How to do it...

1. We start by installing Keras on our local Anaconda environment as follows:

conda install -c conda-forge keras 

Make sure your deep learning environment is activated before executing this command.

1. Next, we import keras library into our Python environment:

from keras.models import Sequential
from keras.layers import Dense

This command outputs the backend used by Keras. By default, the TensorFlow framework is used:

1. To provide a dummy dataset, we will use numpy and the following code:

import numpy as np
x_input = np.array([[1,2,3,4,5]])
y_input = np.array([[10]])

1. When using sequential mode, it's straightforward to stack multiple layers in Keras. In this example, we use one hidden layer with 32 units and an output layer with one unit:

model = Sequential()
model.add(Dense(units=32, input_dim=x_input.shape[1]))
model.add(Dense(units=1))

1. Next, we need to compile our model. While compiling, we can set different settings such as loss function, optimizer, and metrics:

model.compile(loss='mse',
              optimizer='sgd',
              metrics=['accuracy'])

1. In Keras, you can easily print a summary of your model. It will also show the number of parameters within the defined model:

model.summary()

In the following figure, you can see the model summary of our build model:

1. Training the model is straightforward with one command, while simultaneously saving the results to a variable called history:

history = model.fit(x_input, y_input, epochs=10, batch_size=32)

1. For testing, the prediction function can be used after training:

pred = model.predict(x_input, batch_size=128)