# set basic hyperparameters
learning_rate = 0.001
n_epochs = 2000

# define loss and accuracy
bce_loss = tf.keras.losses.BinaryCrossentropy()
accuracy = tf.keras.metrics.Accuracy()

# define optimizer
optimizer = tf.optimizers.Adam(learning_rate = learning_rate)

# save training progress in lists
loss_history = []
accuracy_history = []

for epoch in range(n_epochs):

    with tf.GradientTape() as tape:

        # take binary cross-entropy (bce_loss)
        current_loss = bce_loss(model(df), classification)

        # Update weights based on the gradient of the loss function
        gradients = tape.gradient(current_loss, model.trainable_variables)
        optimizer.apply_gradients(zip(gradients, model.trainable_variables))

        # save in history vectors
        current_loss = current_loss.numpy()
        loss_history.append(current_loss)

        accuracy.update_state(model(df), classification)
        current_accuracy = accuracy.result().numpy()
        accuracy_history.append(current_accuracy)

    # print loss and accuracy scores each 100 epochs
    if (epoch+1) % 100 == 0:
        print(str(epoch+1) + '.tTrain Loss: ' + str(current_loss) + ',tAccuracy: ' + str(current_accuracy))

print('nTraining complete.')