- Related Questions & Answers
- How can Tensorflow be used to train and compile the augmented model?
- How can Tensorflow be used to build normalization layer using Python?
- How can Tensorflow be used to train the model using Python?
- How can Tensorflow be used to train and compile a CNN model?
- How can Tensorflow be used to build a normalization layer for the abalone dataset?
- How can Tensorflow and Estimator be used with Boosted trees to train and evaluate the model?
- How can Tensorflow be used with Estimators to train the model for titanic dataset?
- How can Tensorflow be used to create an input function to to train the model?
- How can Tensorflow be used to train and evaluate the titanic dataset?
- How can TensorFlow be used to train the model for Fashion MNIST dataset in Python?
- How can Tensorflow be used with pre-trained model to build the training and validation dataset?
- How can Tensorflow be used with abalone dataset to build a sequential model?
- How can Tensorflow be used to train the model with the stackoverflow question dataset using Python?
- How can Keras be used to train the model using Python?
- How can Tensorflow and pre-trained model after recompiling be used to visualize the data?

- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who

Training and building the model with respect to the abalone data can be done using the ‘compile’ and ‘fit’ methods respectively. The ‘fit’ method also takes the number of epochs as the parameter.

**Read More:**
What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?

We will be using the abalone dataset, which contains a set of measurements of abalone. Abalone is a type of sea snail. The goal is to predict the age based on other measurements.

We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.

print("The model is being compiled") norm_abalone_model.compile(loss = tf.losses.MeanSquaredError(),optimizer = tf.optimizers.Adam()) print("The model is being fit to the data") norm_abalone_model.fit(abalone_features, abalone_labels, epochs=8)

Code credit: https://www.tensorflow.org/tutorials/load_data/csv

The model is being compiled The model is being fit to the data Epoch 1/8 104/104 [==============================] - 0s 989us/step - loss: 98.3651 Epoch 2/8 104/104 [==============================] - 0s 945us/step - loss: 65.4568 Epoch 3/8 104/104 [==============================] - 0s 922us/step - loss: 21.7297 Epoch 4/8 104/104 [==============================] - 0s 912us/step - loss: 6.3429 Epoch 5/8 104/104 [==============================] - 0s 988us/step - loss: 5.0949 Epoch 6/8 104/104 [==============================] - 0s 958us/step - loss: 4.9868 Epoch 7/8 104/104 [==============================] - 0s 1ms/step - loss: 4.8982 Epoch 8/8 104/104 [==============================] - 0s 1ms/step - loss: 4.7936 <tensorflow.python.keras.callbacks.History at 0x7fda8213c898>

- Once the normalization layers are built, the model is trained with the training data.
- Once the training is complete, the features and labels are passed to the data using the 'Model.fit' method.

Advertisements