python-kerasHow do I create a dense layer in Python using Keras?

In order to create a dense layer in Python using Keras, the following steps must be taken:

1. Import the necessary libraries. This includes keras, tensorflow, and numpy:

import keras
import tensorflow as tf
import numpy as np

2. Create a Sequential model:

model = keras.Sequential()

3. Add a Dense layer to the model. This layer will have 10 nodes and will use the relu activation function:

model.add(keras.layers.Dense(10, activation='relu'))

4. Compile the model using the Adam optimizer and mean_squared_error as the loss function:

model.compile(optimizer='Adam', loss='mean_squared_error')

5. Train the model using a numpy array of data:

model.fit(x_train, y_train)

6. Make predictions using a numpy array of data:

predictions = model.predict(x_test)

7. Evaluate the model using a numpy array of data:

model.evaluate(x_test, y_test)

Helpful links

1. Keras Documentation
2. TensorFlow Documentation

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