def train_and_test(X_train,
Y_train,
X_test=X_test,
Y_test=Y_test,
num_buckets=30000):
# Define a vanilla LSTM model with Keras
lstm_model = get_keras_lstm(num_buckets)
lstm_model.fit(X_train, Y_train, epochs=5, verbose=0)
preds_test = lstm_model.predict(X_test)[:, 0] > 0.5
return (preds_test == Y_test).mean()
def train_and_test(
X_train,
Y_train,
X_valid=X_valid,
Y_valid=Y_valid,
X_test=X_test,
Y_test=Y_test,
num_buckets=30000,
):
# Define a vanilla LSTM model with Keras
lstm_model = get_keras_lstm(num_buckets)
lstm_model.fit(
X_train,
Y_train,
epochs=25,
validation_data=(X_valid, Y_valid),
callbacks=[get_keras_early_stopping(5)],
verbose=0,
)
preds_test = lstm_model.predict(X_test)[:, 0] > 0.5
return (preds_test == Y_test).mean()