Add Model.py

Model.py

@@ -0,0 +1,54 @@
+import keras
+from keras.layers import Dense, BatchNormalization
+from keras import regularizers
+from keras.optimizers import Adam
+from keras.callbacks import ModelCheckpoint, EarlyStopping
+
+import pandas as pd
+import numpy as np
+
+# Model parameters:
+activation = 'relu'
+final_activation = 'sigmoid'
+loss = 'binary_crossentropy'
+batchsize = 200
+epochs = 100
+lr = 0.00005
+
+# Model architecture:
+model = keras.Sequential()
+model.add(
+    Dense(units=300, input_dim=x_train.shape[1], activation=activation, kernel_regularizer=regularizers.L1(0.001)))
+model.add(BatchNormalization())
+model.add(Dense(units=102, activation=activation, kernel_regularizer=regularizers.L1(0.001)))
+model.add(BatchNormalization())
+model.add(Dense(units=12, activation=activation, kernel_regularizer=regularizers.L1(0.001)))
+model.add(BatchNormalization())
+model.add(Dense(units=6, activation=activation, kernel_regularizer=regularizers.L1(0.001)))
+model.add(BatchNormalization())
+model.add(Dense(units=1, activation=final_activation))
+
+model.compile(optimizer=Adam(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:
+
+history = model.fit(
+                x_train,
+                y_train,
+                batch_size=batchsize,
+                callbacks=[EarlyStopping(verbose=True, patience=10, monitor='val_loss'), saveModel],
+                epochs=epochs,
+                validation_data=(
+                    x_val,
+                    y_val))