def CNorm(vst_onlyTokens,
dl_terms,
dl_associations,
vso,
nbEpochs=30,
batchSize=64,
l_numberOfFilters=[4000],
l_filterSizes=[1],
phraseMaxSize=15):
# Preparing data for SLFNN and S-CNN components:
dataSCNN, labels, l_unkownTokens, l_uncompleteExpressions = prepare2D_data(
vst_onlyTokens, dl_terms, dl_associations, vso, phraseMaxSize)
dataSLFNN = numpy.zeros((dataSCNN.shape[0], dataSCNN.shape[2]))
for i in range(dataSCNN.shape[0]):
numberOfToken = 0
for embedding in dataSCNN[i]:
if not numpy.any(embedding):
pass
else:
numberOfToken += 1
dataSLFNN[i] += embedding
if numberOfToken > 0:
dataSLFNN[i] = dataSLFNN[i] / numberOfToken
# Input layers:
inputLP = Input(shape=dataSLFNN.shape[1])
inputCNN = Input(shape=[dataSCNN.shape[1], dataSCNN.shape[2]])
# SLFNN component:
ontoSpaceSize = labels.shape[2]
denseLP = layers.Dense(
units=ontoSpaceSize,
use_bias=True,
kernel_initializer=initializers.GlorotUniform())(inputLP)
modelLP = Model(inputs=inputLP, outputs=denseLP)
# Shallow-CNN component:
l_subLayers = list()
for i, filterSize in enumerate(l_filterSizes):
convLayer = (layers.Conv1D(
l_numberOfFilters[i],
filterSize,
strides=1,
kernel_initializer=initializers.GlorotUniform()))(inputCNN)
outputSize = phraseMaxSize - filterSize + 1
pool = (layers.MaxPool1D(pool_size=outputSize))(convLayer)
activationLayer = (layers.LeakyReLU(alpha=0.3))(pool)
l_subLayers.append(activationLayer)
if len(l_filterSizes) > 1:
concatenateLayer = (layers.Concatenate(axis=-1))(
l_subLayers) # axis=-1 // concatenating on the last dimension
else:
concatenateLayer = l_subLayers[0]
denseLayer = layers.Dense(
ontoSpaceSize,
kernel_initializer=initializers.GlorotUniform())(concatenateLayer)
modelCNN = Model(inputs=inputCNN, outputs=denseLayer)
convModel = Model(inputs=inputCNN, outputs=concatenateLayer)
fullmodel = models.Sequential()
fullmodel.add(convModel)
# Combination of the two components:
combinedLayer = layers.average([modelLP.output, modelCNN.output])
fullModel = Model(inputs=[inputLP, inputCNN], outputs=combinedLayer)
fullModel.summary()
# Compile and train:
fullModel.compile(
optimizer=optimizers.Nadam(),
loss=losses.LogCosh(),
metrics=[metrics.CosineSimilarity(),
metrics.MeanSquaredError()])
fullModel.fit([dataSLFNN, dataSCNN],
labels,
epochs=nbEpochs,
batch_size=batchSize)
return fullModel, vso, l_unkownTokens
def scheduler(epoch):
if epoch <= 2:
return 0.0005 * strategy.num_replicas_in_sync
elif epoch <= 4:
return 0.0002 * strategy.num_replicas_in_sync
else:
return 0.0001 * strategy.num_replicas_in_sync
#Add this for TensorBoard
#callbacks = [tf.keras.callbacks.TensorBoard(log_dir='./logs', profile_batch = 0)]
callbacks = [tf.keras.callbacks.LearningRateScheduler(scheduler)]
print('starting at ', time())
history = model.fit(train, steps_per_epoch=steps_per_epoch, \
epochs=num_epochs, callbacks=callbacks, verbose=1)
print("finishing at:", time())
print("evaluating:", time())
model.evaluate(test, steps=validation_steps)
print("evaluated at:", time())
model_full_path = "/tmp/mymodel" + str(worker_number) + ".h5"
print("Training finished, now saving the model in h5 format to: " +
model_full_path)
model.save(model_full_path, save_format="h5")
print("model saved.\n")
#print("..saving the model in tf format (TF 2.0) to: " + model_full_path)
#tf.keras.models.save_model(model, "/tmp/mymodel"+ str(worker_number) + ".tf", save_format='tf')
#print("model saved.\n")
encoder_output, attention_weights = SelfAttention(
size=128, num_hops=10, use_penalization=False)(encoder_output)
elif config == 2:
encoder_output, attention_weights = Attention(
context='many-to-one', alignment_type='global')(attention_input)
encoder_output = Flatten()(encoder_output)
elif config == 3:
encoder_output, attention_weights = Attention(
context='many-to-one',
alignment_type='local-p*',
window_width=100,
score_function='scaled_dot')(attention_input)
encoder_output = Flatten()(encoder_output)
# Prediction Layer
Y = Dense(units=num_categories, activation='softmax')(encoder_output)
# Compile model
model = Model(inputs=X, outputs=Y)
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print(model.summary())
# Train multi-class classification model
model.fit(x=X_train,
y=Y_train,
validation_data=(X_test, Y_test),
epochs=num_epochs,
batch_size=batch_size)
class RelevanceModel:
def __init__(
self,
feature_config: FeatureConfig,
tfrecord_type: str,
file_io: FileIO,
scorer: Optional[ScorerBase] = None,
metrics: List[Union[Type[kmetrics.Metric], str]] = [],
optimizer: Optional[Optimizer] = None,
model_file: Optional[str] = None,
initialize_layers_dict: dict = {},
freeze_layers_list: list = [],
compile_keras_model: bool = False,
output_name: str = "score",
logger=None,
):
"""
Constructor to instantiate a RelevanceModel that can be used for
training and evaluating the search ML task
Parameters
----------
feature_config : `FeatureConfig` object
FeatureConfig object that defines the features to be loaded in the dataset
and the preprocessing functions to be applied to each of them
tfrecord_type : {"example", "sequence_example"}
Type of the TFRecord protobuf message used for TFRecordDataset
file_io : `FileIO` object
file I/O handler objects for reading and writing data
scorer : `ScorerBase` object
Scorer object that wraps an InteractionModel and converts
input features into scores
metrics : list
List of keras Metric classes that will be used for evaluating the trained model
optimizer : `Optimizer`
Tensorflow keras optimizer to be used for training the model
model_file : str, optional
Path to pretrained model file to be loaded for evaluation or retraining
initialize_layers_dict : dict, optional
Dictionary of tensorflow layer names mapped to the path of pretrained weights
Use this for transfer learning with pretrained weights
freeze_layers_list : list, optional
List of model layer names to be frozen
Use this for freezing pretrained weights from other ml4ir models
compile_keras_model : bool, optional
Whether the keras model loaded from disk should be compiled
with loss, metrics and an optimizer
output_name : str, optional
Name of the output tensorflow node that captures the score
logger : `Logger`, optional
logging handler for status messages
"""
self.feature_config: FeatureConfig = feature_config
self.logger: Logger = logger
self.output_name = output_name
self.scorer = scorer
self.tfrecord_type = tfrecord_type
self.file_io = file_io
if scorer:
self.max_sequence_size = scorer.interaction_model.max_sequence_size
else:
self.max_sequence_size = 0
# Load/Build Model
if model_file and not compile_keras_model:
"""
If a model file is specified, load it without compiling into a keras model
NOTE:
This will allow the model to be only used for inference and
cannot be used for retraining.
"""
self.model: Model = self.load(model_file)
self.is_compiled = False
else:
"""
Specify inputs to the model
Individual input nodes are defined for each feature
Each data point represents features for all records in a single query
"""
inputs: Dict[str, Input] = feature_config.define_inputs()
scores, train_features, metadata_features = scorer(inputs)
# Create model with functional Keras API
self.model = Model(inputs=inputs, outputs={self.output_name: scores})
self.model.output_names = [self.output_name]
# Get loss fn
loss_fn = scorer.loss.get_loss_fn(**metadata_features)
# Get metric objects
metrics_impl: List[Union[str, kmetrics.Metric]] = get_metrics_impl(
metrics=metrics, feature_config=feature_config, metadata_features=metadata_features
)
# Compile model
"""
NOTE:
Related Github issue: https://github.com/tensorflow/probability/issues/519
"""
self.model.compile(
optimizer=optimizer,
loss=loss_fn,
metrics=metrics_impl,
experimental_run_tf_function=False,
)
# Write model summary to logs
model_summary = list()
self.model.summary(print_fn=lambda x: model_summary.append(x))
if self.logger:
self.logger.info("\n".join(model_summary))
if model_file:
"""
If model file is specified, load the weights from the SavedModel
NOTE:
The architecture, loss and metrics of self.model need to
be the same as the loaded SavedModel
"""
self.load_weights(model_file)
# Initialize layer weights
for layer_name, layer_file in initialize_layers_dict.items():
layer = self.model.get_layer(layer_name)
layer.set_weights(self.file_io.load_numpy_array(layer_file, unzip=True))
self.logger.info("Setting {} weights from {}".format(layer_name, layer_file))
# Freeze layer weights
for layer_name in freeze_layers_list:
layer = self.model.get_layer(layer_name)
layer.trainable = False
self.logger.info("Freezing {} layer".format(layer_name))
self.is_compiled = True
@classmethod
def from_relevance_scorer(
cls,
feature_config: FeatureConfig,
interaction_model: InteractionModel,
model_config: dict,
loss: RelevanceLossBase,
metrics: List[Union[kmetrics.Metric, str]],
optimizer: Optimizer,
tfrecord_type: str,
file_io: FileIO,
model_file: Optional[str] = None,
initialize_layers_dict: dict = {},
freeze_layers_list: list = [],
compile_keras_model: bool = False,
output_name: str = "score",
logger=None,
):
"""
Create a RelevanceModel with default Scorer function
constructed from an InteractionModel
Parameters
----------
feature_config : `FeatureConfig` object
FeatureConfig object that defines the features to be loaded in the dataset
and the preprocessing functions to be applied to each of them
tfrecord_type : {"example", "sequence_example"}
Type of the TFRecord protobuf message used for TFRecordDataset
file_io : `FileIO` object
file I/O handler objects for reading and writing data
interaction_model : `InteractionModel` object
InteractionModel object that converts input features into a
dense feature representation
loss : `RelevanceLossBase` object
Loss object defining the final activation layer and the loss function
metrics : list
List of keras Metric classes that will be used for evaluating the trained model
optimizer : `Optimizer`
Tensorflow keras optimizer to be used for training the model
model_file : str, optional
Path to pretrained model file to be loaded for evaluation or retraining
initialize_layers_dict : dict, optional
Dictionary of tensorflow layer names mapped to the path of pretrained weights
Use this for transfer learning with pretrained weights
freeze_layers_list : list, optional
List of model layer names to be frozen
Use this for freezing pretrained weights from other ml4ir models
compile_keras_model : bool, optional
Whether the keras model loaded from disk should be compiled
with loss, metrics and an optimizer
output_name : str, optional
Name of the output tensorflow node that captures the score
logger : `Logger`, optional
logging handler for status messages
Returns
-------
RelevanceModel
RelevanceModel object with a default scorer build with a custom
InteractionModel
"""
assert isinstance(interaction_model, InteractionModel)
assert isinstance(loss, RelevanceLossBase)
scorer: ScorerBase = RelevanceScorer(
model_config=model_config,
interaction_model=interaction_model,
loss=loss,
output_name=output_name,
)
return cls(
scorer=scorer,
feature_config=feature_config,
metrics=metrics,
optimizer=optimizer,
tfrecord_type=tfrecord_type,
model_file=model_file,
initialize_layers_dict=initialize_layers_dict,
freeze_layers_list=freeze_layers_list,
compile_keras_model=compile_keras_model,
output_name=output_name,
file_io=file_io,
logger=logger,
)
@classmethod
def from_univariate_interaction_model(
cls,
model_config,
feature_config: FeatureConfig,
tfrecord_type: str,
loss: RelevanceLossBase,
metrics: List[Union[kmetrics.Metric, str]],
optimizer: Optimizer,
feature_layer_keys_to_fns: dict = {},
model_file: Optional[str] = None,
initialize_layers_dict: dict = {},
freeze_layers_list: list = [],
compile_keras_model: bool = False,
output_name: str = "score",
max_sequence_size: int = 0,
file_io: FileIO = None,
logger=None,
):
"""
Create a RelevanceModel with default UnivariateInteractionModel
Parameters
----------
feature_config : `FeatureConfig` object
FeatureConfig object that defines the features to be loaded in the dataset
and the preprocessing functions to be applied to each of them
model_config : dict
dictionary defining the dense model architecture
tfrecord_type : {"example", "sequence_example"}
Type of the TFRecord protobuf message used for TFRecordDataset
file_io : `FileIO` object
file I/O handler objects for reading and writing data
loss : `RelevanceLossBase` object
Loss object defining the final activation layer and the loss function
metrics : list
List of keras Metric classes that will be used for evaluating the trained model
optimizer : `Optimizer`
Tensorflow keras optimizer to be used for training the model
feature_layer_keys_to_fns : dict
Dictionary of custom feature transformation functions to be applied
on the input features as part of the InteractionModel
model_file : str, optional
Path to pretrained model file to be loaded for evaluation or retraining
initialize_layers_dict : dict, optional
Dictionary of tensorflow layer names mapped to the path of pretrained weights
Use this for transfer learning with pretrained weights
freeze_layers_list : list, optional
List of model layer names to be frozen
Use this for freezing pretrained weights from other ml4ir models
compile_keras_model : bool, optional
Whether the keras model loaded from disk should be compiled
with loss, metrics and an optimizer
output_name : str, optional
Name of the output tensorflow node that captures the score
max_sequence_size : int, optional
Maximum length of the sequence to be used for SequenceExample protobuf objects
logger : `Logger`, optional
logging handler for status messages
Returns
-------
RelevanceModel
RelevanceModel object with a UnivariateInteractionModel
"""
interaction_model: InteractionModel = UnivariateInteractionModel(
feature_config=feature_config,
feature_layer_keys_to_fns=feature_layer_keys_to_fns,
tfrecord_type=tfrecord_type,
max_sequence_size=max_sequence_size,
)
return cls.from_relevance_scorer(
interaction_model=interaction_model,
model_config=model_config,
feature_config=feature_config,
loss=loss,
metrics=metrics,
optimizer=optimizer,
tfrecord_type=tfrecord_type,
model_file=model_file,
initialize_layers_dict=initialize_layers_dict,
freeze_layers_list=freeze_layers_list,
compile_keras_model=compile_keras_model,
output_name=output_name,
file_io=file_io,
logger=logger,
)
def define_scheduler_as_callback(self, monitor_metric, model_config):
"""
Adding reduce lr on plateau as a callback if specified
Parameters
----------
monitor_metric : string
The metric to be monitored by the callback
model_config : dict
dictionary defining the dense model architecture
Returns
-------
reduce_lr
The created scheduler callback object.
"""
if model_config and 'lr_schedule' in model_config:
lr_schedule = model_config['lr_schedule']
lr_schedule_key = lr_schedule['key']
if lr_schedule_key == LearningRateScheduleKey.REDUCE_LR_ON_PLATEAU:
if monitor_metric is None:
reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(factor=lr_schedule.get('factor', 0.5),
patience=lr_schedule.get('patience', 5),
min_lr=lr_schedule.get('min_lr', 0.0001),
mode=lr_schedule.get('mode', 'auto'),
verbose=1)
else:
if not monitor_metric.startswith("val_"):
monitor_metric = "val_{}".format(monitor_metric)
reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor=monitor_metric,
factor=lr_schedule.get('factor', 0.5),
patience=lr_schedule.get('patience', 5),
min_lr=lr_schedule.get('min_lr', 0.0001),
mode=lr_schedule.get('mode', 'auto'),
verbose=1)
return reduce_lr
def fit(
self,
dataset: RelevanceDataset,
num_epochs: int,
models_dir: str,
logs_dir: Optional[str] = None,
logging_frequency: int = 25,
monitor_metric: str = "",
monitor_mode: str = "",
patience=2,
):
"""
Trains model for defined number of epochs
and returns the training and validation metrics as a dictionary
Parameters
----------
dataset : `RelevanceDataset` object
RelevanceDataset object to be used for training and validation
num_epochs : int
Value specifying number of epochs to train for
models_dir : str
Directory to save model checkpoints
logs_dir : str, optional
Directory to save model logs
If set to False, no progress logs will be written
logging_frequency : int, optional
Every #batches to log results
monitor_metric : str, optional
Name of the metric to monitor for early stopping, checkpointing
monitor_mode : {"max", "min"}
Whether to maximize or minimize the monitoring metric
patience : int
Number of epochs to wait before early stopping
Returns
-------
train_metrics : dict
Train and validation metrics in a single dictionary
where key is metric name and value is floating point metric value.
This dictionary will be used for experiment tracking for each ml4ir run
"""
if not monitor_metric.startswith("val_"):
monitor_metric = "val_{}".format(monitor_metric)
callbacks_list: list = self._build_callback_hooks(
models_dir=models_dir,
logs_dir=logs_dir,
is_training=True,
logging_frequency=logging_frequency,
monitor_mode=monitor_mode,
monitor_metric=monitor_metric,
patience=patience,
)
if self.is_compiled:
history = self.model.fit(
x=dataset.train,
validation_data=dataset.validation,
epochs=num_epochs,
verbose=True,
callbacks=callbacks_list,
)
# Write metrics for experiment tracking
# Returns a dictionary
train_metrics = dict()
for metric, value in history.history.items():
if not metric.startswith("val_"):
"""
NOTE:
Prepend "train_" to metrics on training dataset
to differentiate from validation and test metrics
in the final experiment results
"""
# History is a dict of key: list(values per epoch)
# We are capturing the metrics of the last epoch (-1)
train_metrics["train_{}".format(metric)] = value[-1]
else:
train_metrics[metric] = value[-1]
return train_metrics
else:
raise NotImplementedError(
"The model could not be trained. "
"Check if the model was compiled correctly."
" Training loaded SavedModel is not currently supported."
)
def predict(
self,
test_dataset: data.TFRecordDataset,
inference_signature: str = "serving_default",
additional_features: dict = {},
logs_dir: Optional[str] = None,
logging_frequency: int = 25,
):
"""
Predict the scores on the test dataset using the trained model
Parameters
----------
test_dataset : `Dataset` object
`Dataset` object for which predictions are to be made
inference_signature : str, optional
If using a SavedModel for prediction, specify the inference signature to be used for computing scores
additional_features : dict, optional
Dictionary containing new feature name and function definition to
compute them. Use this to compute additional features from the scores.
For example, converting ranking scores for each document into ranks for
the query
logs_dir : str, optional
Path to directory to save logs
logging_frequency : int
Value representing how often(in batches) to log status
Returns
-------
`pd.DataFrame`
pandas DataFrame containing the predictions on the test dataset
made with the `RelevanceModel`
"""
if logs_dir:
outfile = os.path.join(logs_dir, RelevanceModelConstants.MODEL_PREDICTIONS_CSV_FILE)
# Delete file if it exists
self.file_io.rm_file(outfile)
_predict_fn = get_predict_fn(
model=self.model,
tfrecord_type=self.tfrecord_type,
feature_config=self.feature_config,
inference_signature=inference_signature,
is_compiled=self.is_compiled,
output_name=self.output_name,
features_to_return=self.feature_config.get_features_to_log(),
additional_features=additional_features,
max_sequence_size=self.max_sequence_size,
)
predictions_df_list = list()
batch_count = 0
for predictions_dict in test_dataset.map(_predict_fn).take(-1):
predictions_df = pd.DataFrame(predictions_dict)
if logs_dir:
np.set_printoptions(
formatter={"all": lambda x: str(x.decode("utf-8"))
if isinstance(x, bytes) else str(x)},
linewidth=sys.maxsize, threshold=sys.maxsize) # write the full line in the csv not the truncated version.
# Decode bytes features to strings
for col in predictions_df.columns:
if isinstance(predictions_df[col].values[0], bytes):
predictions_df[col] = predictions_df[col].str.decode("utf8")
if os.path.isfile(outfile):
predictions_df.to_csv(outfile, mode="a", header=False, index=False)
else:
# If writing first time, write headers to CSV file
predictions_df.to_csv(outfile, mode="w", header=True, index=False)
else:
predictions_df_list.append(predictions_df)
batch_count += 1
if batch_count % logging_frequency == 0:
self.logger.info("Finished predicting scores for {} batches".format(batch_count))
predictions_df = None
if logs_dir:
self.logger.info("Model predictions written to -> {}".format(outfile))
else:
predictions_df = pd.concat(predictions_df_list)
return predictions_df
def evaluate(
self,
test_dataset: data.TFRecordDataset,
inference_signature: str = None,
additional_features: dict = {},
group_metrics_min_queries: int = 50,
logs_dir: Optional[str] = None,
logging_frequency: int = 25,
compute_intermediate_stats: bool = True,
):
"""
Evaluate the RelevanceModel
Parameters
----------
test_dataset: an instance of tf.data.dataset
inference_signature : str, optional
If using a SavedModel for prediction, specify the inference signature to be used for computing scores
additional_features : dict, optional
Dictionary containing new feature name and function definition to
compute them. Use this to compute additional features from the scores.
For example, converting ranking scores for each document into ranks for
the query
group_metrics_min_queries : int, optional
Minimum count threshold per group to be considered for computing
groupwise metrics
logs_dir : str, optional
Path to directory to save logs
logging_frequency : int
Value representing how often(in batches) to log status
compute_intermediate_stats : bool
Determines if group metrics and other intermediate stats on the test set should be computed
Returns
-------
df_overall_metrics : `pd.DataFrame` object
`pd.DataFrame` containing overall metrics
df_groupwise_metrics : `pd.DataFrame` object
`pd.DataFrame` containing groupwise metrics if
group_metric_keys are defined in the FeatureConfig
metrics_dict : dict
metrics as a dictionary of metric names mapping to values
Notes
-----
You can directly do a `model.evaluate()` only if the keras model is compiled
Override this method to implement your own evaluation metrics.
"""
if self.is_compiled:
metrics_dict = self.model.evaluate(test_dataset)
return None, None, dict(zip(self.model.metrics_names, metrics_dict))
else:
raise NotImplementedError
def run_ttest(self, mean, variance, n, ttest_pvalue_threshold):
"""
Compute the paired t-test statistic and its p-value given mean, standard deviation and sample count
Parameters
----------
mean: float
The mean of the rank differences for the entire dataset
variance: float
The variance of the rank differences for the entire dataset
n: int
The number of samples in the entire dataset
ttest_pvalue_threshold: float
P-value threshold for student t-test
metrics_dict: dict
dictionary of metrics to keep track
Returns
-------
t_test_metrics_dict: Dictionary
A dictionary with the t-test metrics recorded.
"""
raise NotImplementedError
def save(
self,
models_dir: str,
preprocessing_keys_to_fns={},
postprocessing_fn=None,
required_fields_only: bool = True,
pad_sequence: bool = False,
sub_dir: str = "final",
dataset: Optional[RelevanceDataset] = None,
experiment_details: Optional[dict] = None
):
"""
Save the RelevanceModel as a tensorflow SavedModel to the `models_dir`
There are two different serving signatures currently used to save the model:
* `default`: default keras model without any pre/post processing wrapper
* `tfrecord`: serving signature that allows keras model to be served using TFRecord proto messages.
Allows definition of custom pre/post processing logic
Additionally, each model layer is also saved as a separate numpy zipped
array to enable transfer learning with other ml4ir models.
Parameters
----------
models_dir : str
path to directory to save the model
preprocessing_keys_to_fns : dict
dictionary mapping function names to tf.functions that should be
saved in the preprocessing step of the tfrecord serving signature
postprocessing_fn: function
custom tensorflow compatible postprocessing function to be used at serving time.
Saved as part of the postprocessing layer of the tfrecord serving signature
required_fields_only: bool
boolean value defining if only required fields
need to be added to the tfrecord parsing function at serving time
pad_sequence: bool, optional
Value defining if sequences should be padded for SequenceExample proto inputs at serving time.
Set this to False if you want to not handle padded scores.
sub_dir: str, optional
sub directory name to save the model into
dataset : `RelevanceDataset` object
RelevanceDataset object that can optionally be passed to be used by downstream jobs
that want to save the data along with the model.
Note that this feature is currently unimplemented and is upto the users to override
and customize.
experiment_details: dict
Dictionary containing metadata and results about the current experiment
Notes
-----
All the functions passed under `preprocessing_keys_to_fns` here must be
serializable tensor graph operations
"""
model_file = os.path.join(models_dir, sub_dir)
# Save model with default signature
self.model.save(filepath=os.path.join(model_file, "default"))
"""
Save model with custom signatures
Currently supported
- signature to read TFRecord SequenceExample inputs
"""
self.model.save(
filepath=os.path.join(model_file, "tfrecord"),
signatures=define_serving_signatures(
model=self.model,
tfrecord_type=self.tfrecord_type,
feature_config=self.feature_config,
preprocessing_keys_to_fns=preprocessing_keys_to_fns,
postprocessing_fn=postprocessing_fn,
required_fields_only=required_fields_only,
pad_sequence=pad_sequence,
max_sequence_size=self.max_sequence_size,
),
)
# Save individual layer weights
self.file_io.make_directory(os.path.join(model_file, "layers"), clear_dir=True)
for layer in self.model.layers:
try:
self.file_io.save_numpy_array(
np_array=layer.get_weights(),
file_path=os.path.join(model_file, "layers", "{}.npz".format(layer.name)),
zip=True,
)
except FileNotFoundError:
self.logger.warning(
"Error saving layer: {} due to FileNotFoundError. Skipping...".format(layer.name))
self.logger.info("Final model saved to : {}".format(model_file))
def load(self, model_file: str) -> Model:
"""
Loads model from the SavedModel file specified
Parameters
----------
model_file : str
path to file with saved tf keras model
Returns
-------
`tf.keras.Model`
Tensorflow keras model loaded from file
Notes
-----
Retraining currently not supported!
Would require compiling the model with the right loss and optimizer states
"""
"""
NOTE:
There is currently a bug in Keras Model with saving/loading
models with custom losses and metrics.
Therefore, we are currently loading the SavedModel with compile=False
The saved model signatures can be used for inference at serving time
Ref:
https://github.com/keras-team/keras/issues/5916
https://github.com/tensorflow/tensorflow/issues/32348
https://github.com/keras-team/keras/issues/3977
"""
model = tf.keras.models.load_model(model_file, compile=False)
self.logger.info("Successfully loaded SavedModel from {}".format(model_file))
self.logger.warning("Retraining is not yet supported. Model is loaded with compile=False")
return model
def load_weights(self, model_file: str):
"""
Load saved model with compile=False
Parameters
----------
model_file : str
path to file with saved tf keras model
"""
loaded_model = self.load(model_file)
# Set weights of Keras model from the loaded model weights
self.model.set_weights(loaded_model.get_weights())
self.logger.info("Weights have been set from SavedModel. RankingModel can now be trained.")
def _build_callback_hooks(
self,
models_dir: str,
logs_dir: Optional[str] = None,
is_training=True,
logging_frequency=25,
monitor_metric: str = "",
monitor_mode: str = "",
patience=2,
):
"""
Build callback hooks for the training and evaluation loop
Parameters
----------
models_dir : str
Path to directory to save model checkpoints
logs_dir : str
Path to directory to save tensorboard logs
is_training : bool, optional
Whether we are building callbacks for training or evaluation
logging_frequency : int, optional
How often, in number of epochs, to log training and evaluation progress
monitor_metric : str, optional
Name of metric to be used for ModelCheckpoint and EarlyStopping callbacks
monitor_mode : {"max", "min"}, optional
Mode for maximizing or minimizing the ModelCheckpoint and EarlyStopping
patience : int, optional
Number of epochs to wait before early stopping if metric change is below tolerance
Returns
-------
callbacks_list : list
List of callbacks to be used with the RelevanceModel training and evaluation
"""
callbacks_list: list = list()
if is_training:
# Model checkpoint
if models_dir and monitor_metric:
checkpoints_path = os.path.join(
models_dir, RelevanceModelConstants.CHECKPOINT_FNAME
)
cp_callback = callbacks.ModelCheckpoint(
filepath=checkpoints_path,
save_weights_only=False,
verbose=1,
save_best_only=True,
mode=monitor_mode,
monitor=monitor_metric,
)
callbacks_list.append(cp_callback)
# Early Stopping
if monitor_metric:
early_stopping_callback = callbacks.EarlyStopping(
monitor=monitor_metric,
mode=monitor_mode,
patience=patience,
verbose=1,
restore_best_weights=True,
)
callbacks_list.append(early_stopping_callback)
# TensorBoard
if logs_dir:
tensorboard_callback = callbacks.TensorBoard(
log_dir=logs_dir, histogram_freq=1, update_freq=5
)
callbacks_list.append(tensorboard_callback)
# Debugging/Logging
callbacks_list.append(DebuggingCallback(self.logger, logging_frequency))
# Adding lr scheduler as a callback; used for `ReduceLROnPlateau` which we treat today as a callback
scheduler_callback = self.define_scheduler_as_callback(monitor_metric, self.scorer.model_config)
if scheduler_callback:
callbacks_list.append(scheduler_callback)
# Add more here
return callbacks_list
def calibrate(self, relevance_dataset, logger, logs_dir_local, **kwargs)\
-> Tuple[np.ndarray, ...]:
"""Calibrate model with temperature scaling
Parameters
----------
relevance_dataset: RelevanceDataset
RelevanceDataset object to be used for training and evaluating temperature scaling
logger: Logger
Logger object to log events
logs_dir_local: str
path to save the calibration results. (zipped csv file containing original
probabilities, calibrated probabilities, ...)
Returns
-------
`Union[np.ndarray, Tuple[np.ndarray, ...]]`
optimizer output containing temperature value learned during temperature scaling
"""
logger.info("=" * 50)
logger.info("Calibrating the model with temperature scaling")
return temperature_scale(model=self.model,
scorer=self.scorer,
dataset=relevance_dataset,
logger=logger,
logs_dir_local=logs_dir_local,
file_io=self.file_io,
**kwargs)
def add_temperature_layer(self, temperature: float = 1.0, layer_name: str = 'temperature_layer'):
"""Add temperature layer to the input of last activation (softmax) layer
Parameters
----------
self: RelevanceModel
input RelevanceModel object that its last layer inputs will be divided by a
temperature value
temperature: float
a scalar value to scale the last activation layer inputs
layer_name: str
name of the temperature scaling layer
Returns
-------
`RelevanceModel`
updated RelevanceModel object with temperature
"""
# get last layer's output --> MUST **NOT** BE AN ACTIVATION (e.g. SOFTMAX) LAYER
final_layer_name = self.scorer.model_config['layers'][-1]['name']
final_layer = self.model.get_layer(name=final_layer_name).output
temperature_layer = TemperatureScalingLayer(name=layer_name,
temperature=temperature)(final_layer)
# using the `last layer` as final activation function before computing loss
idx_activation = -1
if len(self.model.layers) > 0 and isinstance(self.model.layers[idx_activation],
tf.keras.layers.Activation):
# creating new activation layer
activation_layer_name = self.model.get_layer(index=idx_activation).name
activation_function = self.model.get_layer(index=idx_activation).activation
activation_layer = tf.keras.layers.Activation(
activation_function, name=activation_layer_name)(temperature_layer)
# creating new keras Functional API model
self.model = Model(self.model.inputs, activation_layer)
self.logger.info(f'Temperature Scaling layer added and new Functional API model'
f' replaced; temperature = {temperature}.')
else:
self.logger.info("Skipping adding Temperature Scaling layer because no activation "
"exist in the last layer of Keras original model!")
# creating final model
solver = Model(grid, digit_placeholders) # build the whole model
# compiling created model
solver.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
# grid ---> model ---> degit_placeholders
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# Train Model
# First train
# in the firs training we don't delete any digit
solver.fit(
delete_digits(Xtrain, 0), # we don't delete any digit for now
[Ytrain[:, i, j, :] for i in range(9)
for j in range(9)], # each digit of solution
batch_size=128,
epochs=1, # 1 epoch should be enough for the task
verbose=1,
)
# Second train
early_stop = EarlyStopping(patience=2, verbose=1)
i = 1
for nb_epochs, nb_delete in zip(
[
5, 10, 10
], #[1, 2, 3, 4, 6, 8, 10, 10, 10, 10, 10, 15, 15, 15, 15, 15, 15, 20, 25, 30], # epochs for each round
[
20, 55, 58
] #[1, 2, 3, 4, 6, 8, 10, 12, 14, 17, 20, 23, 25, 30, 35, 40, 45, 50, 55, 60] # digit to pull off
):
infomax = DeepGraphInfomax(gcn_model, corrupted_generator)
x_in, x_out = infomax.in_out_tensors()
# train model
model = Model(inputs=x_in, outputs=x_out)
model.compile(loss=tf.nn.sigmoid_cross_entropy_with_logits,
optimizer=Adam(lr=1e-3))
model.summary()
# create a model image, print to file
plot_model(model, show_shapes=True, to_file="model.png")
epochs = 1000
es = EarlyStopping(monitor="loss", min_delta=0, patience=20)
# next line triggers the model to train, run time 10 mins with a GTX 1070
# 90 minutes on CPU
history = model.fit(gen, epochs=epochs, verbose=0, callbacks=[es])
plot_history(history)
# playing with the embedding vectors, obtain trained node embedding model
x_emb_in, x_emb_out = gcn_model.in_out_tensors()
# for full batch models, squeeze out the batch dim (which is 1)
x_out = tf.squeeze(x_emb_out, axis=0)
emb_model = Model(inputs=x_emb_in, outputs=x_out)
# get the target, document type is the best I can think of at short notice
# data from `notebooks/recommend-content-dgi/01_data_pull_use.py`
text_df = pd.read_csv('data/processed/text_use_large_2000_df.csv')
text_df.head()
text_df['document_type'].value_counts()
return precision
precision = precision(y_true, y_pred)
recall = recall(y_true, y_pred)
return 2*((precision*recall)/(precision+recall+K.epsilon()))
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=[f1, 'accuracy'])
history = model.fit([X_train_II,X_train_V1], y_train,
epochs=epochs,
batch_size=batch_size,
verbose=1,
validation_data=([X_test_II,X_test_V1], y_test))
y_pred = model.predict([X_test_II,X_test_V1])
test_acc = model.evaluate([X_test_II,X_test_V1], y_test, verbose=1)
print("keras acc: ",test_acc)
y_pred =(y_pred>0.4)
list(y_pred)
# print(classification_report(NSR_y_test.argmax(axis=1), y_pred.argmax(axis=1)))
print(classification_report(y_test, y_pred))
class SymmetricAutoencoder:
def __init__(self,
dims,
act='relu',
init='glorot_uniform',
noise_stddev=0.0):
n_stacks = len(dims) - 1
# input
x = Input(shape=(dims[0], ), name='input')
h = GaussianNoise(noise_stddev)(x)
# internal layers in encoder
for i in range(n_stacks - 1):
h = Dense(dims[i + 1],
activation=act,
kernel_initializer=init,
name='encoder_%d' % i)(h)
# h = BatchNormalization(momentum=0.66)(h)
# h = Dropout(0.3)(h)
# hidden layer
h = Dense(
dims[-1],
kernel_initializer=init,
name='encoder_%d' % (n_stacks - 1))(
h) # hidden bottleneck layer, features are extracted from here
y = h
# internal layers in decoder
for i in range(n_stacks - 1, 0, -1):
y = Dense(dims[i],
activation=act,
kernel_initializer=init,
name='decoder_%d' % i)(y)
# y = BatchNormalization(momentum=0.66)(y)
# y = Dropout(0.3)(y)
# output
y = Dense(dims[0], kernel_initializer=init, name='decoder_0')(y)
self.model = Model(inputs=x, outputs=y, name='AE')
self.encoder = Model(inputs=x, outputs=h, name='encoder')
def load_weights(self, weights):
self.model.load_weights(weights, by_name=True)
def load_encoder(self, model):
self.encoder = load_model(model)
def extract_features(self, x):
return self.encoder.predict(x)
def predict(self, x):
y = self.model.predict(x, verbose=0, steps=1)
return y
def compile(self, optimizer='sgd', loss='mse'):
self.model.compile(optimizer=optimizer, loss=loss)
def summary(self):
self.model.summary()
def evaluate(self, x):
predictions = self.model.predict(x)
mse = np.mean(np.power(x - predictions, 2), axis=1)
error_df = pd.DataFrame({'reconstruction_error': mse})
print('MSE: {}'.format(np.mean(mse)))
print(error_df.describe())
def fit(self,
dataset,
epochs=10,
steps_per_epoch=30,
validation_data=None,
validation_steps=None,
save_dir='results/',
file_name='ae_weights.h5',
log_name='Autoencoder'):
tensorboard = tf.keras.callbacks.TensorBoard(
log_dir=(save_dir + "{}".format(log_name)))
checkpoint = ModelCheckpoint(save_dir +
"ae_weights.{epoch:02d}-{loss:.5f}.h5",
monitor='loss',
save_weights_only=True,
period=10)
self.model.fit(dataset,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
validation_data=validation_data,
validation_steps=validation_steps,
callbacks=[checkpoint, tensorboard])
self.model.save_weights(save_dir + file_name)
print('Autoencoder weights are saved to %s/%s', save_dir, file_name)
class VariationalAutoencoder:
def __init__(self,
dims,
act='relu',
init='glorot_uniform',
noise_stddev=0.0):
n_stacks = len(dims) - 1
# input
x = Input(shape=(dims[0], ), name='input')
encoder = GaussianNoise(noise_stddev)(x)
# internal layers in encoder
for i in range(n_stacks - 1):
encoder = Dense(dims[i + 1],
activation=act,
kernel_initializer=init,
name='encoder_%d' % i)(encoder)
encoder = BatchNormalization(momentum=0.66)(encoder)
# encoder = Dropout(0.3)(h)
# variational part
z_mean = Dense(dims[-1], name='z_mean')(encoder)
z_log_var = Dense(dims[-1], name='z_log_var')(encoder)
# use reparameterization trick to push the sampling out as input
z = Lambda(self.sampling, output_shape=(dims[-1], ),
name='z')([z_mean, z_log_var])
encoder = Model(x, [z_mean, z_log_var, z], name='encoder')
encoder.summary()
latent_inputs = Input(shape=(dims[-1], ), name='z_sampling')
y = latent_inputs
# internal layers in decoder
for i in range(n_stacks - 1, 0, -1):
y = Dense(dims[i],
activation=act,
kernel_initializer=init,
name='decoder_%d' % i)(y)
# y = BatchNormalization(momentum=0.66)(decoder)
# y = Dropout(0.3)(decoder)
# output
y = Dense(dims[0], kernel_initializer=init, name='decoder_0')(y)
decoder = Model(latent_inputs, y, name='decoder')
decoder.summary()
outputs = decoder(encoder(x)[2])
self.model = Model(inputs=x, outputs=outputs, name='VAE')
self.encoder = encoder
def get_encoder(self):
return Model(inputs=self.model.input,
outputs=self.model.get_layer("encoder").output)
@staticmethod
def sampling(args):
"""Reparameterization trick by sampling from an isotropic unit Gaussian.
# Arguments
args (tensor): mean and log of variance of Q(z|X)
# Returns
z (tensor): sampled latent vector
"""
z_mean, z_log_var = args
batch = K.shape(z_mean)[0]
dim = K.int_shape(z_mean)[1]
# by default, random_normal has mean = 0 and std = 1.0
epsilon = K.random_normal(shape=(batch, dim))
return z_mean + K.exp(0.5 * z_log_var) * epsilon
def load_weights(self, weights):
self.model.load_weights(weights, by_name=True)
def load_encoder(self, model):
self.encoder = load_model(model)
def extract_features(self, x):
return self.encoder.predict(x)
def predict(self, x):
y = self.model.predict(x, verbose=0, steps=1)
return y
def compile(self, optimizer='sgd', loss='mse'):
self.model.compile(optimizer=optimizer, loss=loss)
def summary(self):
self.model.summary()
def evaluate(self, x):
predictions = self.model.predict(x)
mse = np.mean(np.power(x - predictions, 2), axis=1)
error_df = pd.DataFrame({'reconstruction_error': mse})
print('MSE: {}'.format(np.mean(mse)))
print(error_df.describe())
def fit(self,
dataset,
epochs=10,
steps_per_epoch=30,
validation_data=None,
validation_steps=None,
save_dir='results/',
file_name='vae_weights.h5',
log_name='Variational Autoencoder'):
tensorboard = tf.keras.callbacks.TensorBoard(
log_dir=(save_dir + "{}".format(log_name)))
checkpoint = ModelCheckpoint(save_dir +
"ae_weights.{epoch:02d}-{loss:.5f}.h5",
monitor='loss',
save_weights_only=True,
period=10)
self.model.fit(dataset,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
validation_data=validation_data,
validation_steps=validation_steps,
callbacks=[checkpoint, tensorboard])
self.model.save_weights(save_dir + file_name)
print('Autoencoder weights are saved to %s/%s', save_dir, file_name)
from tensorflow.keras import Model
from tensorflow.keras.layers import Input, Dense
input1 = Input(shape=(13, ))
dense1 = Dense(128, activation='relu')(input1)
dense1 = Dense(64, activation='relu')(dense1)
dense1 = Dense(64, activation='relu')(dense1)
dense1 = Dense(64, activation='relu')(dense1)
output1 = Dense(1)(dense1)
model = Model(inputs=input1, outputs=output1)
#3. compile and fit
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
model.fit(x_train,
y_train,
batch_size=4,
epochs=150,
verbose=1,
validation_split=0.2)
#4. evalutate and predict
mse, mae = model.evaluate(x_test, y_test, batch_size=4)
print("mse :", mse, "\nmae :", mae)
y_predict = model.predict(x_test)
# RMSE 구하기
from sklearn.metrics import mean_squared_error
def RMSE(y_test, y_predict):
return np.sqrt(mean_squared_error(y_test, y_predict))
class MyModel:
def __init__(self,
input_size,
vocab_size,
greedy=False,
beam_width=10,
top_paths=1,
stop_tolerance=20,
reduce_tolerance=15):
self.input_size = input_size
self.vocab_size = vocab_size
self.model = None
self.greedy = greedy
self.beam_width = beam_width
self.top_paths = max(1, top_paths)
self.stop_tolerance = stop_tolerance
self.reduce_tolerance = reduce_tolerance
def summary(self, output=None, target=None):
self.model.summary()
if target is not None:
os.makedirs(output, exist_ok=True)
with open(os.path.join(output, target), "w") as f:
with redirect_stdout(f):
self.model.summary()
def load_checkpoint(self, target):
if os.path.isfile(target):
if self.model is None:
self.compile()
self.model.load_weights(target)
def get_callbacks(self, logdir, checkpoint, monitor="val_loss", verbose=0):
callbacks = [
CSVLogger(filename=os.path.join(logdir, "epochs.log"),
separator=";",
append=True),
TensorBoard(log_dir=logdir,
histogram_freq=10,
profile_batch=0,
write_graph=True,
write_images=False,
update_freq="epoch"),
ModelCheckpoint(filepath=checkpoint,
monitor=monitor,
save_best_only=True,
save_weights_only=True,
verbose=verbose),
EarlyStopping(monitor=monitor,
min_delta=1e-8,
patience=self.stop_tolerance,
restore_best_weights=True,
verbose=verbose),
ReduceLROnPlateau(monitor=monitor,
min_delta=1e-8,
factor=0.2,
patience=self.reduce_tolerance,
verbose=verbose)
]
return callbacks
def compile(self, learning_rate=None, initial_step=0):
# define inputs, outputs and optimizer of the chosen architecture
inputs, outputs = self.architecture(self.input_size,
self.vocab_size + 1)
if learning_rate is None:
learning_rate = CustomSchedule(d_model=self.vocab_size + 1,
initial_step=initial_step)
self.learning_schedule = True
else:
self.learning_schedule = False
optimizer = tf.keras.optimizers.RMSprop(learning_rate=learning_rate)
# create and compile
self.model = Model(inputs=inputs, outputs=outputs)
self.model.compile(
optimizer=optimizer,
loss=lambda y1, y2: tf.py_function(self.ctc_loss_lambda_func,
[y1, y2], [tf.float32]))
def fit(self,
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):
# remove ReduceLROnPlateau (if exist) when use schedule learning rate
if callbacks and self.learning_schedule:
callbacks = [
x for x in callbacks if not isinstance(x, ReduceLROnPlateau)
]
out = self.model.fit(x=x,
y=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,
steps_per_epoch=steps_per_epoch,
validation_steps=validation_steps,
validation_freq=validation_freq,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
**kwargs)
return out
def predict(self,
x,
batch_size=None,
verbose=0,
steps=1,
callbacks=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
ctc_decode=True):
if verbose == 1:
print("Model Predict")
out = self.model.predict(x=x,
batch_size=batch_size,
verbose=verbose,
steps=steps,
callbacks=callbacks,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing)
if not ctc_decode:
return np.log(out.clip(min=1e-8)), []
steps_done = 0
if verbose == 1:
print("CTC Decode")
progbar = tf.keras.utils.Progbar(target=steps)
batch_size = int(np.ceil(len(out) / steps))
input_length = len(max(out, key=len))
predicts, probabilities = [], []
while steps_done < steps:
index = steps_done * batch_size
until = index + batch_size
x_test = np.asarray(out[index:until])
x_test_len = np.asarray([input_length for _ in range(len(x_test))])
decode, log = self.ctc_decode(x_test,
x_test_len,
greedy=self.greedy,
beam_width=self.beam_width,
top_paths=self.top_paths)
if not self.greedy:
probabilities.extend([np.exp(x)[0] for x in log])
else:
probabilities.extend([np.exp(-x)[0] for x in log])
decode = [[[int(p) for p in x if p != -1] for x in y]
for y in decode]
predicts.extend(np.swapaxes(decode, 0, 1))
steps_done += 1
if verbose == 1:
progbar.update(steps_done)
return (predicts, probabilities)
def ctc_decode(self,
y_pred,
input_length,
greedy=True,
beam_width=100,
top_paths=1):
input_shape = y_pred.shape
num_samples, num_steps = input_shape[0], input_shape[1]
y_pred = tf.math.log(
tf.transpose(y_pred, perm=[1, 0, 2]) + K.epsilon())
input_length = tf.cast(input_length, tf.int32)
if greedy:
(decoded,
log_prob) = tf.nn.ctc_greedy_decoder(inputs=y_pred,
sequence_length=input_length)
else:
(decoded, log_prob) = tf.nn.ctc_beam_search_decoder(
inputs=y_pred,
sequence_length=input_length,
beam_width=beam_width,
top_paths=top_paths)
decoded_dense = []
for st in decoded:
# st = tf.sparse.SparseTensor(
# st.indices, st.values, (num_samples, num_steps))
decoded_dense.append(
tf.sparse.to_dense(sp_input=st, default_value=-1))
return (decoded_dense, log_prob)
@staticmethod
def ctc_loss_lambda_func(y_true, y_pred):
if len(y_true.shape) > 2:
y_true = tf.squeeze(y_true)
# y_pred.shape = (batch_size, string_length, alphabet_size_1_hot_encoded)
# output of every model is softmax
# so sum across alphabet_size_1_hot_encoded give 1
# string_length give string length
input_length = tf.math.reduce_sum(y_pred, axis=-1, keepdims=False)
input_length = tf.math.reduce_sum(input_length, axis=-1, keepdims=True)
# y_true strings are padded with 0
# so sum of non-zero gives number of characters in this string
label_length = tf.math.count_nonzero(y_true,
axis=-1,
keepdims=True,
dtype="int64")
loss = K.ctc_batch_cost(y_true, y_pred, input_length, label_length)
# average loss across all entries in the batch
loss = tf.reduce_mean(loss)
return loss
def architecture(self, input_size, d_model):
input_data = Input(name="input", shape=input_size)
cnn = Reshape((input_size[0] // 2, input_size[1] // 2,
input_size[2] * 4))(input_data)
cnn = Conv2D(filters=16,
kernel_size=(3, 3),
strides=(1, 2),
padding="same",
kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=16, kernel_size=(3, 3),
padding="same")(cnn)
cnn = Conv2D(filters=32,
kernel_size=(3, 3),
strides=(1, 1),
padding="same",
kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=32, kernel_size=(3, 3),
padding="same")(cnn)
cnn = Conv2D(filters=40,
kernel_size=(2, 4),
strides=(2, 4),
padding="same",
kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=40,
kernel_size=(3, 3),
padding="same",
kernel_constraint=MaxNorm(4, [0, 1, 2]))(cnn)
cnn = Dropout(rate=0.2)(cnn)
cnn = Conv2D(filters=48,
kernel_size=(3, 3),
strides=(1, 1),
padding="same",
kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=48,
kernel_size=(3, 3),
padding="same",
kernel_constraint=MaxNorm(4, [0, 1, 2]))(cnn)
cnn = Dropout(rate=0.2)(cnn)
cnn = Conv2D(filters=56,
kernel_size=(2, 4),
strides=(2, 4),
padding="same",
kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=56,
kernel_size=(3, 3),
padding="same",
kernel_constraint=MaxNorm(4, [0, 1, 2]))(cnn)
cnn = Dropout(rate=0.2)(cnn)
cnn = Conv2D(filters=64,
kernel_size=(3, 3),
strides=(1, 1),
padding="same",
kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
shape = cnn.get_shape()
bgru = Reshape((shape[1], shape[2] * shape[3]))(cnn)
bgru = Bidirectional(GRU(units=128, return_sequences=True,
dropout=0.5))(bgru)
bgru = Dense(units=256)(bgru)
bgru = Bidirectional(GRU(units=128, return_sequences=True,
dropout=0.5))(bgru)
bgru = Dense(units=256)(bgru)
bgru = Bidirectional(GRU(units=128, return_sequences=True,
dropout=0.5))(bgru)
output_data = Dense(units=d_model, activation="softmax")(bgru)
return (input_data, output_data)
cnns = []
for _ in range(adjs):
input_a = Input((*input_shape, 1))
x = Conv2D(8, 3)(input_a)
x = MaxPool2D(2)(x)
x = Flatten()(x)
x = Model(inputs=[input_a], outputs=x)
cnns.append(x)
combine = Concatenate()([x.output for x in cnns])
z = Dense(classes, activation='softmax')(combine)
model = Model(inputs=[x.input for x in cnns], outputs=z)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train,
y_train_cat,
batch_size=32,
epochs=50,
shuffle=True,
validation_split=0.1)
y_pred = model.predict(x_test).argmax(axis=-1)
acc = accuracy_score(y_test, y_pred)
print("Fold accuracy: {:2.2f}".format(acc))
matrix = confusion_matrix(y_test, y_pred)
print("Fold confusion matrix: \n {}".format(matrix))
total_acc += acc / n_splits
print("Total model accuracy: {:2.2f}".format(total_acc))
sub_Y = Y[start:end, :]
train_X, train_Y, val_X, val_Y = train_test_split(sub_X, sub_Y,
train_portion)
# 保存一次训练的训练过程
train_loss_list = []
train_mse_list = []
train_ic_list = []
val_loss_list = []
val_mse_list = []
val_ic_list = []
# 做一次训练
for epoch in range(EPOCHS):
model.fit(train_X, train_Y, epochs=1, shuffle=False, verbose=0)
# print("Epoch:" + str(epoch))
# 训练集loss和metrics
pred_train = model.predict(train_X)
train_loss = mean_absolute_error(pred_train, train_Y)
trian_mse = mean_squared_error(pred_train, train_Y)
train_loss_list.append(train_loss)
train_mse_list.append(trian_mse)
# print("train_loss:" + str(train_loss) + " train_mse:" + str(trian_mse),end=" ")
# 验证集loss和metrics
pred_val = model.predict(val_X)
val_loss = mean_absolute_error(pred_val, val_Y)
val_mse = mean_squared_error(pred_val, val_Y)
class UNetModel():
def __init__(self,
ckpt_name,
loss,
optimizer,
metrics,
monitor,
dropout_rate=0.25,
epochs=50,
batch_size=32,
input_size=101,
input_layer=None,
output_layer=None):
if input_layer is None:
self.input_layer = Input((input_size, input_size, 1))
else:
self.input_layer = input_layer
if output_layer is None:
self.output_layer = self.build_model(self.input_layer, 16,
dropout_rate)
else:
self.output_layer = output_layer
self.model = Model(self.input_layer, self.output_layer)
self.model.compile(loss=loss, optimizer=optimizer, metrics=metrics)
self.model_name = ckpt_name
self.model_checkpoint = ModelCheckpoint(self.model_name,
monitor=monitor,
mode='max',
save_best_only=True,
verbose=1)
self.reduce_lr = ReduceLROnPlateau(monitor=monitor,
mode='max',
factor=0.5,
patience=5,
min_lr=0.0001,
verbose=1)
self.epochs = epochs
self.batch_size = batch_size
print('''Model info:
model name: {}
loss: {}
optimizer: {}
monitor: {}
dropout rate: {}
epoch: {}
batch size: {}'''.format(ckpt_name, loss, optimizer, monitor,
dropout_rate, epochs, batch_size))
def fit(self, x_train, y_train, x_valid, y_valid):
self.history = self.model.fit(
x_train,
y_train,
validation_data=[x_valid, y_valid],
epochs=self.epochs,
batch_size=self.batch_size,
callbacks=[self.model_checkpoint, self.reduce_lr],
verbose=2)
return self.history
def batch_activate(self, x):
x = BatchNormalization()(x)
x = Activation('relu')(x)
return x
def convolution_block(self,
x,
filters,
size,
strides=(1, 1),
padding='same',
activation=True):
x = Conv2D(filters, size, strides=strides, padding=padding)(x)
if activation is True:
x = self.batch_activate(x)
return x
def residual_block(self, blockInput, num_filters=16, activation=False):
x = self.batch_activate(blockInput)
x = self.convolution_block(x, num_filters, (3, 3))
x = self.convolution_block(x, num_filters, (3, 3), activation=False)
x = Add()([x, blockInput])
if activation:
x = self.batch_activate(x)
return x
def squeeze_excite_block_cSE(self, input, ratio=2):
init = input
filters = K.int_shape(init)[-1]
se_shape = (1, 1, filters)
se = GlobalAveragePooling2D()(init)
se = Reshape(se_shape)(se)
se = Dense(filters // ratio,
activation='relu',
kernel_initializer='he_normal',
use_bias=True)(se)
se = Dense(filters,
activation='sigmoid',
kernel_initializer='he_normal',
use_bias=True)(se)
x = multiply([init, se])
return x
def squeeze_excite_block_sSE(sekf, input):
sSE_scale = Conv2D(1, (1, 1),
activation='sigmoid',
padding="same",
use_bias=True)(input)
return multiply([input, sSE_scale])
def unet_layer(self, blockInput, num_filters, use_csSE_ratio=2):
x = Conv2D(num_filters, (3, 3), activation=None,
padding="same")(blockInput)
x = self.residual_block(x, num_filters)
x = self.residual_block(x, num_filters, activation=True)
if use_csSE_ratio > 0:
sSEx = self.squeeze_excite_block_sSE(x)
cSEx = self.squeeze_excite_block_cSE(x, ratio=use_csSE_ratio)
x = Add()([sSEx, cSEx])
return x
def build_model(self,
input_layer,
start_neurons,
DropoutRatio=0.5,
use_csSE_ratio=2):
# 101 -> 50
conv1 = self.unet_layer(input_layer, start_neurons * 1, use_csSE_ratio)
pool1 = MaxPooling2D((2, 2))(conv1)
pool1 = Dropout(DropoutRatio / 2)(pool1)
# 50 -> 25
conv2 = self.unet_layer(pool1, start_neurons * 2, use_csSE_ratio)
pool2 = MaxPooling2D((2, 2))(conv2)
pool2 = Dropout(DropoutRatio)(pool2)
# 25 -> 12
conv3 = self.unet_layer(pool2, start_neurons * 4, use_csSE_ratio)
pool3 = MaxPooling2D((2, 2))(conv3)
pool3 = Dropout(DropoutRatio)(pool3)
# 12 -> 6
conv4 = self.unet_layer(pool3, start_neurons * 8, use_csSE_ratio)
pool4 = MaxPooling2D((2, 2))(conv4)
pool4 = Dropout(DropoutRatio)(pool4)
# Middle
convm = Conv2D(start_neurons * 16, (3, 3),
activation=None,
padding="same")(pool4)
convm = self.residual_block(convm, start_neurons * 16)
convm = self.residual_block(convm, start_neurons * 16, True)
# 6 -> 12
deconv4 = Conv2DTranspose(start_neurons * 8, (3, 3),
strides=(2, 2),
padding="same")(convm)
uconv4 = concatenate([deconv4, conv4])
uconv4 = Dropout(DropoutRatio)(uconv4)
uconv4 = Conv2D(start_neurons * 8, (3, 3),
activation=None,
padding="same")(uconv4)
uconv4 = self.residual_block(uconv4, start_neurons * 8)
uconv4 = self.residual_block(uconv4, start_neurons * 8, True)
# 12 -> 25
deconv3 = Conv2DTranspose(start_neurons * 4, (3, 3),
strides=(2, 2),
padding="valid")(uconv4)
uconv3 = concatenate([deconv3, conv3])
uconv3 = Dropout(DropoutRatio)(uconv3)
uconv3 = Conv2D(start_neurons * 4, (3, 3),
activation=None,
padding="same")(uconv3)
uconv3 = self.residual_block(uconv3, start_neurons * 4)
uconv3 = self.residual_block(uconv3, start_neurons * 4, True)
# 25 -> 50
deconv2 = Conv2DTranspose(start_neurons * 2, (3, 3),
strides=(2, 2),
padding="same")(uconv3)
uconv2 = concatenate([deconv2, conv2])
uconv2 = Dropout(DropoutRatio)(uconv2)
uconv2 = Conv2D(start_neurons * 2, (3, 3),
activation=None,
padding="same")(uconv2)
uconv2 = self.residual_block(uconv2, start_neurons * 2)
uconv2 = self.residual_block(uconv2, start_neurons * 2, True)
# 50 -> 101
deconv1 = Conv2DTranspose(start_neurons * 1, (3, 3),
strides=(2, 2),
padding="valid")(uconv2)
uconv1 = concatenate([deconv1, conv1])
uconv1 = Dropout(DropoutRatio)(uconv1)
uconv1 = Conv2D(start_neurons * 1, (3, 3),
activation=None,
padding="same")(uconv1)
uconv1 = self.residual_block(uconv1, start_neurons * 1)
uconv1 = self.residual_block(uconv1, start_neurons * 1, True)
output_layer_noActi = Conv2D(1, (1, 1),
padding="same",
activation=None)(uconv1)
output_layer = Activation('sigmoid')(output_layer_noActi)
return output_layer
# Flow training images in batches of 20 using train_datagen generator
train_generator = train_datagen.flow_from_directory(train_dir,
batch_size = 20,
class_mode = 'binary',
target_size = (150, 150))
# Flow validation images in batches of 20 using test_datagen generator
validation_generator = test_datagen.flow_from_directory( validation_dir,
batch_size = 20,
class_mode = 'binary',
target_size = (150, 150))
history = model.fit(
train_generator,
validation_data = validation_generator,
steps_per_epoch = 100,
epochs = 20,
validation_steps = 50,
verbose = 2)
import matplotlib.pyplot as plt
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'r', label='Training accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')
a5 = Dropout(0.2)(a5)
a5 = Flatten()(a5)
z6 = Dense(136)(a5)
# ----------------------------
model = Model(input_img, z6)
model.summary()
# Compile and train model
model.compile(optimizer=Adam(0.001), loss='mse')
train = model.fit(X, y, batch_size=64, epochs=45)
# ## **Plot loss**
def plot_history(history):
fig = plt.figure(figsize=(15, 8))
ax2 = fig.add_subplot(222)
ax2.set_title('model loss')
ax2.plot(history['loss'])
ax2.set_ylabel('loss')
ax2.set_xlabel('epoch')
ax2.legend(['train', 'test'], loc='upper right')
self.act = Dense(1, activation='sigmoid')
def call(self, inputs, training=None, mask=None):
x, mask = inputs
x = self.embedding(inputs)
x = self.attention([x, x, x, mask])
x = tf.reduce_mean(x, axis=1)
x = self.act(x)
return x
if __name__ == '__main__':
imdb = tf.keras.datasets.imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data()
x_train = pad_sequences(x_train, 200)
x = Input([200, ],dtype='int32')
mask = Input([200, ],dtype='int32')
out = TestModel([x, mask])
model = Model(([x, mask], out))
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=tf.keras.optimizers.Adam(1e-5),
metrics=['accuracy']
)
import numpy as np
m = np.ones([25000, 200])
model.fit([x_train, m], y_train, batch_size=32, epochs=1)
output_layer = unet_model(input_layer, 32)
#output_layer = improved_unet_model(input_layer, 32)
model = Model(input_layer, output_layer)
# 저장된 가중치 불러오기
#model.load_weights(checkpoint_path)
# ################## compile 메서드로 모형 완성 ###########################
adam = tf.keras.optimizers.Adam(learning_rate=0.0005,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-07,
amsgrad=True,
name='Adam')
model.compile(loss="mae",
optimizer=adam,
metrics=[maeOverFscore_keras, fscore_keras, "accuracy"])
# ################## fit 메서드로 트레이닝 #################################
#model_history = model.fit_sample()
model_history = model.fit(train_dataset,
epochs=10,
verbose=1,
shuffle=True,
callbacks=callbacks_list)
pred = model.predict(testGenerator())
submission = pd.read_csv('Submission_form.csv')
submission.iloc[:, 1:] = pred.reshape(-1, 1600)
submission.to_csv('Answer.csv', index=False)
with strategy.scope():
rfanet_x = RFDN()
x = Input(shape=(120, 160, 3))
out = rfanet_x(x)
parallel_model = Model(inputs=x, outputs=out)
parallel_model.compile(loss=loss_fn, optimizer=optimizer, metrics=metrics)
parallel_model.summary()
print('Ready for training!\n')
# Callbacks
callbacks = get_nyu_callbacks(parallel_model,
train_generator,
val_generator,
runPath,
totaL_epochs=args.epochs,
warmup_epoch=5,
batch_size=args.bs,
lr=args.lr,
val_loss="val_loss_sirmse_baseline")
# Start training
parallel_model.fit(train_generator,
validation_data=val_generator,
callbacks=callbacks,
epochs=args.epochs,
shuffle=True,
batch_size=args.bs,
verbose=1)
#compiling the model by using categorical_crossentropy loss
model.compile(optimizer=opti_flow, loss = 'categorical_crossentropy', metrics=['mae','accuracy'])
model.summary()
#visualizing the model on tensorboard
tensorboard = TensorBoard(log_dir="logs\{}".format(time()),write_graph=True)
#calling the datagenerator and passing the inputs to our model for training
i=0
hist_frames=[]
for x, y in datagen():
i=i+1
print(i)
if(i == 15000): break
history = model.fit(x,y, batch_size=64, epochs=1,callbacks=[tensorboard])
hist_frames.append(history.history)
#saving training history
print("\nhistory dict:",hist_frames)
#saving the model after training
model.save('C:\\Users\\Tanya Joon\\Documents\\MM 803 Image\\model.h5')
#saving the training loss in an numpy array
loss_array=[]
for i in hist_frames:
for j in i['loss']:
loss_array.append(j)
#saving the training accuracy in an numpy array
backbone = ResNet50(weights=None,
input_shape=(224, 224, 3),
pooling='avg',
include_top=False)
x = backbone(backbone.input)
x = Dense(64, activation='relu', kernel_initializer='he_uniform')(x)
x = Dropout(0.5)(x)
x = Dense(32, activation='relu', kernel_initializer='he_uniform')(x)
x = Dropout(0.5)(x)
output = Dense(1, activation='relu', kernel_initializer='he_uniform')(x)
model = Model(backbone.input, output)
model_checkpoint = ModelCheckpoint(str(
models_path.joinpath('{epoch:02d}-{val_n_mae:.2f}.h5')),
period=1)
lr_sche = LearningRateScheduler(lr_schedule)
model.compile(loss=loss,
optimizer=tf.keras.optimizers.Adam(0.0001,
decay=1e-3 /
STEP_SIZE_TRAIN),
metrics=[n_mae])
his = model.fit(train_dataset,
validation_data=val_dataset,
epochs=epochs,
callbacks=[model_checkpoint, wandbcb, lr_sche],
verbose=1)
# %% Save history to csv and images
history = his.history
save_history(history_path, history)
plot_history(history_path, history)
conv_out_03 = layers.Flatten()(dense_01)
drop_02 = layers.Dropout(0.5)(dense_01)
out_layer = layers.Dense(n_cls, activation='softmax')(drop_02)
# %% Trainable Model
org_model = Model(inputs=in_layer, outputs=out_layer)
org_model.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['acc'])
if not os.path.exists('./logs/features'):
org_model.fit(X_train,
to_categorical(y_train),
batch_size=128,
epochs=1000,
callbacks=[EarlyStopping(patience=50)],
validation_data=(X_test, to_categorical(y_test)))
# %% Model for Analysis
out_01_model = Model(inputs=in_layer, outputs=conv_out_01)
out_02_model = Model(inputs=in_layer, outputs=conv_out_02)
out_03_model = Model(inputs=in_layer, outputs=conv_out_03)
# %% Embeddings for training data
def _ext(X_data, y_data):
sorted_X = []
sorted_y = []
for i in range(n_cls):
class Transformer():
"""
Transformer Model.
References:
Bryan M. Li and FOR.ai
A Transformer Chatbot Tutorial with TensorFlow 2.0, 2019
Medium: https://medium.com/tensorflow/a-transformer-chatbot-tutorial-with-tensorflow-2-0-88bf59e66fe2
Tensorflow documentation
Transformer model for language understanding
URL: https://www.tensorflow.org/tutorials/text/transformer
Trung Tran
Create The Transformer With Tensorflow 2.0
Machine Talk: https://machinetalk.org/2019/04/29/create-the-transformer-with-tensorflow-2-0/
Github: https://github.com/ChunML/NLP/tree/master/machine_translation
Jupyter Notebook: https://colab.research.google.com/drive/1YhN8ZCZhrv18Hw0a_yIkuZ5tTh4EZDuG#scrollTo=ha0dNJogUPQN
"""
def __init__(self, tokenizer, num_layers, units, d_model, num_heads, dropout=0.0, stop_tolerance=20, reduce_tolerance=15):
self.tokenizer = tokenizer
self.num_layers = num_layers
self.units = units
self.d_model = d_model
self.num_heads = num_heads
self.dropout = dropout
self.stop_tolerance = stop_tolerance
self.reduce_tolerance = reduce_tolerance
self.model = None
self.encoder = None
self.decoder = None
def summary(self, output=None, target=None):
"""Show/Save model structure (summary)"""
self.model.summary()
if target is not None:
os.makedirs(output, exist_ok=True)
with open(os.path.join(output, target), "w") as f:
with redirect_stdout(f):
self.model.summary()
def load_checkpoint(self, target):
"""Restore model to construct transformer/encoder/decoder"""
if os.path.isfile(target):
if self.model is None:
self.compile()
self.model.load_weights(target)
def get_callbacks(self, logdir, checkpoint, monitor="val_loss", verbose=0):
"""Setup the list of callbacks for the model"""
callbacks = [
CSVLogger(
filename=os.path.join(logdir, "epochs.log"),
separator=";",
append=True),
TensorBoard(
log_dir=logdir,
histogram_freq=10,
profile_batch=0,
write_graph=True,
write_images=False,
update_freq="epoch"),
ModelCheckpoint(
filepath=checkpoint,
monitor=monitor,
save_best_only=True,
save_weights_only=True,
verbose=verbose),
EarlyStopping(
monitor=monitor,
min_delta=1e-8,
patience=self.stop_tolerance,
restore_best_weights=True,
verbose=verbose),
ReduceLROnPlateau(
monitor=monitor,
min_delta=1e-8,
factor=0.2,
patience=self.reduce_tolerance,
verbose=verbose)
]
return callbacks
def compile(self, learning_rate=None, initial_step=0):
"""Build models (train, encoder and decoder)"""
enc_input = Input(shape=(None,), name="enc_input")
dec_input = Input(shape=(None,), name="dec_input")
enc_padding_mask, look_ahead_mask, dec_padding_mask = create_masks(enc_input, dec_input)
self.encoder = Encoder(num_layers=self.num_layers,
d_model=self.d_model,
num_heads=self.num_heads,
dff=self.units,
input_vocab_size=self.tokenizer.vocab_size,
maximum_position_encoding=self.tokenizer.vocab_size,
rate=self.dropout)
self.decoder = Decoder(num_layers=self.num_layers,
d_model=self.d_model,
num_heads=self.num_heads,
dff=self.units,
target_vocab_size=self.tokenizer.vocab_size,
maximum_position_encoding=self.tokenizer.vocab_size,
rate=self.dropout)
enc_output = self.encoder(enc_input, enc_padding_mask)
dec_output, _ = self.decoder(dec_input, enc_output, look_ahead_mask, dec_padding_mask)
if learning_rate is None:
learning_rate = CustomSchedule(d_model=self.d_model, initial_step=initial_step)
self.learning_schedule = True
else:
self.learning_schedule = False
optimizer = Adam(learning_rate=learning_rate, beta_1=0.9, beta_2=0.98, epsilon=1e-9)
self.model = Model(inputs=[enc_input, dec_input], outputs=dec_output, name="transformer")
self.model.compile(optimizer=optimizer, loss=loss_func, metrics=["accuracy"])
def fit(self,
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):
"""
Model training on data yielded (fit function has support to generator).
A fit() abstration function of TensorFlow 2 using the model_train.
:param: See tensorflow.keras.Model.fit()
:return: A history object
"""
# remove ReduceLROnPlateau (if exist) when use schedule learning rate
if callbacks and self.learning_schedule:
callbacks = [x for x in callbacks if not isinstance(x, ReduceLROnPlateau)]
out = self.model.fit(x=x, y=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, steps_per_epoch=steps_per_epoch,
validation_steps=validation_steps, validation_freq=validation_freq,
max_queue_size=max_queue_size, workers=workers,
use_multiprocessing=use_multiprocessing, **kwargs)
return out
def predict(self,
x,
batch_size=None,
verbose=0,
steps=1,
callbacks=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False):
"""
Model predicting on data yielded (generator).
A predict() abstration function of TensorFlow 2 using the encoder and decoder models
:param: See tensorflow.keras.Model.predict()
:return: A numpy array(s) of predictions.
"""
try:
enqueuer = GeneratorEnqueuer(x, use_multiprocessing=use_multiprocessing)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
steps_done = 0
if verbose == 1:
print("Model Predict")
progbar = Progbar(target=steps)
predicts = []
while steps_done < steps:
x = next(output_generator)[0]
for sentence in x:
enc_input = tf.expand_dims(sentence, axis=0)
dec_input = tf.expand_dims([self.tokenizer.SOS], axis=0)
for _ in range(self.tokenizer.maxlen):
enc_padding_mask, look_ahead_mask, dec_padding_mask = create_masks(enc_input, dec_input)
enc_output = self.encoder(enc_input, enc_padding_mask) # (batch_size, inp_seq_len, d_model)
dec_output, _ = self.decoder(dec_input, enc_output, look_ahead_mask, dec_padding_mask)
# select the last word from the seq_len dimension
predictions = dec_output[:, -1:, :] # (batch_size, 1, vocab_size)
predicted_id = tf.cast(tf.argmax(predictions, axis=-1), dtype=tf.int32)
# return the result if the predicted_id is equal to the end token
if tf.equal(predicted_id, self.tokenizer.EOS):
break
# concatentate the predicted_id to the output which is given to the decoder as its input.
dec_input = tf.concat([dec_input, predicted_id], axis=-1)
dec_input = tf.squeeze(dec_input, axis=0)
dec_input = self.tokenizer.decode(dec_input)
predicts.append(self.tokenizer.remove_tokens(dec_input))
steps_done += 1
if verbose == 1:
progbar.update(steps_done)
finally:
enqueuer.stop()
return predicts
def model_cat(images, categories):
imgs = []
labels = []
for i in range(int(len(categories))):
if (images[i].shape == (128, 128, 3)):
try:
labels.append((categories[i][0]))
imgs.append(images[i])
except:
print(categories[i])
imgs_train = np.asarray(imgs[:int(len(imgs) * 0.98)])
imgs_validate = np.asarray(imgs[int(len(imgs) * 0.98):])
labels_train = np.asarray(labels[:int(len(labels) * 0.98)])
labels_validate = np.asarray(labels[int(len(labels) * 0.98):])
input = Input(shape=(imgs_train.shape[1], imgs_train.shape[2], 3))
# convolution layers （VGG structure)
conv_layer1 = Conv2D(filters=32, kernel_size=3, activation='relu')
conv_output1 = conv_layer1(input)
conv_layer2 = Conv2D(filters=32, kernel_size=3, activation='relu')
conv_output2 = conv_layer2(conv_output1)
pool_layer1 = MaxPool2D(pool_size=(2, 2))
pool_output1 = pool_layer1(conv_output2)
conv_layer3 = Conv2D(filters=64, kernel_size=3, activation='relu')
conv_output3 = conv_layer3(pool_output1)
conv_layer4 = Conv2D(filters=64, kernel_size=3, activation='relu')
conv_output4 = conv_layer4(conv_output3)
pool_layer2 = MaxPool2D(pool_size=(2, 2))
pool_output2 = pool_layer2(conv_output4)
drop_out2 = Dropout(0.2)(pool_output2)
Flatten_output = Flatten()(drop_out2)
dense_sigmoid = Dense(16, activation='sigmoid')(Flatten_output)
output = Dense(labels_train.shape[1], activation='softmax')(dense_sigmoid)
Model_cat = Model(input, output)
# optimizer
optimizer = optimizers.Adam(lr=0.00002)
Model_cat.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['mse', 'accuracy'])
history = Model_cat.fit(imgs_train,
labels_train,
epochs=50,
batch_size=256,
validation_data=[imgs_validate, labels_validate])
Model_cat.save("Model_cat.hdf5")
cat_precict = Model_cat.predict(imgs_validate)
num_true_predict = 0
for i in range(labels_validate.shape[0]):
for j in range(labels_validate.shape[1]):
labels_validate[i][j] = np.argmax(labels_validate[i][j])
if np.argmax(cat_precict[i]) in labels_validate[i]:
num_true_predict += 1
print(np.argmax(cat_precict[i]))
print("test accuracy for categories is:",
num_true_predict / labels_validate.shape[0])
return Model_cat, history.history
dropout_1 = Dropout(0.3, seed=42)(fc_1)
fc_2 = Dense(32, activation='relu')(dropout_1)
output = Dense(n_out, activation='softmax')(fc_2)
# Build model
model = Model(inputs=[X_in, A_in], outputs=output)
optimizer = Adam(lr=learning_rate)
model.compile(optimizer=optimizer,
loss='sparse_categorical_crossentropy',
metrics=['acc'])
# Train model
validation_data = (test_X, test_y)
model.fit(train_X,
train_y,
batch_size=16,
validation_data=validation_data,
epochs=10,
verbose=0)
y_pred = model.predict(X_test, verbose=1)
y_p = []
for row in y_pred:
y_p.append(np.argmax(row))
target_names = ['0', '1', '2']
print("Fold: ", fold)
fold += 1
# print(classification_report(y_test, y_p, target_names=target_names))
f1_weighted_per_fold.append(
f1_score(y_test, y_p, average='weighted'))
f1_macro_per_fold.append(f1_score(y_test, y_p, average='macro'))
f1_micro_per_fold.append(f1_score(y_test, y_p, average='micro'))
def model_caption(images, captions_word2vec, captions_onehot, word2vec):
decoder_input_raw = []
decoder_output_raw = []
encoder_raw = []
encoder_test = np.asarray(images[:int(0.1 * len(images))])
decoder_input_test = np.asarray(
captions_word2vec[:int(0.1 * len(captions_word2vec))])
decoder_output_test = np.asarray(
captions_onehot[:int(0.1 * len(captions_onehot))])
encoder_test = encoder_test.reshape(encoder_test.shape[0], 1,
encoder_test.shape[1],
encoder_test.shape[2], 3)
for i in range(int(len(captions_word2vec))):
if (images[i].shape == (128, 128, 3)):
decoder_input_raw += [captions_word2vec[i]]
decoder_output_raw += [captions_onehot[i]]
encoder_raw += [images[i]]
max = 0
for i in range(len(captions_onehot)):
if (max < len(captions_onehot[i])):
max = len(captions_word2vec[i])
encoder_train_input = np.asarray(encoder_raw[:int(0.98 *
len(encoder_raw))])
encoder_validate_input = np.asarray(encoder_raw[int(0.98 *
len(encoder_raw)):])
decoder_input_raw = np.asarray(decoder_input_raw)
decoder_output_raw = np.asarray(decoder_output_raw)
decoder_input_raw = preprocessing.sequence.pad_sequences(decoder_input_raw,
maxlen=None,
dtype='float',
padding='post',
truncating='post',
value=0)
decoder_output_raw = preprocessing.sequence.pad_sequences(
decoder_output_raw,
maxlen=None,
dtype='float',
padding='post',
truncating='post',
value=0)
decoder_input_raw = preprocessing.sequence.pad_sequences(
decoder_input_raw,
maxlen=decoder_input_raw.shape[1] + 1,
dtype='float',
padding='pre',
truncating='pre',
value=0)
decoder_output_raw = preprocessing.sequence.pad_sequences(
decoder_output_raw,
maxlen=decoder_output_raw.shape[1] + 1,
dtype='float',
padding='post',
truncating='post',
value=0)
decoder_train_input = decoder_input_raw[:int(
len(decoder_input_raw) * 0.98)]
decoder_train_output = decoder_output_raw[:int(
len(decoder_output_raw) * 0.98)]
decoder_validate_input = decoder_input_raw[
int(len(decoder_input_raw) * 0.98):]
decoder_validate_output = decoder_output_raw[
int(len(decoder_output_raw) * 0.98):]
encoder_input = Input(shape=(encoder_train_input.shape[1],
encoder_train_input.shape[2], 3))
# convolution layers （VGG structure)
conv_layer1 = Conv2D(filters=32, kernel_size=3, activation='relu')
conv_output1 = conv_layer1(encoder_input)
conv_layer2 = Conv2D(filters=32, kernel_size=3, activation='relu')
conv_output2 = conv_layer2(conv_output1)
pool_layer1 = MaxPool2D(pool_size=(2, 2))
pool_output1 = pool_layer1(conv_output2)
conv_layer3 = Conv2D(filters=64, kernel_size=3, activation='relu')
conv_output3 = conv_layer3(pool_output1)
conv_layer4 = Conv2D(filters=64, kernel_size=3, activation='relu')
conv_output4 = conv_layer4(conv_output3)
pool_layer2 = MaxPool2D(pool_size=(2, 2))
pool_output2 = pool_layer2(conv_output4)
drop_out2 = Dropout(0.2)(pool_output2)
# flatten layer
Flatten_layer = Flatten()
Flatten_output = Flatten_layer(drop_out2)
Full_connect_h = Dense(128, activation='relu')
Full_connect_h_output = Full_connect_h(Flatten_output)
Full_connect_c = Dense(128, activation='relu')
Full_connect_c_output = Full_connect_c(Flatten_output)
Full_connect_attention = Dense(512, activation="relu")
Full_connect_attention_output = Full_connect_attention(Flatten_output)
# decoder
decoder_input = Input(shape=(None, decoder_train_input.shape[2]))
num_hidden_lstm = int(Full_connect_h_output.shape[1])
decoder_lstm = LSTM(num_hidden_lstm,
return_state=True,
return_sequences=True)
decoder_out, decoder_h, decoder_c = decoder_lstm(
decoder_input,
initial_state=[Full_connect_h_output, Full_connect_c_output])
# ensure a same dimmension for dot product in attention layer
decoder_dense1 = Dense(Full_connect_attention_output.shape[1],
activation='relu')
decoder_dense1_output = decoder_dense1(decoder_out)
# Attention layer
attention = Attention()
attention_distribute = attention(
[decoder_dense1_output, Full_connect_attention_output])
# attention output into softmax for output
decoder_dense2 = Dense(decoder_train_output.shape[2], activation='softmax')
decoder_output = decoder_dense2(attention_distribute)
Model_train = Model([encoder_input, decoder_input], decoder_output)
# optimizer
optimizer = optimizers.SGD(lr=0.000002,
momentum=0.9) # , nesterov=True)# decay=1e-6)
Model_train.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['mse', 'accuracy'])
Model_train.load_weights("Model_train.h5")
history = Model_train.fit(
[encoder_train_input, decoder_train_input],
decoder_train_output,
epochs=5000,
batch_size=256,
validation_data=[[encoder_validate_input, decoder_validate_input],
decoder_validate_output])
Model_train.save("Model_train.hdf5")
# encoder model
Model_encoder = Model(encoder_input, [
Full_connect_h_output, Full_connect_c_output,
Full_connect_attention_output
])
Model_encoder.save("Model_encoder.hdf5")
# decoder model
h_decoder_input = Input(shape=(num_hidden_lstm, ))
c_decoder_input = Input(shape=(num_hidden_lstm, ))
attention_decoder_input = Input(
shape=(Full_connect_attention_output.shape[1], ))
decoder_out, decoder_h_output, decoder_c_output = decoder_lstm(
decoder_input, initial_state=[h_decoder_input, c_decoder_input])
decoder_dense_out1 = decoder_dense1(decoder_out)
attention_distribute = attention(
[attention_decoder_input, decoder_dense_out1])
decoder_output = decoder_dense2(attention_distribute)
Model_decoder = Model([
decoder_input, h_decoder_input, c_decoder_input,
attention_decoder_input
], [decoder_output, decoder_h_output, decoder_c_output])
Model_decoder.save("Model_decoder.hdf5")
# prediction
# BLUE score list
BLUE_score_list = []
# maximum words in a sentence
max_word = 20
for i in range(len(encoder_test)):
outputs_list = []
outputs = ""
for k in range(len(decoder_input_test[i])):
if (sum(decoder_output_test[i][k]) != 0):
output = np.argmax(decoder_output_test[i][k])
output = wordforid[output]
outputs = outputs + output + ' '
outputs_list.append(output)
print("actually reference:", outputs)
[h, c, attention_] = Model_encoder.predict(encoder_test[i])
# a blank image for normalisation
blank_reference = np.ones((1, width, height, 3)) * 128
[h_reference, c_reference,
attention_reference] = Model_encoder.predict(blank_reference)
current_decoder_input = np.zeros([1, 1, len(decoder_input_test[0][0])])
current_decoder_input_reference = np.zeros(
[1, 1, len(decoder_input_test[0][0])])
outputs_hat = ""
outputs_hat_list = []
outputs_hatt = ""
outputs_hatt_list = []
# for BLUE score counting
BLUE = []
BLUE_element = {}
for j in range(max_word):
[current_decoder_output, h, c] = Model_decoder.predict(
[current_decoder_input, h, c, attention_])
[current_decoder_output_reference, h_reference,
c_reference] = Model_decoder.predict([
current_decoder_input_reference, h_reference, c_reference,
attention_reference
])
# normalisation of decoder output
current_decoder_out = (current_decoder_output -
current_decoder_output_reference) / (
current_decoder_output**0.75)
output_hat = np.argmax(current_decoder_out)
output_hat = wordforid[output_hat]
output_hatt = np.argmax(current_decoder_output)
current_decoder_input = word2vec[output_hatt]
current_decoder_input = current_decoder_input.reshape(1, 1, 300)
output_hatt = wordforid[output_hatt]
output_hattt = np.argmax(current_decoder_output_reference)
current_decoder_input_reference = word2vec[output_hattt]
current_decoder_input_reference = current_decoder_input_reference.reshape(
1, 1, 300)
count_current_word = outputs_list.count(output_hat)
# We use the advanced BLUE score, if number of the word in output is bigger than reference, use the
# maximum word number in reference
if (count_current_word > 0):
try:
if (BLUE_element[output_hat] < count_current_word):
BLUE_element[output_hat] += 1
BLUE.append(1)
except:
BLUE_element[output_hat] = 1
BLUE.append(1)
else:
BLUE.append(0)
outputs_hat = outputs_hat + output_hat + ' '
outputs_hat_list.append(output_hat)
outputs_hatt = outputs_hatt + output_hatt + ' '
outputs_hatt_list.append(output_hatt)
print("normal prediction:", outputs_hat)
print("prediction:", outputs_hatt)
if (len(BLUE) > 0):
BLUE_score = sum(BLUE) / len(BLUE)
BLUE_score_list.append(BLUE_score)
print("BLUE score is", BLUE_score)
print('\n')
overall_BLUE_Score = sum(BLUE_score_list) / len(BLUE_score_list)
print("overall BLUE score is:", overall_BLUE_Score)
return Model_encoder, Model_decoder, history.history, overall_BLUE_Score
class ClassificationPipe(PipelineBase):
"""Cral pipeline for classification task."""
def __init__(self, *args, **kwargs):
super(ClassificationPipe, self).__init__(task_type='classification',
*args,
**kwargs)
def add_data(self, *args, **kwargs):
"""Parses dataset once for generating metadata and versions the data.
Args:
*args: Description
**kwargs: Description
Deleted Parameters:
train_images_dir (str): path to images
val_images_dir (str, optional): path to validation images
split (float, optional): float to divide training dataset into
training and validation
"""
self.dataset_hash, self.dataset_csv_path, self.dataset_json = classification_dataset_hasher( # noqa: E501
tempfile.gettempdir(), *args, **kwargs)
# try_mlflow_log(log_artifact, local_path=dataset_csv_path)
# try_mlflow_log(log_artifact, local_path=dataset_json)
with open(self.dataset_json) as f:
self.data_dict = json.loads(f.read())
self.update_project_file(self.data_dict)
def set_aug(self, aug):
"""Sets the augmentation pipeline.
Args:
aug (TYPE): An albumentations data-augmentation pipeline
"""
# Do a check on data
self.aug_pipeline = AugmentorClassification(aug)
# update_json(self)
def visualize_data(self, image_url, allow_aug=False):
"""Summary.
Args:
image_url (TYPE): Description
allow_aug (bool, optional): Description
Raises:
ValueError: Description
"""
# tfrecord_dir = self.cral_meta_data['tfrecord_path']
# train_tfrecords = list(
# glob.glob(os.path.join(tfrecord_dir, 'train*.tfrecord')))
# test_tfrecords = list(
# glob.glob(os.path.join(tfrecord_dir, 'test*.tfrecord')))
if allow_aug is True and self.aug_pipeline is None:
raise ValueError('No augmentation pipeline has been provided')
def lock_data(self, gen_stats=False):
"""Parse Data and makes tf-records and creates meta-data.
Args:
gen_stats (bool, optional): If True uses tfdv to create stats graph
"""
meta_info = classification_tfrecord_creator(
meta_json=os.path.join(tempfile.gettempdir(), 'dataset.json'),
dataset_csv=os.path.join(tempfile.gettempdir(), 'dataset.csv'),
out_path=tempfile.gettempdir())
self.update_project_file(meta_info)
# generate cavets overview html graphs with tfdv and log
# disabling due to version problem
if gen_stats:
from cral.data_feeder.utils import generate_stats
generate_stats(os.path.join(tempfile.gettempdir(), 'statistics'))
def set_algo(self,
feature_extractor,
config,
weights='imagenet',
base_trainable=False,
preprocessing_fn=None,
optimizer=tf.keras.optimizers.Adam(lr=1e-4, clipnorm=0.001),
distribute_strategy=None):
"""Set model for training and prediction.
Args:
feature_extractor (str,model): Name of base model
config: Is an instance of MLPConfig for the head of the network
weights: one of `None` (random initialization),'imagenet'
(pre-training on ImageNet),or the path to the weights file
to be loaded
base_trainable (bool, optional): If set False the base models
layers will not be trainable useful fortransfer learning
preprocessing_fn (func, optional): needs to to be set if a in built
model is not being used
Raises:
ValueError: If network name assigned to `feature_extractor` is not
yet supported.
"""
classification_algo_meta = dict(feature_extractor_from_cral=False,
classification_meta=None)
# if config is not None:
assert isinstance(
config, MLPConfig
), f'config has to be an object of MLPConfig but got{type(config)}'
height = config.height
width = config.width
fully_connected_layer = config.fully_connected_layer
dropout_rate = config.dropout_rate
hidden_layer_activation = config.hidden_layer_activation
final_layer_activation = config.final_layer_activation
assert isinstance(feature_extractor,
str), 'expected a string got {} instead'.format(
type(feature_extractor))
feature_extractor = feature_extractor.lower()
if feature_extractor not in classification_networks.keys():
raise ValueError('feature extractor has to be one of {}'.format(
list(classification_networks.keys())))
if weights in ('imagenet', None):
backbone, self.preprocessing_fn = classification_networks[
feature_extractor](weights=weights,
input_shape=(height, width, 3))
elif tf.saved_model.contains_saved_model(weights):
backbone, self.preprocessing_fn = classification_networks[
feature_extractor](weights=None,
input_shape=(height, width, 3))
else:
assert False, 'Weights file is not supported'
if preprocessing_fn is not None:
# assert preprocessing function once
self.preprocessing_fn = preprocessing_fn
# freeze/train backbone
backbone.trainable = base_trainable
num_classes = self.cral_meta_data['num_classes']
final_hidden_layer = densely_connected_head(
feature_extractor_model=backbone,
fully_connected_layer=fully_connected_layer,
dropout_rate=dropout_rate,
hidden_layer_Activation=hidden_layer_activation)
output = Dense(units=num_classes,
activation=final_layer_activation)(final_hidden_layer)
# Assign resize dimensions
resize_height = tf.constant(height, dtype=tf.int64)
resize_width = tf.constant(width, dtype=tf.int64)
@tf.function(
input_signature=[tf.TensorSpec([None, None, 3], dtype=tf.uint8)])
def _preprocess(image_array):
"""tf.function-deocrated version of preprocess_"""
im_arr = tf.image.resize(image_array,
(resize_height, resize_width))
im_arr = self.preprocessing_fn(im_arr)
input_batch = tf.expand_dims(im_arr, axis=0)
return input_batch
self.model = Model(inputs=backbone.inputs,
outputs=output,
name='{}_custom'.format(feature_extractor))
if tf.saved_model.contains_saved_model(weights):
self.model.load_weights(
os.path.join(weights, 'variables', 'variables'))
# Attach function to Model
self.model.preprocess = _preprocess
# Attach resize dimensions to Model
self.model.resize_height = resize_height
self.model.resize_width = resize_width
# Model parallelism
if distribute_strategy is None:
self.model.compile(optimizer=optimizer,
loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=['accuracy'])
else:
with distribute_strategy.scope():
self.model.compile(
optimizer=optimizer,
loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=['accuracy'])
classification_algo_meta['feature_extractor_from_cral'] = True
classification_meta = dict(feature_extractor=feature_extractor,
architecture='MLP',
weights=weights,
base_trainable=base_trainable,
config=jsonpickle.encode(config))
classification_algo_meta['classification_meta'] = classification_meta
self.height = height
self.width = width
# log_param('feature_extractor', feature_extractor)
self.update_project_file(classification_algo_meta)
self.update_project_file(classification_algo_meta)
def train(self,
num_epochs,
snapshot_prefix,
snapshot_path,
snapshot_every_n,
batch_size=2,
validation_batch_size=None,
validate_every_n=1,
callbacks=[],
steps_per_epoch=None,
compile_options=None,
log_evry_n_step=100):
"""This function starts the training loop, with metric logging enabled.
Args:
num_epochs (int): number of epochs to run training on
snapshot_prefix (str): prefix to assign to the checkpoint file
snapshot_path (str): a valid folder path where the checkpoints are
to be stored
snapshot_every_n (int): take a snapshot at every nth epoch
batch_size (int, optional): batch size
validation_batch_size (None, optional): the batch size for
validation loop, if None(default) then equal to
`batch_size` argument
validate_every_n (int, optional): Run validation every nth epoch
callbacks (list, optional): list of keras callbacks to be passed to
model.fit() method
steps_per_epoch (None, optional): steps size of each epoch
compile_options (None, optional): A dictionary to be passed to
model.compile method
Raises:
ValueError: If model is not defined
"""
assert isinstance(num_epochs,
int), 'num epochs to run should be in `int`'
assert os.path.isdir(snapshot_path), '{} doesnot exist'.format(
snapshot_path)
assert isinstance(callbacks, list)
assert isinstance(validate_every_n, int)
snapshot_prefix = str(snapshot_prefix)
if validation_batch_size is None:
validation_batch_size = batch_size
# self.height = height
# self.width = width
num_classes = int(self.cral_meta_data['num_classes'])
training_set_size = int(self.cral_meta_data['num_training_images'])
test_set_size = int(self.cral_meta_data['num_test_images'])
if self.model is None:
raise ValueError(
'please define a model first using set_algo() function')
if compile_options is not None:
assert isinstance(compile_options, dict)
self.model.compile(**compile_options)
meta_info = dict(height=self.height,
width=self.width,
num_epochs=num_epochs,
batch_size=batch_size)
self.update_project_file(meta_info)
tfrecord_dir = self.cral_meta_data['tfrecord_path']
train_tfrecords = os.path.join(tfrecord_dir, 'train*.tfrecord')
test_tfrecords = os.path.join(tfrecord_dir, 'test*.tfrecord')
train_input_function = classification_tfrecord_parser(
filenames=train_tfrecords,
height=self.height,
width=self.width,
num_classes=num_classes,
processing_func=self.preprocessing_fn,
augmentation=self.aug_pipeline,
batch_size=batch_size)
if test_set_size > 0:
test_input_function = classification_tfrecord_parser(
filenames=test_tfrecords,
height=self.height,
width=self.width,
num_classes=num_classes,
processing_func=self.preprocessing_fn,
augmentation=self.aug_pipeline,
batch_size=validation_batch_size,
num_repeat=-1)
validation_steps = test_set_size / validation_batch_size
else:
test_input_function = None
validation_steps = None
if steps_per_epoch is None:
steps_per_epoch = training_set_size / batch_size
# callbacks.append(KerasCallback(log_evry_n_step))
# callbacks.append(KerasCallback())
# callbacks.append(
# checkpoint_callback(
# snapshot_every_epoch=snapshot_every_n,
# snapshot_path=snapshot_path,
# checkpoint_prefix=snapshot_prefix,
# save_h5=False))
# Attach segmind.cral as an asset
tf.io.gfile.copy(self.cral_file, 'segmind.cral', overwrite=True)
cral_asset_file = tf.saved_model.Asset('segmind.cral')
self.model.cral_file = cral_asset_file
# pred_model = tf.keras.models.load_model('saved_model')
# location_to_cral_file = pred_model.cral_file.asset_path.numpy()
# log_param('training_steps_per_epoch', int(steps_per_epoch))
# if test_set_size > 0:
# log_param('val_steps_per_epoch', int(validation_steps))
# log_gpu_params()
# Train & test
self.model.fit(x=train_input_function,
epochs=num_epochs,
callbacks=callbacks,
steps_per_epoch=steps_per_epoch,
validation_data=test_input_function,
validation_steps=validation_steps,
validation_freq=validate_every_n)
final_model_path = os.path.join(snapshot_path,
str(snapshot_prefix) + '_final')
self.model.save(filepath=final_model_path, overwrite=True)
print('Saved the final Model to :\n {}'.format(final_model_path))
def prediction_model(self, checkpoint_file):
self.model = keras.models.load_model(checkpoint_file, compile=False)
try:
location_to_cral_file = self.model.cral_file.asset_path.numpy()
with open(location_to_cral_file) as f:
metainfo = json.loads(f.read())
except AttributeError:
print(
"Couldn't locate any cral config file, probably this model was not trained using cral, or may be corrupted" # noqa: E501
)
for k, v in metainfo.items():
print(k, v)
# architecture = metainfo['classification_meta']['architecture']
# num_classes = int(metainfo['num_classes'])
feature_extractor = metainfo['classification_meta'][
'feature_extractor']
size = (metainfo['height'], metainfo['width'])
_, preprocessing_fn = classification_networks[feature_extractor](
weights=None)
pred_object = ClassificationPredictor(
model=self.model, preprocessing_func=preprocessing_fn, size=size)
return pred_object.predict
#encoded representation:
encoded = layers.Dense(num_neurons,
activation="relu",
kernel_regularizer=regulariser)(stock_in)
#decoded representation:
decoded = layers.Dense(features,
activation="linear",
kernel_regularizer=regulariser)(encoded)
autoencoder = Model(stock_in, decoded)
encoder = Model(stock_in, encoded)
encoded_input = Input(shape=(num_neurons, ))
decoder_layer = autoencoder.layers[-1]
decoder = Model(encoded_input, decoder_layer(encoded_input))
autoencoder.compile(optimizer='sgd', loss='mean_squared_error')
'''
autoencoder.fit(X_train,X_train, epochs = 500, verbose = 1)
encoded_imgs = encoder.predict(X_test)
decoded_imgs = decoder.predict(encoded_imgs)
encoded_Train = encoder.predict(X_train)
encoded_Test = encoder.predict(X_test)
'''
#==============================================================================
#==============================================================================
'''auto-encoding the folds:'''
Epochs = 100
#fold 1:
autoencoder.fit(fold1_train, fold1_train, epochs=Epochs, verbose=1)
mode='min',
verbose=1)
mc = ModelCheckpoint(fnModel,
monitor='val_accuracy',
verbose=1,
save_best_only=True,
mode='max')
t0 = time.time()
h = m.fit(
X_train,
y_train,
batch_size=500, #1000, # 1000
epochs=200,
callbacks=[mc],
#validation_split= 0.1
validation_data=(X_val, y_val))
tf.keras.backend.clear_session()
fnModel = 'ryModel_3.hdf5'
#se normalizan los datos
X_train = X_train.reshape(-1, nTime, nFreq, 1).astype('float32')
X_val = X_val.reshape(-1, nTime, nFreq, 1).astype('float32')
X_test = X_test.reshape(-1, nTime, nFreq, 1).astype('float32')
#X_testREAL= X_testREAL.reshape( -1, nTime, nFreq, 1).astype('float32')
from tensorflow.keras.layers import Input, Dense, Dropout
from tensorflow.keras import Model
from tensorflow.keras.losses import SparseCategoricalCrossentropy
pixels = 28 * 28
hidden_nodes = 64
dropout = 0.3
(xtr, ytr), (xte, yte) = tf.keras.datasets.mnist.load_data()
xtr = xtr.reshape((60000, pixels)).astype(np.float32) / 255.0
xte = xte.reshape((10000, pixels)).astype(np.float32) / 255.0
inputs = Input(shape=(pixels, ), name='images')
z = Dense(hidden_nodes, activation='relu', name='hidden1')(inputs)
z = Dropout(dropout)(z)
z = Dense(10, activation='softmax')(z)
our_model = Model(inputs=inputs, outputs=z)
our_model.summary()
our_model.compile(optimizer='adam',
loss=SparseCategoricalCrossentropy(),
metrics=['accuracy'])
results = our_model.fit(xtr,
ytr,
batch_size=32,
epochs=10,
validation_split=0.2)
our_model.save('hw01_model.hdf5')
