Ahmed Ali

# Decision Tree vs Logistic Regression
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score

dt = DecisionTreeClassifier().fit(X_train, y_train)
lr = LogisticRegression().fit(X_train, y_train)

print("DT AUC:", roc_auc_score(y_test, dt.predict_proba(X_test)[:,1]))
print("LR AUC:", roc_auc_score(y_test, lr.predict_proba(X_test)[:,1]))

model = tf.keras.Sequential([
  tf.keras.layers.Conv2D(32,(3,3),activation="relu"),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128,activation="relu"),
  tf.keras.layers.Dense(10,activation="softmax")
])
model.compile(optimizer="adam",
              loss="sparse_categorical_crossentropy",
              metrics=["accuracy"])

models = {
  "RNN":  tf.keras.layers.SimpleRNN(128),
  "LSTM": tf.keras.layers.LSTM(128),
  "GRU":  tf.keras.layers.GRU(128),
}
for name, layer in models.items():
    m = tf.keras.Sequential([layer, Dense(1)])
    m.compile(optimizer="adam", loss="mse")
    history = m.fit(X_train, y_train, epochs=20)
    print(f"{name} val_loss:", min(history.history["val_loss"]))