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Home Forums Assignment NPTEL_ Practical Machine learning with tensor flow Practical Machine Learning with Tensorflow – Assignment 8

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    • #350
      Abhishek TyagiAbhishek Tyagi
      Keymaster

      1)- What will be the values of​ (z+w)​ if we execute the following code:

      x = tf.constant (0,0)

      with tf.GradietTape(persistent=True) as t:

      t.watch(x)

      Y = tf.cos(x)

      Z = tf.sin(x) + tf.tanh(x)

      w = z – y + x

      dw_dx = t.gradient(w, x)

      dz_dx = t.gradient(z, x)

      dy_dx = t.gradient(y, x)

      Answer- -1

       

      2)- What will be the values of​ dz_dx​ if we execute the previous code:

      Answer- 2

       

      3)- What will be the values of ​ dw_dx​ if we execute the previous code

      Answer- 3

       

      4)- Please visit ​ this​ notebook for answering the following questions. What will be the output of the model before training if we
      give x = 3.0 as input?

      Answer- 33

       

      5)- What is the range of loss after the 10th epoch during the training process?

      Answer- 1.1 – 1.5

       

      6)- What is the range of loss after the 20th epoch?

      Answer- 1.1 – 1.5

       

       

      7)- What is the range of loss after the 10th epoch if the learning rate was 0.01?

      Answer- 15-25

       

       

      8)- What is the range of loss after the 20th epoch if the learning rate was 0.01?

      Answer- 5-15

       

       

      9)- Suppose you have 4 GPUs (having 2GB memory each) to train your deep learning model. You have 1000 data points in your dataset
      that takes around 50 GB of storage. What will be the best choice for batch size for one device (using Mirrored Strategy in TF)?

      Answer- 32

       

       

      10)- Select the steps that can be performed in a distributed manner:

      Answer- Loss computation
      Model Prediction.

       

      11)- Following strategies can be used in case of multiple machines:
      Answer- Multi-Worker Mirrored Strategy
      Parameter Servers Strategy

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