After initializing your AutoEncoder you are all set to train it. Which of the following pieces of code will you use? def autoencoder_training (X_train, Y_train, epochs): history autoencoder.fit (# YOUR CODE HERE) return history Options: i. autoencoder.fit(X_train, ...Read more

Consider the following code for a simple AutoEncoder, what is model_1 outputting ? inputs = tf.keras.layers.Input(shape=(784,)) def simple_autoencoder(): encoder = tf.keras.layers.Dense(units=32, activation=’relu’)(inputs) decoder = tf.keras.layers.Dense(units=784, activation=’sigmoid’)(encoder) return encoder, decoder output_1, output_2 = simple_autoencoder() model_1 = tf.keras.Model(inputs=inputs, outputs=output_1) model_2 = tf.keras.Model(inputs=inputs, outputs=output_2) options: Displaying the reconstruction of the original input ...Read more

Consider the following code snippet. How will you include Total Loss Variation in it? Use TensorFlow as tf. (Answer in the format, x + y(z), considering python’s spacing convention) def calculate_gradients(image, content_targets, style_targets, style_weight, content_weight,with_regularization=True): total_variation_weight = 30 with tf.GradientTape() as tape: if with_regularization: loss += ...Read more