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models.py
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models.py
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# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import print_function
import warnings
import copy
import json
import os
import yaml
import numpy as np
from . import backend as K
from . import optimizers
from . import layers as layer_module
from .utils.io_utils import ask_to_proceed_with_overwrite
from .utils.generic_utils import has_arg
from .engine.training import Model
from .engine import topology
from .engine.topology import Layer
from .engine.topology import Input
from .legacy import layers as legacy_layers
from .legacy import models as legacy_models
from .legacy import interfaces
try:
import h5py
except ImportError:
h5py = None
def save_model(model, filepath, overwrite=True, include_optimizer=True):
"""Save a model to a HDF5 file.
The saved model contains:
- the model's configuration (topology)
- the model's weights
- the model's optimizer's state (if any)
Thus the saved model can be reinstantiated in
the exact same state, without any of the code
used for model definition or training.
# Arguments
model: Keras model instance to be saved.
filepath: String, path where to save the model.
overwrite: Whether we should overwrite any existing
model at the target location, or instead
ask the user with a manual prompt.
include_optimizer: If True, save optimizer's state together.
# Raises
ImportError: if h5py is not available.
"""
if h5py is None:
raise ImportError('`save_model` requires h5py.')
def get_json_type(obj):
"""Serialize any object to a JSON-serializable structure.
# Arguments
obj: the object to serialize
# Returns
JSON-serializable structure representing `obj`.
# Raises
TypeError: if `obj` cannot be serialized.
"""
# if obj is a serializable Keras class instance
# e.g. optimizer, layer
if hasattr(obj, 'get_config'):
return {'class_name': obj.__class__.__name__,
'config': obj.get_config()}
# if obj is any numpy type
if type(obj).__module__ == np.__name__:
if isinstance(obj, np.ndarray):
return {'type': type(obj),
'value': obj.tolist()}
else:
return obj.item()
# misc functions (e.g. loss function)
if callable(obj):
return obj.__name__
# if obj is a python 'type'
if type(obj).__name__ == type.__name__:
return obj.__name__
raise TypeError('Not JSON Serializable:', obj)
from . import __version__ as keras_version
# If file exists and should not be overwritten.
if not overwrite and os.path.isfile(filepath):
proceed = ask_to_proceed_with_overwrite(filepath)
if not proceed:
return
with h5py.File(filepath, mode='w') as f:
f.attrs['keras_version'] = str(keras_version).encode('utf8')
f.attrs['backend'] = K.backend().encode('utf8')
f.attrs['model_config'] = json.dumps({
'class_name': model.__class__.__name__,
'config': model.get_config()
}, default=get_json_type).encode('utf8')
model_weights_group = f.create_group('model_weights')
if legacy_models.needs_legacy_support(model):
model_layers = legacy_models.legacy_sequential_layers(model)
else:
model_layers = model.layers
topology.save_weights_to_hdf5_group(model_weights_group, model_layers)
if include_optimizer and hasattr(model, 'optimizer'):
if isinstance(model.optimizer, optimizers.TFOptimizer):
warnings.warn(
'TensorFlow optimizers do not '
'make it possible to access '
'optimizer attributes or optimizer state '
'after instantiation. '
'As a result, we cannot save the optimizer '
'as part of the model save file.'
'You will have to compile your model again '
'after loading it. '
'Prefer using a Keras optimizer instead '
'(see keras.io/optimizers).')
else:
f.attrs['training_config'] = json.dumps({
'optimizer_config': {
'class_name': model.optimizer.__class__.__name__,
'config': model.optimizer.get_config()
},
'loss': model.loss,
'metrics': model.metrics,
'sample_weight_mode': model.sample_weight_mode,
'loss_weights': model.loss_weights,
}, default=get_json_type).encode('utf8')
# Save optimizer weights.
symbolic_weights = getattr(model.optimizer, 'weights')
if symbolic_weights:
optimizer_weights_group = f.create_group('optimizer_weights')
weight_values = K.batch_get_value(symbolic_weights)
weight_names = []
for i, (w, val) in enumerate(zip(symbolic_weights,
weight_values)):
# Default values of symbolic_weights is /variable
# for theano and cntk
if K.backend() == 'theano' or K.backend() == 'cntk':
if hasattr(w, 'name'):
if w.name.split('/')[-1] == 'variable':
name = str(w.name) + '_' + str(i)
else:
name = str(w.name)
else:
name = 'param_' + str(i)
else:
if hasattr(w, 'name') and w.name:
name = str(w.name)
else:
name = 'param_' + str(i)
weight_names.append(name.encode('utf8'))
optimizer_weights_group.attrs['weight_names'] = weight_names
for name, val in zip(weight_names, weight_values):
param_dset = optimizer_weights_group.create_dataset(
name,
val.shape,
dtype=val.dtype)
if not val.shape:
# scalar
param_dset[()] = val
else:
param_dset[:] = val
f.flush()
def load_model(filepath, custom_objects=None, compile=True):
"""Loads a model saved via `save_model`.
# Arguments
filepath: String, path to the saved model.
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
compile: Boolean, whether to compile the model
after loading.
# Returns
A Keras model instance. If an optimizer was found
as part of the saved model, the model is already
compiled. Otherwise, the model is uncompiled and
a warning will be displayed. When `compile` is set
to False, the compilation is omitted without any
warning.
# Raises
ImportError: if h5py is not available.
ValueError: In case of an invalid savefile.
"""
if h5py is None:
raise ImportError('`load_model` requires h5py.')
if not custom_objects:
custom_objects = {}
def convert_custom_objects(obj):
"""Handles custom object lookup.
# Arguments
obj: object, dict, or list.
# Returns
The same structure, where occurrences
of a custom object name have been replaced
with the custom object.
"""
if isinstance(obj, list):
deserialized = []
for value in obj:
deserialized.append(convert_custom_objects(value))
return deserialized
if isinstance(obj, dict):
deserialized = {}
for key, value in obj.items():
deserialized[key] = convert_custom_objects(value)
return deserialized
if obj in custom_objects:
return custom_objects[obj]
return obj
with h5py.File(filepath, mode='r') as f:
# instantiate model
model_config = f.attrs.get('model_config')
if model_config is None:
raise ValueError('No model found in config file.')
model_config = json.loads(model_config.decode('utf-8'))
model = model_from_config(model_config, custom_objects=custom_objects)
# set weights
topology.load_weights_from_hdf5_group(f['model_weights'], model.layers)
# Early return if compilation is not required.
if not compile:
return model
# instantiate optimizer
training_config = f.attrs.get('training_config')
if training_config is None:
warnings.warn('No training configuration found in save file: '
'the model was *not* compiled. Compile it manually.')
return model
training_config = json.loads(training_config.decode('utf-8'))
optimizer_config = training_config['optimizer_config']
optimizer = optimizers.deserialize(optimizer_config,
custom_objects=custom_objects)
# Recover loss functions and metrics.
loss = convert_custom_objects(training_config['loss'])
metrics = convert_custom_objects(training_config['metrics'])
sample_weight_mode = training_config['sample_weight_mode']
loss_weights = training_config['loss_weights']
# Compile model.
model.compile(optimizer=optimizer,
loss=loss,
metrics=metrics,
loss_weights=loss_weights,
sample_weight_mode=sample_weight_mode)
# Set optimizer weights.
if 'optimizer_weights' in f:
# Build train function (to get weight updates).
if isinstance(model, Sequential):
model.model._make_train_function()
else:
model._make_train_function()
optimizer_weights_group = f['optimizer_weights']
optimizer_weight_names = [n.decode('utf8') for n in
optimizer_weights_group.attrs['weight_names']]
optimizer_weight_values = [optimizer_weights_group[n] for n in
optimizer_weight_names]
try:
model.optimizer.set_weights(optimizer_weight_values)
except ValueError:
warnings.warn('Error in loading the saved optimizer '
'state. As a result, your model is '
'starting with a freshly initialized '
'optimizer.')
return model
def model_from_config(config, custom_objects=None):
"""Instantiates a Keras model from its config.
# Arguments
config: Configuration dictionary.
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
# Returns
A Keras model instance (uncompiled).
# Raises
TypeError: if `config` is not a dictionary.
"""
if isinstance(config, list):
raise TypeError('`model_from_config` expects a dictionary, not a list. '
'Maybe you meant to use '
'`Sequential.from_config(config)`?')
return layer_module.deserialize(config, custom_objects=custom_objects)
def model_from_yaml(yaml_string, custom_objects=None):
"""Parses a yaml model configuration file and returns a model instance.
# Arguments
yaml_string: YAML string encoding a model configuration.
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
# Returns
A Keras model instance (uncompiled).
"""
config = yaml.load(yaml_string)
return layer_module.deserialize(config, custom_objects=custom_objects)
def model_from_json(json_string, custom_objects=None):
"""Parses a JSON model configuration file and returns a model instance.
# Arguments
json_string: JSON string encoding a model configuration.
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
# Returns
A Keras model instance (uncompiled).
"""
config = json.loads(json_string)
return layer_module.deserialize(config, custom_objects=custom_objects)
class Sequential(Model):
"""Linear stack of layers.
# Arguments
layers: list of layers to add to the model.
# Note
The first layer passed to a Sequential model
should have a defined input shape. What that
means is that it should have received an `input_shape`
or `batch_input_shape` argument,
or for some type of layers (recurrent, Dense...)
an `input_dim` argument.
# Example
```python
model = Sequential()
# first layer must have a defined input shape
model.add(Dense(32, input_dim=500))
# afterwards, Keras does automatic shape inference
model.add(Dense(32))
# also possible (equivalent to the above):
model = Sequential()
model.add(Dense(32, input_shape=(500,)))
model.add(Dense(32))
# also possible (equivalent to the above):
model = Sequential()
# here the batch dimension is None,
# which means any batch size will be accepted by the model.
model.add(Dense(32, batch_input_shape=(None, 500)))
model.add(Dense(32))
```
"""
def __init__(self, layers=None, name=None):
self.layers = [] # Stack of layers.
self.model = None # Internal Model instance.
self.inputs = [] # List of input tensors
self.outputs = [] # List of length 1: the output tensor (unique).
self._trainable = True
self._initial_weights = None
# Model attributes.
self.inbound_nodes = []
self.outbound_nodes = []
self.built = False
# Set model name.
if not name:
prefix = 'sequential_'
name = prefix + str(K.get_uid(prefix))
self.name = name
# Add to the model any layers passed to the constructor.
if layers:
for layer in layers:
self.add(layer)
def add(self, layer):
"""Adds a layer instance on top of the layer stack.
# Arguments
layer: layer instance.
# Raises
TypeError: If `layer` is not a layer instance.
ValueError: In case the `layer` argument does not
know its input shape.
ValueError: In case the `layer` argument has
multiple output tensors, or is already connected
somewhere else (forbidden in `Sequential` models).
"""
if not isinstance(layer, Layer):
raise TypeError('The added layer must be '
'an instance of class Layer. '
'Found: ' + str(layer))
if not self.outputs:
# first layer in model: check that it is an input layer
if not layer.inbound_nodes:
# create an input layer
if not hasattr(layer, 'batch_input_shape'):
raise ValueError('The first layer in a '
'Sequential model must '
'get an `input_shape` or '
'`batch_input_shape` argument.')
# Instantiate the input layer.
x = Input(batch_shape=layer.batch_input_shape,
dtype=layer.dtype, name=layer.name + '_input')
# This will build the current layer
# and create the node connecting the current layer
# to the input layer we just created.
layer(x)
if len(layer.inbound_nodes) != 1:
raise ValueError('A layer added to a Sequential model must '
'not already be connected somewhere else. '
'Model received layer ' + layer.name +
' which has ' +
str(len(layer.inbound_nodes)) +
' pre-existing inbound connections.')
if len(layer.inbound_nodes[0].output_tensors) != 1:
raise ValueError('All layers in a Sequential model '
'should have a single output tensor. '
'For multi-output layers, '
'use the functional API.')
self.outputs = [layer.inbound_nodes[0].output_tensors[0]]
self.inputs = topology.get_source_inputs(self.outputs[0])
# We create an input node, which we will keep updated
# as we add more layers
topology.Node(outbound_layer=self,
inbound_layers=[],
node_indices=[],
tensor_indices=[],
input_tensors=self.inputs,
output_tensors=self.outputs,
# no model-level masking for now
input_masks=[None for _ in self.inputs],
output_masks=[None],
input_shapes=[x._keras_shape for x in self.inputs],
output_shapes=[self.outputs[0]._keras_shape])
else:
output_tensor = layer(self.outputs[0])
if isinstance(output_tensor, list):
raise TypeError('All layers in a Sequential model '
'should have a single output tensor. '
'For multi-output layers, '
'use the functional API.')
self.outputs = [output_tensor]
# update self.inbound_nodes
self.inbound_nodes[0].output_tensors = self.outputs
self.inbound_nodes[0].output_shapes = [self.outputs[0]._keras_shape]
self.layers.append(layer)
self.built = False
def pop(self):
"""Removes the last layer in the model.
# Raises
TypeError: if there are no layers in the model.
"""
if not self.layers:
raise TypeError('There are no layers in the model.')
self.layers.pop()
if not self.layers:
self.outputs = []
self.inbound_nodes = []
self.outbound_nodes = []
else:
self.layers[-1].outbound_nodes = []
self.outputs = [self.layers[-1].output]
# update self.inbound_nodes
self.inbound_nodes[0].output_tensors = self.outputs
self.inbound_nodes[0].output_shapes = [self.outputs[0]._keras_shape]
self.built = False
def get_layer(self, name=None, index=None):
"""Retrieve a layer that is part of the model.
Returns a layer based on either its name (unique)
or its index in the graph. 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.
"""
if not self.built:
self.build()
return self.model.get_layer(name, index)
def call(self, inputs, mask=None):
if not self.built:
self.build()
return self.model.call(inputs, mask)
def build(self, input_shape=None):
if not self.inputs or not self.outputs:
raise TypeError('Sequential model cannot be built: model is empty.'
' Add some layers first.')
# actually create the model
self.model = Model(self.inputs, self.outputs[0],
name=self.name + '_model')
self.model.trainable = self.trainable
# mirror model attributes
self.supports_masking = self.model.supports_masking
self._output_mask_cache = self.model._output_mask_cache
self._output_tensor_cache = self.model._output_tensor_cache
self._output_shape_cache = self.model._output_shape_cache
self.input_layers = self.model.input_layers
self.input_layers_node_indices = self.model.input_layers_node_indices
self.input_layers_tensor_indices = self.model.input_layers_tensor_indices
self.output_layers = self.model.output_layers
self.output_layers_node_indices = self.model.output_layers_node_indices
self.output_layers_tensor_indices = self.model.output_layers_tensor_indices
self.nodes_by_depth = self.model.nodes_by_depth
self.container_nodes = self.model.container_nodes
self.output_names = self.model.output_names
self.input_names = self.model.input_names
self._feed_input_names = self.model._feed_input_names
self._feed_inputs = self.model._feed_inputs
# Make sure child model callbacks
# will call the parent Sequential model.
self.model.callback_model = self
self.built = True
@property
def uses_learning_phase(self):
if not self.built:
self.build()
return self.model.uses_learning_phase
@property
def _flattened_layers(self):
layers = []
if self.layers:
# Support for legacy models
if isinstance(self.layers[0], legacy_layers.Merge):
merge = self.layers[0]
for layer in merge.layers:
if hasattr(layer, '_flattened_layers'):
for sublayer in layer._flattened_layers:
if sublayer not in layers:
layers.append(sublayer)
elif hasattr(layer, 'layers'):
for sublayer in layer.layers:
if sublayer not in layers:
layers.append(sublayer)
else:
if layer not in layers:
layers.append(layer)
else:
if self.layers[0] not in layers:
layers.append(self.layers[0])
for layer in self.layers[1:]:
if layer not in layers:
layers.append(layer)
return layers
def _gather_list_attr(self, attr):
all_attrs = []
for layer in self._flattened_layers:
all_attrs += getattr(layer, attr, [])
return all_attrs
@property
def trainable(self):
return self._trainable
@trainable.setter
def trainable(self, value):
if self.model:
self.model.trainable = value
self._trainable = value
@property
def trainable_weights(self):
if not self.trainable:
return []
# Support for legacy behavior
return self._gather_list_attr('trainable_weights')
@property
def non_trainable_weights(self):
# Support for legacy behavior
weights = self._gather_list_attr('non_trainable_weights')
if not self.trainable:
trainable_weights = self._gather_list_attr('trainable_weights')
return trainable_weights + weights
return weights
@property
def updates(self):
if not self.built:
self.build()
return self.model.updates
@property
def state_updates(self):
if not self.built:
self.build()
return self.model.state_updates
def get_updates_for(self, inputs):
if not self.built:
self.build()
return self.model.get_updates_for(inputs)
@property
def losses(self):
if not self.built:
self.build()
return self.model.losses
def get_losses_for(self, inputs):
if not self.built:
self.build()
return self.model.get_losses_for(inputs)
@property
def regularizers(self):
if not self.built:
self.build()
return self.model.regularizers
def get_weights(self):
"""Retrieves the weights of the model.
# Returns
A flat list of Numpy arrays
(one array per model weight).
"""
# Legacy support
if legacy_models.needs_legacy_support(self):
layers = legacy_models.legacy_sequential_layers(self)
weights = []
for layer in layers:
weights.append(layer.get_weights())
return weights
if not self.built:
self.build()
return self.model.get_weights()
def set_weights(self, weights):
"""Sets the weights of the model.
# Arguments
weights: Should be a list
of Numpy arrays with shapes and types matching
the output of `model.get_weights()`.
"""
# Legacy support
if legacy_models.needs_legacy_support(self):
layers = legacy_models.legacy_sequential_layers(self)
for layer in layers:
nb_param = len(layer.weights)
layer.set_weights(weights[:nb_param])
weights = weights[nb_param:]
if not self.built:
self.build()
self.model.set_weights(weights)
def load_weights(self, filepath, by_name=False):
if h5py is None:
raise ImportError('`load_weights` requires h5py.')
f = h5py.File(filepath, mode='r')
if 'layer_names' not in f.attrs and 'model_weights' in f:
f = f['model_weights']
# Legacy support
if legacy_models.needs_legacy_support(self):
layers = legacy_models.legacy_sequential_layers(self)
else:
layers = self.layers
if by_name:
topology.load_weights_from_hdf5_group_by_name(f, layers)
else:
topology.load_weights_from_hdf5_group(f, layers)
if hasattr(f, 'close'):
f.close()
def save_weights(self, filepath, overwrite=True):
if h5py is None:
raise ImportError('`save_weights` requires h5py.')
# If file exists and should not be overwritten:
if not overwrite and os.path.isfile(filepath):
proceed = ask_to_proceed_with_overwrite(filepath)
if not proceed:
return
# Legacy support
if legacy_models.needs_legacy_support(self):
layers = legacy_models.legacy_sequential_layers(self)
else:
layers = self.layers
f = h5py.File(filepath, 'w')
topology.save_weights_to_hdf5_group(f, layers)
f.flush()
f.close()
def compile(self, optimizer, loss,
metrics=None,
sample_weight_mode=None,
weighted_metrics=None,
**kwargs):
"""Configures the learning process.
# Arguments
optimizer: str (name of optimizer) or optimizer object.
See [optimizers](/optimizers).
loss: str (name of objective function) or objective function.
See [losses](/losses).
metrics: list of metrics to be evaluated by the model
during training and testing.
Typically you will use `metrics=['accuracy']`.
See [metrics](/metrics).
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).
weighted_metrics: list of metrics to be evaluated and weighted
by sample_weight or class_weight during training and testing
**kwargs: for Theano/CNTK backends, these are passed into
K.function. When using the TensorFlow backend, these are
passed into `tf.Session.run`.
# Example
```python
model = Sequential()
model.add(Dense(32, input_shape=(500,)))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
```
"""
# create the underlying model
self.build()
# call compile method of Model class
self.model.compile(optimizer, loss,
metrics=metrics,
sample_weight_mode=sample_weight_mode,
weighted_metrics=weighted_metrics,
**kwargs)
self.optimizer = self.model.optimizer
self.loss = self.model.loss
self.total_loss = self.model.total_loss
self.loss_weights = self.model.loss_weights
self.metrics = self.model.metrics
self.weighted_metrics = self.model.weighted_metrics
self.metrics_tensors = self.model.metrics_tensors
self.metrics_names = self.model.metrics_names
self.sample_weight_mode = self.model.sample_weight_mode
self.sample_weights = self.model.sample_weights
self.targets = self.model.targets
def fit(self, x, y, batch_size=32, epochs=10, verbose=1, callbacks=None,
validation_split=0., validation_data=None, shuffle=True,
class_weight=None, sample_weight=None, initial_epoch=0, **kwargs):
"""Trains the model for a fixed number of epochs.
# Arguments
x: input data, as a Numpy array or list of Numpy arrays
(if the model has multiple inputs).
y: labels, as a Numpy array.
batch_size: integer. Number of samples per gradient update.
epochs: integer, the number of epochs to train the model.
verbose: 0 for no logging to stdout,
1 for progress bar logging, 2 for one log line per epoch.
callbacks: list of `keras.callbacks.Callback` instances.
List of callbacks to apply during training.
See [callbacks](/callbacks).
validation_split: float (0. < x < 1).
Fraction of the data to use as held-out validation data.
validation_data: tuple (x_val, y_val) or tuple
(x_val, y_val, val_sample_weights) to be used as held-out
validation data. Will override validation_split.
shuffle: boolean or str (for 'batch').
Whether to shuffle the samples at each epoch.
'batch' is a special option for dealing with the
limitations of HDF5 data; it shuffles in batch-sized chunks.
class_weight: dictionary mapping classes to a weight value,
used for scaling the loss function (during training only).
sample_weight: Numpy array of weights for
the training samples, used for scaling 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().
initial_epoch: epoch at which to start training
(useful for resuming a previous training run)
# 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.
"""
# Legacy support
if 'nb_epoch' in kwargs:
warnings.warn('The `nb_epoch` argument in `fit` '
'has been renamed `epochs`.')
epochs = kwargs.pop('nb_epoch')
if kwargs:
raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))
if not self.built:
raise RuntimeError('The model needs to be compiled '
'before being used.')
return self.model.fit(x, y,
batch_size=batch_size,
epochs=epochs,
verbose=verbose,
callbacks=callbacks,
validation_split=validation_split,
validation_data=validation_data,
shuffle=shuffle,
class_weight=class_weight,
sample_weight=sample_weight,
initial_epoch=initial_epoch)
def evaluate(self, x, y, batch_size=32, verbose=1,
sample_weight=None):
"""Computes the loss on some input data, batch by batch.
# Arguments
x: input data, as a Numpy array or list of Numpy arrays
(if the model has multiple inputs).
y: labels, as a Numpy array.
batch_size: integer. Number of samples per gradient update.
verbose: verbosity mode, 0 or 1.
sample_weight: sample weights, as a Numpy array.
# Returns
Scalar test loss (if the model has no metrics)
or list of scalars (if the model computes other metrics).
The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
# Raises
RuntimeError: if the model was never compiled.
"""
if not self.built:
raise RuntimeError('The model needs to be compiled '
'before being used.')
return self.model.evaluate(x, y,
batch_size=batch_size,
verbose=verbose,
sample_weight=sample_weight)
def predict(self, x, batch_size=32, verbose=0):
"""Generates output predictions for the input samples.
The input samples are processed batch by batch.
# Arguments
x: the input data, as a Numpy array.
batch_size: integer.
verbose: verbosity mode, 0 or 1.
# Returns
A Numpy array of predictions.
"""
if not self.built:
self.build()
return self.model.predict(x, batch_size=batch_size, verbose=verbose)
def predict_on_batch(self, x):
"""Returns predictions for a single batch of samples.
# Arguments
x: input data, as a Numpy array or list of Numpy arrays
(if the model has multiple inputs).
# Returns
A Numpy array of predictions.
"""
if not self.built:
self.build()
return self.model.predict_on_batch(x)
def train_on_batch(self, x, y, class_weight=None,
sample_weight=None):
"""Single gradient update over one batch of samples.
# Arguments
x: input data, as a Numpy array or list of Numpy arrays
(if the model has multiple inputs).
y: labels, as a Numpy array.
class_weight: dictionary mapping classes to a weight value,
used for scaling the loss function (during training only).
sample_weight: sample weights, as a Numpy array.
# Returns
Scalar training loss (if the model has no metrics)
or list of scalars (if the model computes other metrics).
The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
# Raises
RuntimeError: if the model was never compiled.
"""
if not self.built:
raise RuntimeError('The model needs to be compiled '
'before being used.')
return self.model.train_on_batch(x, y,
sample_weight=sample_weight,
class_weight=class_weight)
def test_on_batch(self, x, y,
sample_weight=None):
"""Evaluates the model over a single batch of samples.
# Arguments
x: input data, as a Numpy array or list of Numpy arrays
(if the model has multiple inputs).
y: labels, as a Numpy array.
sample_weight: sample weights, as a Numpy array.
# Returns
Scalar test loss (if the model has no metrics)
or list of scalars (if the model computes other metrics).
The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
# Raises
RuntimeError: if the model was never compiled.
"""
if not self.built:
raise RuntimeError('The model needs to be compiled '
'before being used.')
return self.model.test_on_batch(x, y,
sample_weight=sample_weight)
def predict_proba(self, x, batch_size=32, verbose=1):
"""Generates class probability predictions for the input samples.
The input samples are processed batch by batch.
# Arguments
x: input data, as a Numpy array or list of Numpy arrays
(if the model has multiple inputs).
batch_size: integer.
verbose: verbosity mode, 0 or 1.
# Returns
A Numpy array of probability predictions.
"""
preds = self.predict(x, batch_size, verbose)
if preds.min() < 0. or preds.max() > 1.:
warnings.warn('Network returning invalid probability values. '
'The last layer might not normalize predictions '
'into probabilities '