If we include a callback to early stop our training process…
If we include a callback to early stop our training process and prevent overfitting if we do not observe progress in 10 epochs like the one below callback_list = [tf.keras.callbacks.EarlyStopping(monitor=’mae’, patience=10)] where should we include it in our ML architeture?
Read DetailsConsider a portfolio with the following weights w, expected…
Consider a portfolio with the following weights w, expected return on each risky asset ER, and covariance matrix Cov below. w = np.array([0.05, 0.03]) ER = np.array([0.10, 0.02])Cov = np.cov([[0.004, 0.0156], [0.0156, 0.009]]) Which of the following expressions represents the portfolio variance in Python?
Read DetailsConsider the code below num_epochs = 500 batch = 100 # Const…
Consider the code below num_epochs = 500 batch = 100 # Construct hidden layers inputs = tf.keras.layers.Dense(units=16, activation=’relu’, input_shape=[X_train.shape[1]]) hidden = tf.keras.layers.Dense(units=16, activation=’relu’) outputs = tf.keras.layers.Dense(units=1) # Stack the layers model = tf.keras.Sequential([inputs, hidden, outputs]) # Loss function and optimizer(with learning rate) loss = ‘mse’ optimizer = tf.keras.optimizers.Adam(0.001) # Compile the model model.compile(loss=loss, optimizer=optimizer, metrics=[‘mae’]) # Train the model history = model.fit(x_train_normalized, y_train_normalized, epochs=num_epochs, batch_size=batch, validation_split=0.1, verbose=0)After running the code above, how do you pick the new number of epochs to train your model one last time in the whole dataset?
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