def test_tab_props_extraction():
data_path = pkg_resources.resource_filename('janggu', 'resources/')
csvfile = os.path.join(data_path, 'sample.csv')
sam1 = Table('sample1', filename=csvfile)
sam2 = Table('sample2', filename=csvfile)
props = _data_props([sam1, sam2])
assert 'sample1' in props
assert 'sample2' in props
assert props['sample1']['shape'] == (1, )
assert props['sample2']['shape'] == (1, )
with pytest.raises(Exception):
_data_props((0, ))
def test_dna_props_extraction(tmpdir):
os.environ['JANGGU_OUTPUT'] = tmpdir.strpath
data_path = pkg_resources.resource_filename('janggu', 'resources/')
bed_file = os.path.join(data_path, 'sample.bed')
refgenome = os.path.join(data_path, 'sample_genome.fa')
dna = Bioseq.create_from_refgenome('dna',
refgenome=refgenome,
storage='ndarray',
roi=bed_file,
binsize=200,
stepsize=200,
order=1)
props = _data_props(dna)
assert 'dna' in props
assert props['dna']['shape'] == (200, 1, 4)
with pytest.raises(Exception):
_data_props((0, ))
def create_model(template, modelparams=None, inputs=None,
outputs=None, name=None):
"""Constructor method.
This method instantiates a keras model with
model template and parameters. It also allows to
automatically infer and extend the correct
input and output layers for the network.
Parameters
-----------
template : function
Python function that defines a model template of a neural network.
The function signature must adhere to the signature
:code:`template(inputs, inputp, outputp, modelparams)`
and is expected to return
:code:`(input_tensor, output_tensor)` of the neural network.
modelparams : list or tuple or None
Additional model parameters that are passed along to template
upon creation of the neural network. For instance,
this could contain number of neurons at each layer.
Default: None.
inputs : Dataset, list(Dataset) or None
Input datasets from which the input layer shapes should be derived.
Use this option together with the :code:`inputlayer` decorator (see Example below).
outputs : Dataset, list(Dataset) or None
Output datasets from which the output layer shapes should be derived.
Use this option toghether with :code:`outputdense` or :code:`outputconv`
decorators (see Example below).
name : str or None
Model name. If None, a unique model name is generated
based on the model configuration and network architecture.
Examples
--------
Specify a model using a model template and parameters:
.. code-block:: python
def test_manual_model(inputs, inp, oup, params):
in_ = Input(shape=(10,), name='ip')
layer = Dense(params)(in_)
output = Dense(1, activation='sigmoid', name='out')(in_)
return in_, output
# Defines the same model by invoking the definition function
# and the create constructor.
model = create_model(template=test_manual_model, modelparams=3)
model.summary()
Specify a model using automatic input and output layer determination.
That is, only the model body needs to be specified:
.. code-block:: python
import numpy as np
from janggu import inputlayer, outputdense
from janggu.data import Array
# Some random data which you would like to use as input for the
# model.
DATA = Array('ip', np.random.random((1000, 10)))
LABELS = Array('out', np.random.randint(2, size=(1000, 1)))
# The decorators inputlayer and outputdense
# extract the layer shapes and append the respective layers
# to the network
# so that only the model body remains to be specified.
# Note that the the decorator order matters.
# inputlayer must be specified before outputdense.
@inputlayer
@outputdense(activation='sigmoid')
def test_inferred_model(inputs, inp, oup, params):
with inputs.use('ip') as in_:
# the with block allows for easy
# access of a specific named input.
output = Dense(params)(in_)
return in_, output
# create a model.
model = create_model(template=test_inferred_model, modelparams=3,
name='test_model',
inputp=DATA,
outputp=LABELS)
"""
inputs_ = _data_props(inputs) if inputs else None
outputs_ = _data_props(outputs) if outputs else None
input_tensors, output_tensors = template(None, inputs_,
outputs_, modelparams)
model = Model(inputs=input_tensors, outputs=output_tensors, name=name)
return model