Specifying the modified Flagel et al. CNN architecture

The Python scripts containing code to train the modified Flagel et al. network can be found in the files train_*_flagel.py. They can be run using the following command commands:

# Here divergence_scaling is either 0.5, 1.0, or 2.0 coalescent units

# Minimum dXY network
python3 train_min_flagel.py --coal_units <divergence_scaling>

# Mean dXY network
python3 train_mean_flagel.py --coal_units <divergence_scaling>

TensorFlow imports

import tensorflow.keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import (
  Dense,
  Dropout,
  Flatten
)
from tensorflow.keras.layers import (
  Conv1D,
  AveragePooling1D
)
from tensorflow.keras.callbacks import (
  EarlyStopping,
  ModelCheckpoint
)
from tensorflow.keras.losses import categorical_crossentropy
from tensorflow.keras.optimizers import Adam

Specifying the network architecture

model = Sequential()
model.add(
    Conv1D(
        64, kernel_size=2,
        activation='relu',
        input_shape=(xtrain.shape[1],xtrain.shape[2])
    )
)
model.add(
    Conv1D(
        32, kernel_size=2,
        activation='relu'
    )
)
model.add(
    AveragePooling1D(
        pool_size=2
    )
)
model.add(Dropout(0.25))

model.add(
    Conv1D(
        32, kernel_size=2,
        activation='relu'
    )
)
model.add(
    AveragePooling1D(
        pool_size=2
    )
)
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4, activation='softmax'))
model.compile(
    loss=categorical_crossentropy,
    optimizer=Adam(),
    metrics=['accuracy']
)
print(model.summary())

callbacks = [
    EarlyStopping(monitor='val_loss'),
    ModelCheckpoint(
        filepath='hyde_flagel_mean_{}.mdl'.format(cu),
        monitor='val_loss',
        save_best_only=True
    )
]
model.fit(
    xtrain, ytrain,
    batch_size=32,
    epochs=10,
    verbose=1,
    callbacks=callbacks,
    validation_data=(xval,yval)
)

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References

• L Flagel, Y Brandvain, and DR Schrider. 2019. The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference. Molecular Biology and Evolution 36:220–238. https://doi.org/10.1093/molbev/msy224.