| import keras |
| from keras.layers import Dense |
| from keras.optimizers import Adam |
| from keras.callbacks import ModelCheckpoint, EarlyStopping |
|
|
| import pandas as pd |
| import numpy as np |
|
|
| |
| activation = 'relu' |
| final_activation = 'sigmoid' |
| loss = 'binary_crossentropy' |
| batchsize = 200 |
| epochs = 150 |
| lr = 0.0001 |
| class_weight = {0: 0.33, 1: 1.0} |
|
|
| |
| model = keras.Sequential() |
| model.add(Dense(units=21, input_shape=(21,), activation=activation)) |
| model.add(Dense(units=21, activation=activation)) |
| model.add(Dense(units=12, activation=activation)) |
| model.add(Dense(units=12, activation=activation)) |
| model.add(Dense(units=4, activation=activation)) |
| model.add(Dense(units=4, activation=activation)) |
| model.add(Dense(units=1, activation=final_activation)) |
|
|
| model.compile(optimizer=Adam(learning_rate=lr), loss=loss, |
| metrics=['accuracy', 'AUC']) |
| model.summary() |
|
|
|
|
| |
| saveModel = ModelCheckpoint('best_model.hdf5', |
| save_best_only=True, |
| monitor='val_loss', |
| mode='min') |
|
|
|
|
| |
|
|
| model.fit( |
| x_train, |
| y_train, |
| batch_size=batchsize, |
| callbacks=[EarlyStopping(verbose=True, patience=15, monitor='val_loss'), saveModel], |
| epochs=epochs, |
| validation_data=( |
| x_val, |
| y_val), |
| shuffle=True, |
| class_weight=class_weight) |
