#
# Copyright 2018 Analytics Zoo Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import zoo.pipeline.api.autograd as autograd
from zoo.feature.image import ImageSet
from zoo.feature.text import TextSet
from zoo.feature.common import FeatureSet
from zoo.pipeline.api.keras.base import ZooKerasLayer
from zoo.pipeline.api.keras.utils import *
from bigdl.nn.layer import Layer
from zoo.common.utils import callZooFunc
if sys.version >= '3':
long = int
unicode = str
[docs]class KerasNet(ZooKerasLayer):
[docs] def save(self, path, over_write=False):
raise Exception("This is a deprecated method. Please use saveModel instead.")
[docs] def saveModel(self, modelPath, weightPath=None, over_write=False):
"""
Save this module to path with protobuf format.
:param modelPath: The path to save module, local file system,
HDFS and Amazon S3 is supported.
HDFS path should be like "hdfs://[host]:[port]/xxx"
Amazon S3 path should be like "s3a://bucket/xxx"
:param weightPath: The Path for the parameters
:param over_write: override the existing model on modelPath or not.
"""
super(KerasNet, self).saveModel(modelPath=modelPath,
weightPath=weightPath,
over_write=over_write)
[docs] def compile(self, optimizer, loss, metrics=None):
"""
Configure the learning process. It MUST be called before fit or evaluate.
# Arguments
optimizer: Optimization method to be used. One can alternatively pass in the corresponding
string representation, such as 'sgd'.
loss: Criterion to be used. One can alternatively pass in the corresponding string
representation, such as 'mse'.
metrics: List of validation methods to be used. Default is None if no validation is needed.
For convenience, string representations are supported: 'accuracy' (or 'acc'),
'top5accuracy' (or 'top5acc'), 'mae', 'auc', 'treennaccuracy' and 'loss'.
For example, you can either use [Accuracy()] or ['accuracy'].
"""
if isinstance(optimizer, six.string_types):
optimizer = to_bigdl_optim_method(optimizer)
criterion = loss
if isinstance(loss, six.string_types):
criterion = to_bigdl_criterion(loss)
if callable(loss):
from zoo.pipeline.api.autograd import CustomLoss
criterion = CustomLoss(loss, self.get_output_shape()[1:])
if metrics and all(isinstance(metric, six.string_types) for metric in metrics):
metrics = to_bigdl_metrics(metrics, loss)
callZooFunc(self.bigdl_type, "zooCompile",
self.value,
optimizer,
criterion,
metrics)
[docs] def set_tensorboard(self, log_dir, app_name):
"""
Set summary information during the training process for visualization purposes.
Saved summary can be viewed via TensorBoard.
In order to take effect, it needs to be called before fit.
Training summary will be saved to 'log_dir/app_name/train'
and validation summary (if any) will be saved to 'log_dir/app_name/validation'.
# Arguments
log_dir: The base directory path to store training and validation logs.
app_name: The name of the application.
"""
callZooFunc(self.bigdl_type, "zooSetTensorBoard",
self.value,
log_dir,
app_name)
[docs] def get_train_summary(self, tag=None):
"""
Get the scalar from model train summary
Return 2-D array like object which could be converted
by nd.array()
# Arguments
tag: The string variable represents the scalar wanted
"""
# exception handle
if tag != "Loss" and tag != "LearningRate" and tag != "Throughput":
raise TypeError('Only "Loss", "LearningRate", "Throughput"'
+ 'are supported in train summary')
return callZooFunc(self.bigdl_type, "zooGetScalarFromSummary",
self.value, tag, "Train")
[docs] def get_validation_summary(self, tag=None):
"""
Get the scalar from model validation summary
Return 2-D array like object which could be converted
by np.array()
Note: The metric and tag may not be consistent
Please look up following form to pass tag parameter
Left side is your metric during compile
Right side is the tag you should pass
'Accuracy' | 'Top1Accuracy'
'BinaryAccuracy' | 'Top1Accuracy'
'CategoricalAccuracy' | 'Top1Accuracy'
'SparseCategoricalAccuracy' | 'Top1Accuracy'
'AUC' | 'AucScore'
'HitRatio' | 'HitRate@k' (k is Top-k)
'Loss' | 'Loss'
'MAE' | 'MAE'
'NDCG' | 'NDCG'
'TFValidationMethod' | '${name + " " + valMethod.toString()}'
'Top5Accuracy' | 'Top5Accuracy'
'TreeNNAccuracy' | 'TreeNNAccuracy()'
'MeanAveragePrecision' | 'MAP@k' (k is Top-k) (BigDL)
'MeanAveragePrecision' | 'PascalMeanAveragePrecision' (Zoo)
'StatelessMetric' | '${name}'
# Arguments
tag: The string variable represents the scalar wanted
"""
return callZooFunc(self.bigdl_type, "zooGetScalarFromSummary",
self.value, tag, "Validation")
[docs] def set_checkpoint(self, path, over_write=True):
"""
Configure checkpoint settings to write snapshots every epoch during the training process.
In order to take effect, it needs to be called before fit.
# Arguments
path: The path to save snapshots. Make sure this path exists beforehand.
over_write: Whether to overwrite existing snapshots in the given path. Default is True.
"""
callZooFunc(self.bigdl_type, "zooSetCheckpoint",
self.value,
path,
over_write)
[docs] def clear_gradient_clipping(self):
"""
Clear gradient clipping parameters. In this case, gradient clipping will not be applied.
In order to take effect, it needs to be called before fit.
"""
callZooFunc(self.bigdl_type, "zooClearGradientClipping",
self.value)
[docs] def set_constant_gradient_clipping(self, min, max):
"""
Set constant gradient clipping during the training process.
In order to take effect, it needs to be called before fit.
# Arguments
min: The minimum value to clip by. Float.
max: The maximum value to clip by. Float.
"""
callZooFunc(self.bigdl_type, "zooSetConstantGradientClipping",
self.value,
float(min),
float(max))
[docs] def set_gradient_clipping_by_l2_norm(self, clip_norm):
"""
Clip gradient to a maximum L2-Norm during the training process.
In order to take effect, it needs to be called before fit.
# Arguments
clip_norm: Gradient L2-Norm threshold. Float.
"""
callZooFunc(self.bigdl_type, "zooSetGradientClippingByL2Norm",
self.value,
float(clip_norm))
[docs] def set_evaluate_status(self):
"""
Set the model to be in evaluate status, i.e. remove the effect of Dropout, etc.
"""
callZooFunc(self.bigdl_type, "zooSetEvaluateStatus",
self.value)
return self
[docs] def fit(self, x, y=None, batch_size=32, nb_epoch=10,
validation_split=0, validation_data=None, distributed=True):
"""
Train a model for a fixed number of epochs on a DataSet.
# Arguments
x: Input data. A Numpy array or RDD of Sample, ImageSet or TextSet.
y: Labels. A Numpy array. Default is None if x is already Sample RDD or ImageSet or TextSet.
batch_size: Number of samples per gradient update. Default is 32.
nb_epoch: Number of epochs to train.
validation_data: Tuple (x_val, y_val) where x_val and y_val are both Numpy arrays.
Can also be RDD of Sample or ImageSet or TextSet.
Default is None if no validation is involved.
distributed: Boolean. Whether to train the model in distributed mode or local mode.
Default is True. In local mode, x and y must both be Numpy arrays.
"""
if distributed:
if isinstance(x, np.ndarray) and isinstance(y, np.ndarray):
if validation_data:
validation_data = to_sample_rdd(*validation_data)
elif validation_split != 0:
if validation_split > 1 or validation_split < 0:
raise TypeError("validation split must in range [0, 1]")
split_index = int(len(x) * (1 - validation_split))
validation_data = (x[split_index:], y[split_index:])
x, y = x[:split_index], y[:split_index]
validation_data = to_sample_rdd(*validation_data)
training_data = to_sample_rdd(x, y)
elif (isinstance(x, RDD) or isinstance(x, ImageSet) or isinstance(x, TextSet)) \
or isinstance(x, FeatureSet) and not y:
training_data = x
else:
raise TypeError("Unsupported training data type: %s" % type(x))
callZooFunc(self.bigdl_type, "zooFit",
self.value,
training_data,
batch_size,
nb_epoch,
validation_data)
else:
if validation_data:
val_x = [JTensor.from_ndarray(x) for x in to_list(validation_data[0])]
val_y = JTensor.from_ndarray(validation_data[1])
else:
val_x, val_y = None, None
callZooFunc(self.bigdl_type, "zooFit",
self.value,
[JTensor.from_ndarray(x) for x in to_list(x)],
JTensor.from_ndarray(y),
batch_size,
nb_epoch,
val_x,
val_y)
[docs] def evaluate(self, x, y=None, batch_size=32):
"""
Evaluate a model on a given dataset in distributed mode.
# Arguments
x: Evaluation data. A Numpy array or RDD of Sample or ImageSet or TextSet.
y: Labels. A Numpy array.
Default is None if x is already Sample RDD or ImageSet or TextSet.
batch_size: Number of samples per batch. Default is 32.
"""
if isinstance(x, np.ndarray) and isinstance(y, np.ndarray):
data = to_sample_rdd(x, y)
elif (isinstance(x, RDD) or isinstance(x, ImageSet) or isinstance(x, TextSet)) and not y:
data = x
else:
raise TypeError("Unsupported evaluation data type: %s" % type(x))
return callZooFunc(self.bigdl_type, "zooEvaluate",
self.value,
data,
batch_size)
[docs] def forward(self, input):
"""
NB: It's for debug only, please use optimizer.optimize() in production.
Takes an input object, and computes the corresponding output of the module
:param input: ndarray or list of ndarray
:param input: ndarray or list of ndarray or JTensor or list of JTensor.
:return: ndarray or list of ndarray
"""
jinput, input_is_table = self.check_input(input)
output = callZooFunc(self.bigdl_type,
"zooForward",
self.value,
jinput,
input_is_table)
return self.convert_output(output)
[docs] @staticmethod
def convert_output(output):
if type(output) is JTensor:
return output.to_ndarray()
elif len(output) == 1:
return KerasNet.convert_output(output[0])
else:
return [KerasNet.convert_output(x) for x in output]
[docs] def predict(self, x, batch_per_thread=4, distributed=True):
"""
Use a model to do prediction.
# Arguments
x: Prediction data. A Numpy array or RDD of Sample or ImageSet.
batch_per_thread:
The default value is 4.
When distributed is True,the total batch size is batch_per_thread * rdd.getNumPartitions.
When distributed is False the total batch size is batch_per_thread * numOfCores.
distributed: Boolean. Whether to do prediction in distributed mode or local mode.
Default is True. In local mode, x must be a Numpy array.
"""
if isinstance(x, ImageSet) or isinstance(x, TextSet):
results = callZooFunc(self.bigdl_type, "zooPredict",
self.value,
x,
batch_per_thread)
return ImageSet(results) if isinstance(x, ImageSet) else TextSet(results)
if distributed:
if isinstance(x, np.ndarray):
data_rdd = to_sample_rdd(x, np.zeros([x.shape[0]]))
elif isinstance(x, RDD):
data_rdd = x
else:
raise TypeError("Unsupported prediction data type: %s" % type(x))
results = callZooFunc(self.bigdl_type, "zooPredict",
self.value,
data_rdd,
batch_per_thread)
return results.map(lambda result: Layer.convert_output(result))
else:
if isinstance(x, np.ndarray) or isinstance(x, list):
results = callZooFunc(self.bigdl_type, "zooPredict",
self.value,
self._to_jtensors(x),
batch_per_thread)
return [Layer.convert_output(result) for result in results]
else:
raise TypeError("Unsupported prediction data type: %s" % type(x))
[docs] def predict_classes(self, x, batch_per_thread=4, zero_based_label=True):
"""
Use a model to predict for classes. By default, label predictions start from 0.
# Arguments
x: Prediction data. A Numpy array or RDD of Sample.
batch_per_partition:
The default value is 4.
When distributed is True, the total batch size is batch_per_thread * rdd.getNumPartitions.
When distributed is False the total batch size is batch_per_thread * numOfCores.
zero_based_label: Boolean. Whether result labels start from 0.
Default is True. If False, result labels start from 1.
"""
if isinstance(x, np.ndarray):
data_rdd = to_sample_rdd(x, np.zeros([x.shape[0]]))
elif isinstance(x, RDD):
data_rdd = x
else:
raise TypeError("Unsupported prediction data type: %s" % type(x))
return callZooFunc(self.bigdl_type, "zooPredictClasses",
self.value,
data_rdd,
batch_per_thread,
zero_based_label)
[docs] def get_layer(self, name):
layer = [l for l in self.layers if l.name() == name]
if (len(layer) == 0):
raise Exception("Could not find a layer named: %s" + name)
elif (len(layer) > 1):
raise Exception("There are multiple layers named: %s" + name)
else:
return layer[0]
[docs] def summary(self, line_length=120, positions=[.33, .55, .67, 1.]):
"""
Print out the summary information of an Analytics Zoo Keras Model.
For each layer in the model, there will be a separate row containing four columns:
________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
================================================================================
In addition, total number of parameters of this model, separated into trainable and
non-trainable counts, will be printed out after the table.
# Arguments
line_length The total length of one row. Default is 120.
positions: The maximum absolute length proportion(%) of each field.
List of Float of length 4.
Usually you don't need to adjust this parameter.
Default is [.33, .55, .67, 1.], meaning that
the first field will occupy up to 33% of line_length,
the second field will occupy up to (55-33)% of line_length,
the third field will occupy up to (67-55)% of line_length,
the fourth field will occupy the remaining line (100-67)%.
If the field has a larger length, the remaining part will be trimmed.
If the field has a smaller length, the remaining part will be white spaces.
"""
callZooFunc(self.bigdl_type, "zooKerasNetSummary",
self.value,
line_length,
[float(p) for p in positions])
[docs] def to_model(self):
from zoo.pipeline.api.keras.models import Model
return Model.from_jvalue(callZooFunc(self.bigdl_type, "kerasNetToModel", self.value))
@property
def layers(self):
jlayers = callZooFunc(self.bigdl_type, "getSubModules", self)
layers = [Layer.of(jlayer) for jlayer in jlayers]
return layers
[docs] def flattened_layers(self, include_container=False):
jlayers = callZooFunc(self.bigdl_type, "getFlattenSubModules", self, include_container)
layers = [Layer.of(jlayer) for jlayer in jlayers]
return layers
[docs]class Merge(ZooKerasLayer):
"""
Used to merge a list of inputs into a single output, following some merge mode.
Merge must have at least two input layers.
When using this layer as the first layer in a model, you need to provide the argument
input_shape for input layers (a list of shape tuples, does not include the batch dimension).
# Arguments
layers: A list of layer instances. Must be more than one layer.
mode: Merge mode. String, must be one of: 'sum', 'mul', 'concat', 'ave', 'cos',
'dot', 'max', 'sub', 'div', 'min'. Default is 'sum'.
concat_axis: Int, axis to use when concatenating layers.
Only specify this when merge mode is 'concat'.
Default is -1, meaning the last axis of the input.
input_shape: A list of shape tuples, each not including batch.
name: String to set the name of the layer.
If not specified, its name will by default to be a generated string.
>>> l1 = InputLayer(input_shape=(3, 5))
creating: createZooKerasInputLayer
>>> l2 = InputLayer(input_shape=(3, 5))
creating: createZooKerasInputLayer
>>> merge = Merge(layers=[l1, l2], mode='sum', name="merge1")
creating: createZooKerasMerge
"""
def __init__(self, layers=None, mode="sum", concat_axis=-1,
input_shape=None, **kwargs):
super(Merge, self).__init__(None,
list(layers) if layers else None,
mode,
concat_axis,
input_shape,
**kwargs)
[docs]def merge(inputs, mode="sum", concat_axis=-1, name=None):
"""
Functional merge. Only use this method if you are defining a graph model.
Used to merge a list of input nodes into a single output node (NOT layers!),
following some merge mode.
# Arguments
inputs: A list of node instances. Must be more than one node.
mode: Merge mode. String, must be one of: 'sum', 'mul', 'concat', 'ave', 'cos',
'dot', 'max', 'sub', 'div', 'min'. Default is 'sum'.
concat_axis: Int, axis to use when concatenating nodes.
Only specify this when merge mode is 'concat'.
Default is -1, meaning the last axis of the input.
name: String to set the name of the functional merge.
If not specified, its name will by default to be a generated string.
"""
return Merge(mode=mode, concat_axis=concat_axis, name=name)(list(inputs))