Lazily one-hot encoding bed sequences using Keras Sequence.
Project description
Lazily one-hot encoding bed sequences using Keras Sequence.
How do I install this package?
As usual, just download it using pip:
pip install keras_bed_sequenceTests Coverage
Since some software handling coverages sometimes get slightly different results, here’s three of them:
Usage examples
The following examples are tested within the package test suite.
Classification task example
Let’s start by building an extremely simple classification task model:
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from keras_mixed_sequence import MixedSequence
model = Sequential([
Flatten(),
Dense(1)
])
model.compile(
optimizer="nadam",
loss="MSE"
)We then proceed to load the training data into Keras Sequences, using, in particular, a MixedSequence object:
import numpy as np
from keras_mixed_sequence import MixedSequence
from keras_bed_sequence import BedSequence
batch_size = 32
bed_sequence = BedSequence(
"hg19",
"path/to/bed/files.bed",
batch_size
)
y = the_output_values
mixed_sequence = MixedSequence(
x=bed_sequence,
y=y,
batch_size=batch_size
)Finally, we can proceed to use the obtained MixedSequence to train our model:
model.fit(
mixed_sequence,
steps_per_epoch=mixed_sequence.steps_per_epoch,
epochs=2,
verbose=0,
shuffle=True
)Auto-encoding task example
Let’s start by building an extremely simple auto-encoding task model:
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, Reshape, Conv2DTranspose
model = Sequential([
Reshape((200, 4, 1)),
Conv2D(16, kernel_size=3, activation="relu"),
Conv2DTranspose(1, kernel_size=3, activation="relu"),
Reshape((-1, 200, 4))
])
model.compile(
optimizer="nadam",
loss="MSE"
)We then proceed to load the training data into Keras Sequences, using, in particular, a MixedSequence object:
import numpy as np
from keras_mixed_sequence import MixedSequence
from keras_bed_sequence import BedSequence
batch_size = 32
bed_sequence = BedSequence(
Genome("hg19", chromosomes=["chr1"]),
"path/to/bed/files.bed",
batch_size
)
mixed_sequence = MixedSequence(
x=bed_sequence,
y=bed_sequence,
batch_size=batch_size
)Finally, we can proceed to use the obtained MixedSequence to train our model:
model.fit(
mixed_sequence,
steps_per_epoch=mixed_sequence.steps_per_epoch,
epochs=2,
verbose=0,
shuffle=True
)Multi-task example (classification + auto-encoding)
Let’s start by building an extremely simple multi-tasks model:
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Conv2D, Reshape, Flatten, Conv2DTranspose, Input
inputs = Input(shape=(200, 4))
flattened = Flatten()(inputs)
output1 = Dense(
units=1,
activation="relu",
name="output1"
)(flattened)
hidden = Reshape((200, 4, 1))(inputs)
hidden = Conv2D(16, kernel_size=3, activation="relu")(hidden)
hidden = Conv2DTranspose(1, kernel_size=3, activation="relu")(hidden)
output2 = Reshape((200, 4), name="output2")(hidden)
model = Model(
inputs=inputs,
outputs=[output1, output2],
name="my_model"
)
model.compile(
optimizer="nadam",
loss="MSE"
)We then proceed to load the training data into Keras Sequences, using, in particular, a MixedSequence object:
import numpy as np
from keras_mixed_sequence import MixedSequence
from keras_bed_sequence import BedSequence
batch_size = 32
bed_sequence = BedSequence(
"hg19",
"{cwd}/test.bed".format(
cwd=os.path.dirname(os.path.abspath(__file__))
),
batch_size
)
y = np.random.randint(
2,
size=(bed_sequence.samples_number, 1)
)
mixed_sequence = MixedSequence(
bed_sequence,
{
"output1": y,
"output2": bed_sequence
},
batch_size
)Finally, we can proceed to use the obtained MixedSequence to train our model:
model.fit(
mixed_sequence,
steps_per_epoch=mixed_sequence.steps_per_epoch,
epochs=2,
verbose=0,
shuffle=True
)Release history Release notifications | RSS feed
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