from . import backend as K
import numpy as np
from pyeddl.utils.utils import iterate_minibatches
[docs]class Model(object):
"""The `Model` class adds training & evaluation routines to a `Network`.
"""
[docs] def __init__(self, cmodel=None):
if cmodel:
self.c_model = cmodel
@classmethod
def from_model(cls, name):
return cls(cmodel=K.get_model(name))
[docs] def compile(self, 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`.
"""
self.optimizer = optimizer
self.losses = losses or []
self.metrics = metrics or []
self.loss_weights = loss_weights
self.sample_weight_mode = sample_weight_mode
self.weighted_metrics = weighted_metrics
self.device = device
# Compile model
K.compile(self.c_model, self.optimizer, self.losses, self.metrics, self.device)
[docs] def fit(self,
x=None,
y=None,
batch_size=None,
epochs=1,
verbose=1,
callbacks=None,
validation_split=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.
"""
# Check optimizer
# Check input sizes
print("Training model...")
for e in range(epochs):
print("Epoch #{}...".format(e))
for i, batch in enumerate(iterate_minibatches(x, y, batch_size, shuffle=shuffle)):
print('\t- Training batch #{}...'.format(i))
x_batch, y_batch = batch
self.train_on_batch(x_batch, y_batch)
print('\t=> Epoch #{}\tTrain loss: {:.5f}\tAcc: {:.5f}\tVal. loss: {:.5f}\tVal. acc: {:.5f}'.format(e, 0,0,0,0))
[docs] def evaluate(self,
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.
"""
pass
[docs] def train_on_batch(self, 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.
"""
K.train_batch(x, y)
def summary(self):
return K.summary(self.c_model)
def plot(self, filename='model.pdf'):
return K.plot(self.c_model, filename)