Create Model.py

Model.py

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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)