: bool
            if true, save a checkpoint after performing the training step
        max_checkpoints_to_keep: int
            the maximum number of checkpoints to keep.  Older checkpoints are discarded.

        Returns
        -------
        float
            the loss on the batch
        """
        self._ensure_built()
        dataset = NumpyDataset(X, y, w)
        return self.fit(dataset,
                        nb_epoch=1,
                        max_checkpoints_to_keep=max_checkpoints_to_keep,
                        checkpoint_interval=self._global_step.numpy() +
                        2 if checkpoint else 0,
                        variables=variables,
                        loss=loss,
                        callbacks=callbacks)

    def _predict(self, generator: Iterable[Tuple[Any, Any, Any]],
                 transformers: List[Transformer],
                 outputs: Optional[OneOrMany[tf.Tensor]], uncertainty: bool,
                 other_output_types: Optional[OneOrMany[str]]):
        """
        Predict outputs for data provided by a generator.

        This is the private implementation of prediction.  Do not
        call it directly.  Instead call one of the public prediction
        methods.

        Parameters
        ----------
        generator: generator
            this should generate batches, each represented as a tuple of the form
            (inputs, labels, weights).
        transformers: list of dc.trans.Transformers
            Transformers that the input data has been transformed by.  The output
            is passed through these transformers to undo the transformations.
        outputs: Tensor or list of Tensors
            The outputs to return.  If this is None, the model's standard prediction
            outputs will be returned.  Alternatively one or more Tensors within the
            model may be specified, in which case the output of those Tensors will be
            returned.
        uncertainty: bool
            specifies whether this is being called as part of estimating uncertainty.
            If True, it sets the training flag so that dropout will be enabled, and
            returns the values of the uncertainty outputs.
        other_output_types: list, optional
            Provides a list of other output_types (strings) to predict from model.

        Returns
        -------
        a NumPy array of the model produces a single output, or a list of arrays
        if it produces multiple outputs
        """
        results: Optional[List[List[np.ndarray]]] = None
        variances: Optional[List[List[np.ndarray]]] = None
        if (outputs is not None) and (other_output_types is not None):
            raise ValueError(
                'This model cannot compute outputs and other output_types simultaneously.'
                'Please invoke one at a time.')
        if uncertainty and (other_output_types is not None):
            raise ValueError(
                'This model cannot compute uncertainties and other output types simultaneously.'
                'Please invoke one at a time.')
        if uncertainty:
            assert outputs is None
            if self._variance_outputs is None or len(
                    self._variance_outputs) == 0:
                raise ValueError('This model cannot compute uncertainties')
            if len(self._variance_outputs) != len(self._prediction_outputs):
                raise ValueError(
                    'The number of variances must exactly match the number of outputs'
                )
        if other_output_types:
            assert outputs is None
            if self._other_outputs is None or len(self._other_outputs) == 0:
                raise ValueError(
                    'This model cannot compute other outputs since no other output_types were specified.'
                )
        if (outputs is not None and self.model.inputs is not None and
                len(self.model.inputs) == 0):
            raise ValueError(
                "Cannot use 'outputs' argument with a model that does not specify its inputs."
                "Note models defined in imperative subclassing style cannot specify outputs"
            )
        if tf.is_tensor(outputs):
            outputs = [outputs]
        for batch in generator:
            inputs, labels, weights = batch
            self._create_inputs(inputs)
            inputs, _, _ = self._prepare_batch((inputs, None, None))

            # Invoke the model.
            if len(inputs) == 1:
                inputs = inputs[0]
            if outputs is not None:
                outputs = tuple(outputs)
                key = tuple(t.ref() for t in outputs)
                if key not in self._output_functions:
                    self._output_functions[key] = tf.keras.backend.function(
                        self.model.inputs, outputs)
                output_values = self._output_functions[key](inputs)
            else:
                output_values = self._compute_model(inputs)
                if tf.is_tensor(output_values):
                    output_values = [output_values]
                output_values = [t.numpy() for t in output_values]

            # Apply tranformers and record results.
            if uncertainty:
                var = [output_values[i] for i in self._variance_outputs]
                if variances is None:
                    variances = [var]
                else:
                    for i, t in enumerate(var):
                        variances[i].append(t)
            access_values = []
            if other_output_types:
                access_values += self._other_outputs
            elif self._prediction_outputs is not None:
                access_values += self._prediction_outputs

            if len(access_values) > 0:
                output_values = [output_values[i] for i in access_values]

            if len(transformers) > 0:
                if len(output_values) > 1:
                    raise ValueError(
                        "predict() does not support Transformers for models with multiple outputs."
                    )
                elif len(output_values) == 1:
                    output_values = [
                        undo_transforms(output_values[0], transformers)
                    ]
            if results is None:
                results = [[] for i in range(len(output_values))]
            for i, t in enumerate(output_values):
                results[i].append(t)

        # Concatenate arrays to create the final results.
        final_results = []
        final_variances = []
        if results is not None:
            for r in results:
                final_results.append(np.concatenate(r, axis=0))
        if uncertainty and variances is not None:
            for v in variances:
                final_variances.append(np.concatenate(v, axis=0))
            return zip(final_results, final_variances)
        if len(final_results) == 1:
            return final_results[0]
        else:
            return final_results

    @tf.function(experimental_relax_shapes=True)
    def _compute_model(self, inputs: Sequence):
        """Evaluate the model for a set of inputs."""
        return self.model(inputs, training=False)

    def predict_on_generator(
            self,
            generator: Iterable[Tuple[Any, Any, Any]],
            transformers: List[Transformer] = [],
            outputs: Optional[OneOrMany[tf.Tensor]] = None,
            output_types: Optional[OneOrMany[str]] = None
    ) -> OneOrMany[np.ndarray]:
        """
        Parameters
        ----------
        generator: generator
            this should generate batches, each represented as a tuple of the form
            (inputs, labels, weights).
        transformers: list of dc.trans.Transformers
            Transformers that the input data has been transformed by.  The output
            is passed through these transformers to undo the transformations.
        outputs: Tensor or list of Tensors
            The outputs to return.  If this is None, the model's
            standard prediction outputs will be returned.
            Alternatively one or more Tensors within the model may be
            specified, in which case the output of those Tensors will
            be returned. If outputs is specified, output_types must be
            None.
        output_types: String or list of Strings
            If specified, all outputs of this type will be retrieved
            from the model. If output_types is specified, outputs must
            be None.

        Returns
        -------
        OneOrMany[np.ndarray]
            a NumPy array of the model produces a single output, or a list of arrays
            if it produces multiple outputs
        """
        return self._predict(generator, transformers, outputs, False,
                             output_types)

    def predict_on_batch(
        self,
        X: np.typing.ArrayLike,
        transformers: List[Transformer] = [],
        outputs: Optional[OneOrMany[tf.Tensor]] = None
    ) -> OneOrMany[np.ndarray]:
        """Generates predictions for input samples, processing samples in a batch.

        Parameters
        ----------
        X: ndarray
            the input data, as a Numpy array.
        transformers: list of dc.trans.Transformers
            Transformers that the input data has been transformed by.  The output
            is passed through these transformers to undo the transformations.
        outputs: Tensor or list of Tensors
            The outputs to return.  If this is None, the model's standard prediction
            outputs will be returned.  Alternatively one or more Tensors within the
            model may be specified, in which case the output of those Tensors will be
            returned.

        Returns
        -------
        OneOrMany[np.ndarray]
            a NumPy array of the model produces a single output, or a list of arrays
            if it produces multiple outputs
        """
        dataset = NumpyDataset(X=X, y=None)
        return self.predict(dataset, transformers, outputs)

    def predict_uncertainty_on_batch(
            self,
            X: Sequence,
            masks: int = 50) -> OneOrMany[Tuple[np.ndarray, np.ndarray]]:
        """
        Predict the model's outputs, along with the uncertainty in each one.

        The uncertainty is computed as described in https://arxiv.org/abs/1703.04977.
        It involves repeating the prediction many times with different dropout masks.
        The prediction is computed as the average over all the predictions.  The
        uncertainty includes both the variation among the predicted values (epistemic
        uncertainty) and the model's own estimates for how well it fits the data
        (aleatoric uncertainty).  Not all models support uncertainty prediction.

        Parameters
        ----------
        X: ndarray
            the input data, as a Numpy array.
        masks: int
            the number of dropout masks to average over

        Returns
        -------
        OneOrMany[Tuple[y_pred, y_std]]
        y_pred: np.ndarray
            predicted value of the output
        y_std: np.ndarray
            standard deviation of the corresponding element of y_pred
        """
        dataset = NumpyDataset(X=X, y=None)
        return self.predict_uncertainty(dataset, masks)

    def predict(
            self,
            dataset: Dataset,
            transformers: List[Transformer] = [],
            outputs: Optional[OneOrMany[tf.Tensor]] = None,
            output_types: Optional[List[str]] = None) -> OneOrMany[np.ndarray]:
        """
        Uses self to make predictions on provided Dataset object.

        Parameters
        ----------
        dataset: dc.data.Dataset
            Dataset to make prediction on
        transformers: list of dc.trans.Transformers
            Transformers that the input data has been transformed by.  The output
            is passed through these transformers to undo the transformations.
        outputs: Tensor or list of Tensors
            The outputs to return.  If this is None, the model's standard prediction
            outputs will be returned.  Alternatively one or more Tensors within the
            model may be specified, in which case the output of those Tensors will be
            returned.
        output_types: String or list of Strings
            If specified, all outputs of this type will be retrieved
            from the model. If output_types is specified, outputs must
            be None.

        Returns
        -------
        a NumPy array of the model produces a single output, or a list of arrays
        if it produces multiple outputs
        """
        generator = self.default_generator(dataset,
                                           mode='predict',
                                           deterministic=True,
                                           pad_batches=False)
        return self.predict_on_generator(generator,
                                         transformers=transformers,
                                         outputs=outputs,
                                         output_types=output_types)

    def predict_embedding(self, dataset: Dataset) -> OneOrMany[np.ndarray]:
        """
        Predicts embeddings created by underlying model if any exist.
        An embedding must be specified to have `output_type` of
        `'embedding'` in the model definition.

        Parameters
        ----------
        dataset: dc.data.Dataset
            Dataset to make prediction on

        Returns
        -------
        a NumPy array of the embeddings model produces, or a list
        of arrays if it produces multiple embeddings
        """
        generator = self.default_generator(dataset,
                                           mode='predict',
                                           pad_batches=False)
        return self._predict(generator, [], None, False, ['embedding'])

    def predict_uncertainty(
            self,
            dataset: Dataset,
            masks: int = 50) -> OneOrMany[Tuple[np.ndarray, np.ndarray]]:
        """
        Predict the model's outputs, along with the uncertainty in each one.

        The uncertainty is computed as described in https://arxiv.org/abs/1703.04977.
        It involves repeating the prediction many times with different dropout masks.
        The prediction is computed as the average over all the predictions.  The
        uncertainty includes both the variation among the predicted values (epistemic
        uncertainty) and the model's own estimates for how well it fits the data
        (aleatoric uncertainty).  Not all models support uncertainty prediction.

        Parameters
        ----------
        dataset: dc.data.Dataset
            Dataset to make prediction on
        masks: int
            the number of dropout masks to average over

        Returns
        -------
        for each output, a tuple (y_pred, y_std) where y_pred is the predicted
        value of the output, and each element of y_std estimates the standard
        deviation of the corresponding element of y_pred
        """
        sum_pred: List[np.ndarray] = []
        sum_sq_pred: List[np.ndarray] = []
        sum_var: List[np.ndarray] = []
        for i in range(masks):
            generator = self.default_generator(dataset,
                                               mode='uncertainty',
                                               pad_batches=False)
            results = self._predict(generator, [], None, True, None)
            if len(sum_pred) == 0:
                for p, v in results:
                    sum_pred.append(p)
                    sum_sq_pred.append(p * p)
                    sum_var.append(v)
            else:
                for j, (p, v) in enumerate(results):
                    sum_pred[j] += p
                    sum_sq_pred[j] += p * p
                    sum_var[j] += v
        output = []
        std = []
        for i in range(len(sum_pred)):
            p = sum_pred[i] / masks
            output.append(p)
            std.append(
                np.sqrt(sum_sq_pred[i] / masks - p * p + sum_var[i] / masks))
        if len(output) == 1:
            return (output[0], std[0])
        else:
            return list(zip(output, std))

    def evaluate_generator(self,
                           generator: Iterable[Tuple[Any, Any, Any]],
                           metrics: List[Metric],
                           transformers: List[Transformer] = [],
                           per_task_metrics: bool = False):
        """Evaluate the performance of this model on the data produced by a generator.

        Parameters
        ----------
        generator: generator
            this should generate batches, each represented as a tuple of the form
            (inputs, labels, weights).
        metric: list of deepchem.metrics.Metric
            Evaluation metric
        transformers: list of dc.trans.Transformers
            Transformers that the input data has been transformed by.  The output
            is passed through these transformers to undo the transformations.
        per_task_metrics: bool
            If True, return per-task scores.

        Returns
        -------
        dict
            Maps tasks to scores under metric.
        """
        evaluator = GeneratorEvaluator(self, generator, transformers)
        return evaluator.compute_model_performance(metrics, per_task_metrics)

    def compute_saliency(self, X: np.ndarray) -> OneOrMany[np.ndarray]:
        """Compute the saliency map for an input sample.

        This computes the Jacobian matrix with the derivative of each output element
        with respect to each input element.  More precisely,

        - If this model has a single output, it returns a matrix of shape
            (output_shape, input_shape) with the derivatives.
        - If this model has multiple outputs, it returns a list of matrices, one
            for each output.

        This method cannot be used on models that take multiple inputs.

        Parameters
        ----------
        X: ndarray
            the input data for a single sample

        Returns
        -------
        the Jacobian matrix, or a list of matrices
        """
        input_shape = X.shape
        X = np.reshape(X, [1] + list(X.shape))
        self._create_inputs([X])
        X_b, _, _ = self._prepare_batch(([X], None, None))

        # Use a GradientTape to compute gradients.

        X_c = tf.constant(X_b[0])
        with tf.GradientTape(persistent=True,
                             watch_accessed_variables=False) as tape:
            tape.watch(X_c)
            outputs = self._compute_model(X_c)
            if tf.is_tensor(outputs):
                outputs = [outputs]
            final_result = []
            for output in outputs:
                output_shape = tuple(output.shape.as_list()[1:])
                output = tf.reshape(output, [-1])
                result = []
                for i in range(output.shape[0]):
                    result.append(tape.gradient(output[i], X_c))
                final_result.append(
                    tf.reshape(tf.stack(result),
                               output_shape + input_shape).numpy())
        if len(final_result) == 1:
            return final_result[0]
        return final_result

    def _prepare_batch(self, batch: Tuple[Any, Any,
                                          Any]) -> Tuple[List, List, List]:
        inputs, labels, weights = batch
        inputs = [
            x if x.dtype == t else x.astype(t)
            for x, t in zip(inputs, self._input_dtypes)
        ]
        if labels is not None:
            labels = [
                x if x.dtype == t else x.astype(t)
                for x, t in zip(labels, self._label_dtypes)
            ]
        if weights is not None:
            weights = [
                x if x.dtype == t else x.astype(t)
                for x, t in zip(weights, self._weights_dtypes)
            ]
        for i in range(len(inputs)):
            shape = inputs[i].shape
            dims = len(shape)
            expected_dims = len(self._input_shapes[i])
            if dims < expected_dims:
                inputs[i] = inputs[i].reshape(shape + (1,) *
                                              (expected_dims - dims))
            elif dims > expected_dims and all(
                    d == 1 for d in shape[expected_dims:]):
                inputs[i] = inputs[i].reshape(shape[:expected_dims])
        return (inputs, labels, weights)

    def default_generator(
            self,
            dataset: Dataset,
            epochs: int = 1,
            mode: str = 'fit',
            deterministic: bool = True,
            pad_batches: bool = True) -> Iterable[Tuple[List, List, List]]:
        """Create a generator that iterates batches for a dataset.

        Subclasses may override this method to customize how model inputs are
        generated from the data.

        Parameters
        ----------
        dataset: Dataset
            the data to iterate
        epochs: int
            the number of times to iterate over the full dataset
        mode: str
            allowed values are 'fit' (called during training), 'predict' (called
            during prediction), and 'uncertainty' (called during uncertainty
            prediction)
        deterministic: bool
            whether to iterate over the dataset in order, or randomly shuffle the
            data for each epoch
        pad_batches: bool
            whether to pad each batch up to this model's preferred batch size

        Returns
        -------
        a generator that iterates batches, each represented as a tuple of lists:
        ([inputs], [outputs], [weights])
        """
        for epoch in range(epochs):
            for (X_b, y_b, w_b,
                 ids_b) in dataset.iterbatches(batch_size=self.batch_size,
                                               deterministic=deterministic,
                                               pad_batches=pad_batches):
                yield ([X_b], [y_b], [w_b])

    def save_checkpoint(self,
                        max_checkpoints_to_keep: int = 5,
                        model_dir: Optional[str] = None) -> None:
        """Save a checkpoint to disk.

        Usually you do not need to call this method, since fit() saves checkpoints
        automatically.  If you have disabled automatic checkpointing during fitting,
        this can be called to manually write checkpoints.

        Parameters
        ----------
        max_checkpoints_to_keep: int
            the maximum number of checkpoints to keep.  Older checkpoints are discarded.
        model_dir: str, default None
            Model directory to save checkpoint to. If None, revert to self.model_dir
        """
        self._ensure_built()
        if model_dir is None:
            model_dir = self.model_dir
        if not os.path.exists(model_dir):
            os.makedirs(model_dir)
        manager = tf.train.CheckpointManager(self._checkpoint, model_dir,
                                             max_checkpoints_to_keep)
        manager.save()

    def get_checkpoints(self, model_dir: Optional[str] = None):
        """Get a list of all available checkpoint files.

        Parameters
        ----------
        model_dir: str, default None
            Directory to get list of checkpoints from. Reverts to self.model_dir if None

        """
        if model_dir is None:
            model_dir = self.model_dir
        return tf.train.get_checkpoint_state(
            model_dir).all_model_checkpoint_paths

    def restore(self,
                checkpoint: Optional[str] = None,
                model_dir: Optional[str] = None) -> None:
        """Reload the values of all variables from a checkpoint file.

        Parameters
        ----------
        checkpoint: str
            the path to the checkpoint file to load.  If this is None, the most recent
            checkpoint will be chosen automatically.  Call get_checkpoints() to get a
            list of all available checkpoints.
        model_dir: str, default None
            Directory to restore checkpoint from. If None, use self.model_dir.
        """
        self._ensure_built()
        if model_dir is None:
            model_dir = self.model_dir
        if checkpoint is None:
            checkpoint = tf.train.latest_checkpoint(model_dir)
        if checkpoint is None:
            raise ValueError('No checkpoint found')
        self._checkpoint.restore(checkpoint)

    def get_global_step(self) -> int:
        """Get the number of steps of fitting that have been performed."""
        return int(self._global_step)

    def _log_scalar_to_tensorboard(self, name: str, value: Any, step: int):
        """Log a scalar value to Tensorboard."""
        with self._summary_writer.as_default():
            tf.summary.scalar(name, value, step)

    def _create_assignment_map(self,
                               source_model: "KerasModel",
                               include_top: bool = True,
                               **kwargs) -> Dict[Any, Any]:
        """
        Creates a default assignment map between variables of source and current model.
        This is used only when a custom assignment map is missing. This assumes the
        model is made of different layers followed by a dense layer for mapping to
        output tasks. include_top is used to control whether or not the final dense
        layer is used. The default assignment map is useful in cases where the type
        of task is different (classification vs regression) and/or number of tasks.

        Parameters
        ----------
        source_model: dc.models.KerasModel
            Source model to copy variable values from.
        include_top: bool, default True
            if true, copies the last dense layer
        """
        assignment_map: Dict[Any, Any] = {}
        source_vars = source_model.model.trainable_variables
        dest_vars = self.model.trainable_variables

        if not include_top:
            source_vars = source_vars[:-2]
            dest_vars = dest_vars[:-2]

        for source_var, dest_var in zip(source_vars, dest_vars):
            assignment_map[source_var.ref()] = dest_var

        return assignment_map

    def _create_value_map(self, source_model: "KerasModel",
                          **kwargs) -> Dict[Any, Any]:
        """
        Creates a value map between variables in the source model and their
        current values. This is used only when a custom value map is missing, and
        assumes the restore method has been called under self.session.

        Parameters
        ----------
        source_model: dc.models.KerasModel
            Source model to create value map from
        """
        value_map: Dict[Any, Any] = {}
        source_vars = source_model.model.trainable_variables

        for source_var in source_vars:
            value_map[source_var.ref()] = source_var.numpy()

        return value_map

    def load_from_pretrained(self,
                             source_model: "KerasModel",
                             assignment_map: Optional[Dict[Any, Any]] = None,
                             value_map: Optional[Dict[Any, Any]] = None,
                             checkpoint: Optional[str] = None,
                             model_dir: Optional[str] = None,
                             include_top: bool = True,
                             inputs: Optional[Sequence[Any]] = None,
                             **kwargs) -> None:
        """Copies variable values from a pretrained model. `source_model` can either
        be a pretrained model or a model with the same architecture. `value_map`
        is a variable-value dictionary. If no `value_map` is provided, the variable
        values are restored to the `source_model` from a checkpoint and a default
        `value_map` is created. `assignment_map` is a dictionary mapping variables
        from the `source_model` to the current model. If no `assignment_map` is
        provided, one is made from scratch and assumes the model is composed of
        several different layers, with the final one being a dense layer. `include_top`
        is used to control whether or not the final dense layer is used. The default
        assignment map is useful in cases where the type of task is different
        (classification vs regression) and/or number of tasks in the setting.

        Parameters
        ----------
        source_model: dc.KerasModel, required
            source_model can either be the pretrained model or a dc.KerasModel with
            the same architecture as the pretrained model. It is used to restore from
            a checkpoint, if value_map is None and to create a default assignment map
            if assignment_map is None
        assignment_map: Dict, default None
            Dictionary mapping the source_model variables and current model variables
        value_map: Dict, default None
            Dictionary containing source_model trainable variables mapped to numpy
            arrays. If value_map is None, the values are restored and a default
            variable map is created using the restored values
        checkpoint: str, default None
            the path to the checkpoint file to load.  If this is None, the most recent
            checkpoint will be chosen automatically.  Call get_checkpoints() to get a
            list of all available checkpoints
        model_dir: str, default None
            Restore model from custom model directory if needed
        include_top: bool, default True
            if True, copies the weights and bias associated with the final dense
            layer. Used only when assignment map is None
        inputs: List, input tensors for model
            if not None, then the weights are built for both the source and self.
            This option is useful only for models that are built by
            subclassing tf.keras.Model, and not using the functional API by tf.keras
        """
        if inputs is not None:
            # Ensure weights for both models are built.
            source_model.model(inputs)
            self.model(inputs)

        self._ensure_built()
        if value_map is None:
            logger.info(
                "No value map provided. Creating default value map from restored model."
            )
            source_model.restore(model_dir=model_dir, checkpoint=checkpoint)
            value_map = self._create_value_map(source_model=source_model)

        if assignment_map is None:
            logger.info(
                "No assignment map provided. Creating custom assignment map.")
            assignment_map = self._create_assignment_map(
                source_model=source_model, include_top=include_top)

        for source_var, dest_var in assignment_map.items():
            assert source_var.deref().shape == dest_var.shape
            dest_var.assign(value_map[source_var])


class _StandardLoss(object):
    """The implements the loss function for models that use a dc.models.losses.Loss."""

    def __init__(self, model: tf.keras.Model, loss: Loss) -> None:
        self.model = model
        self.loss = loss

    def __call__(self, outputs: List, labels: List, weights: List) -> float:
        if len(outputs) != 1 or len(labels) != 1 or len(weights) != 1:
            raise ValueError(
                "Loss functions expects exactly one each of outputs, labels, and weights"
            )
        losses = self.loss._compute_tf_loss(outputs[0], labels[0])
        w = weights[0]
        if len(w.shape) < len(losses.shape):
            if tf.is_tensor(w):
                shape = tuple(w.shape.as_list())
            else:
                shape = w.shape
            shape = tuple(-1 if x is None else x for x in shape)
            w = tf.reshape(w, shape + (1,) * (len(losses.shape) - len(w.shape)))

        loss = losses * w
        return tf.reduce_mean(loss) + sum(self.model.losses)