Applying the deep learning model with Keras

At this point, we are ready to apply Keras to our data.

Getting ready

We will be using the following from Keras:

• from keras.models import Sequential
• from keras.layers import Dense, Activation

How to do it...

This section walks through the following steps to apply a deep learning model, using Keras on our dataset:

1. Import the following libraries to build a Sequential model from keras, using the following script:

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

2. Configure the Sequential model from keras, using the following script:

model = Sequential()
model.add(Dense(32, activation='relu', input_dim=xtrain_array.shape[1]))
model.add(Dense(10, activation='relu'))
model.add(Dense(ytrain_OHE.shape[1], activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

3. We fit and train the model and store the results to a variable called accuracy_history, using the following script:

accuracy_history...