# How can Tensorflow be used with abalone dataset to build a sequential model?

A sequential model can be built in Keras using the ‘Sequential’ method. The number and type of layers are specified inside this method.

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 sequential model is being built")
abalone_model = tf.keras.Sequential([
layers.Dense(64),
layers.Dense(1)
])
abalone_model.compile(loss = tf.losses.MeanSquaredError(),optimizer = tf.optimizers.Adam())
print("The data is being fit to the model")
abalone_model.fit(abalone_features, abalone_labels, epochs=10)

## Output

The sequential model is being built
The data is being fit to the model
Epoch 1/10
104/104 [==============================] - 0s 963us/step - loss: 84.2213
Epoch 2/10
104/104 [==============================] - 0s 924us/step - loss: 16.0268
Epoch 3/10
104/104 [==============================] - 0s 860us/step - loss: 9.4125
Epoch 4/10
104/104 [==============================] - 0s 898us/step - loss: 8.9159
Epoch 5/10
104/104 [==============================] - 0s 912us/step - loss: 7.9076
Epoch 6/10
104/104 [==============================] - 0s 936us/step - loss: 6.8316
Epoch 7/10
104/104 [==============================] - 0s 992us/step - loss: 7.1021
Epoch 8/10
104/104 [==============================] - 0s 1ms/step - loss: 7.0550
Epoch 9/10
104/104 [==============================] - 0s 1ms/step - loss: 6.2762
Epoch 10/10
104/104 [==============================] - 0s 883us/step - loss: 6.5584
<tensorflow.python.keras.callbacks.History at 0x7fda82a35160>

## Explanation

• A regression model is built to predict the 'age' column of the abalone dataset.
• A sequential model is built, since there is a single input tensor.
• The model is compiled (trained), and then the features and labels are passed to the 'Model.fit' method.