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import keras
from keras.layers import Dense
from keras.optimizers import Nadam
from keras.callbacks import ModelCheckpoint, EarlyStopping
import pandas as pd
import numpy as np
# Model parameters:
activation = 'relu'
final_activation = 'softmax'
loss = 'categorical_crossentropy'
batchsize = 100
epochs = 200
lr = 0.001
# Model architecture:
model = keras.Sequential()
model.add(Dense(units=8, input_shape=(8,), activation=activation))
model.add(Dense(units=8, activation=activation))
model.add(Dense(units=4, activation=activation))
model.add(Dense(units=4, activation=activation))
model.add(Dense(units=2, activation=final_activation))
model.compile(optimizer=Nadam(learning_rate=lr), loss=loss,
metrics=['accuracy', 'AUC'])
model.summary()
# Model checkpoints:
saveModel = ModelCheckpoint('best_model.hdf5',
save_best_only=True,
monitor='val_loss',
mode='min')
# Model training:
model.fit(
x_train,
y_train,
batch_size=batchsize,
callbacks=[EarlyStopping(verbose=True, patience=20, monitor='val_loss'), saveModel],
epochs=epochs,
validation_data=(
x_val,
y_val),
shuffle=True) |