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, ...
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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 ...
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 += ...
Q1.Consider the following code for Class Activation Maps. Which layer(s) of the model do we choose as outputs to draw out the class activation map ? Check all that apply. The layer which performs concatenation in the model The layer which feeds ...