Model (pyeddl.models)

Model

The Model class adds training & evaluation routines to a Network.

Model

Model

class pyeddl.model.Model(cmodel=None)

The Model class adds training & evaluation routines to a Network.

__init__(cmodel=None)

Initialize self. See help(type(self)) for accurate signature.

compile(optimizer, losses=None, metrics=None, loss_weights=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None, device='cpu', **kwargs)

Configures the model for training. # Arguments

optimizer: String (name of optimizer) or optimizer instance. loss: String (name of objective function) or objective function.

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’}.

loss_weights: Optional list or dictionary specifying scalar

coefficients (Python floats) to weight the loss contributions of different model outputs. The loss value that will be minimized by the model will then be the weighted sum of all individual losses, weighted by the loss_weights coefficients. If a list, it is expected to have a 1:1 mapping to the model’s outputs. If a dict, it is expected to map output names (strings) to scalar coefficients.

sample_weight_mode: If you need to do timestep-wise

sample weighting (2D weights), set this to “temporal”. None defaults to sample-wise weights (1D). If the model has multiple outputs, you can use a different sample_weight_mode on each output by passing a dictionary or a list of modes.

weighted_metrics: List of metrics to be evaluated and weighted

by sample_weight or class_weight during training and testing.

target_tensors: By default, Keras will create placeholders for the

model’s target, which will be fed with the target data during training. If instead you would like to use your own target tensors (in turn, Keras will not expect external Numpy data for these targets at training time), you can specify them via the target_tensors argument. It can be a single tensor (for a single-output model), a list of tensors, or a dict mapping output names to target tensors.

**kwargs: When using the Theano/CNTK backends, these arguments

are passed into K.function. When using the TensorFlow backend, these arguments are passed into tf.Session.run.

# Raises

ValueError: In case of invalid arguments for

optimizer, loss, metrics or sample_weight_mode.

evaluate(x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False)

Returns the loss value & metrics values for the model in test mode. Computation is done in batches. # Arguments

x: Input data. It could be:

• A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).

• A dict mapping input names to the corresponding array/tensors, if the model has named inputs.

• None (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors).

y: Target data. Like the input data x,

it could be either Numpy array(s), framework-native tensor(s), list of Numpy arrays (if the model has multiple outputs) or None (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors). If output layers in the model are named, you can also pass a dictionary mapping output names to Numpy arrays.

batch_size: Integer or None.

Number of samples per gradient update. If unspecified, batch_size will default to 32. Do not specify the batch_size is your data is in the form of symbolic tensors, generators

verbose: 0 or 1. Verbosity mode.

0 = silent, 1 = progress bar.

sample_weight: Optional Numpy array of weights for

the test samples, used for weighting the loss function. You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. In this case you should make sure to specify sample_weight_mode=”temporal” in compile().

steps: Integer or None.

Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of None.

callbacks: List of callbacks to apply during evaluation. 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.

# Raises

ValueError: in case of invalid arguments.

# Returns

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

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 dict mapping input names to the corresponding array/tensors, if the model has named inputs.

y: Target data. Like the input data x,

it could be either Numpy array(s), framework-native tensor(s), list of Numpy arrays (if the model has multiple outputs) or None (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors). If output layers in the model are named, you can also pass a dictionary mapping output names to Numpy arrays.

batch_size: Integer or None.

Number of samples per gradient update. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of symbolic tensors, generators, or Sequence instances (since they generate batches).

epochs: Integer. Number of epochs to train the model.

An epoch is an iteration over the entire x and y data provided. Note that in conjunction with initial_epoch, epochs is to be understood as “final epoch”. The model is not trained for a number of iterations given by epochs, but merely until the epoch of index epochs is reached.

verbose: Integer. 0, 1, or 2. Verbosity mode.

0 = silent, 1 = progress bar, 2 = one line per epoch.

callbacks: List of callbacks to apply during training and validation validation_split: Float between 0 and 1.

Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the x and y data provided, before shuffling. This argument is not supported when x is a generator or Sequence instance.

validation_data: Data on which to evaluate

the loss and any model metrics at the end of each epoch. The model will not be trained on this data. validation_data will override validation_split. validation_data could be:

• tuple (x_val, y_val) of Numpy arrays or tensors

• tuple (x_val, y_val, val_sample_weights) of Numpy arrays

• dataset or a dataset iterator

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

shuffle: Boolean (whether to shuffle the training data

before each epoch) or str (for ‘batch’). ‘batch’ is a special option for dealing with the limitations of HDF5 data; it shuffles in batch-sized chunks. Has no effect when steps_per_epoch is not None.

class_weight: Optional dictionary mapping class indices (integers)

to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to “pay more attention” to samples from an under-represented class.

sample_weight: Optional Numpy array of weights for

the training samples, used for weighting the loss function (during training only). You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. In this case you should make sure to specify sample_weight_mode=”temporal” in compile(). This argument is not supported when x generator, or Sequence instance, instead provide the sample_weights as the third element of x.

initial_epoch: Integer.

Epoch at which to start training (useful for resuming a previous training run).

steps_per_epoch: Integer or None.

Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. When training with input tensors such as TensorFlow data tensors, the default None is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined.

validation_steps: Only relevant if steps_per_epoch

is specified. Total number of steps (batches of samples) to validate before stopping.

validation_steps: Only relevant if validation_data is provided

and is a generator. Total number of steps (batches of samples) to draw before stopping when performing validation at the end of every epoch.

validation_freq: Only relevant if validation data is provided. Integer

or list/tuple/set. If an integer, specifies how many training epochs to run before a new validation run is performed, e.g. validation_freq=2 runs validation every 2 epochs. If a list, tuple, or set, specifies the epochs on which to run validation, e.g. validation_freq=[1, 2, 10] runs validation at the end of the 1st, 2nd, and 10th epochs.

max_queue_size: Integer. Used for generator.

Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.

workers: 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.

**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.

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

Runs a single gradient update on a single batch of data.

Args:

x: Numpy array of training data,

or list of Numpy arrays if the model has multiple inputs. If all inputs in the model are named, you can also pass a dictionary mapping input names to Numpy arrays.

y: Numpy array of target data,

or list of Numpy arrays if the model has multiple outputs. If all outputs in the model are named, you can also pass a dictionary mapping output names to Numpy arrays.

sample_weight: Optional array of the same length as x, containing

weights to apply to the model’s loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. In this case you should make sure to specify sample_weight_mode=”temporal” in compile().

class_weight: Optional dictionary mapping

class indices (integers) to a weight (float) to apply to the model’s loss for the samples from this class during training. This can be useful to tell the model to “pay more attention” to samples from an under-represented class.

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.