How can Keras be used to train the model using Python Program?

Keras was developed as a part of the research for the project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System). Keras is a deep learning API, which is written in Python. It is a high-level API that has a productive interface that helps solve machine learning problems. It runs on top of the Tensorflow framework. It was built to help experiment in a quick manner. It provides essential abstractions and building blocks that are essential in developing and encapsulating machine learning solutions.

It is highly scalable and comes with cross-platform abilities. This means Keras can be run on TPU or clusters of GPUs. Keras models can also be exported to run in a web browser or a mobile phone as well.

Keras is already present within the Tensorflow package. It can be accessed using the below line of code.

import tensorflow
from tensorflow import keras

The Keras functional API helps create models that are more flexible in comparison to models created using sequential API. The functional API can work with models that have non-linear topology, can share layers and work with multiple inputs and outputs. A deep learning model is usually a directed acyclic graph (DAG) that contains multiple layers. The functional API helps build the graph of layers.

We are using 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. Following is the code snippet to train the model −

Example

print("The model is being plotted")
keras.utils.plot_model(model, "my_resnet.png", show_shapes=True)
print("Split the data into training and test data")
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
print("Convert the type of data to float")
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
print("Compiling the model")
model.compile(
   optimizer=keras.optimizers.RMSprop(1e-3),
   loss=keras.losses.CategoricalCrossentropy(from_logits=True),
   metrics=["acc"],
)
model.fit(x_train[:2000], y_train[:2000], batch_size=64, epochs=2, validation_split=0.2)

Code credit − https://www.tensorflow.org/guide/keras/functional

Output

The model is being plotted
Split the data into training and test data
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
170500096/170498071 [==============================] - 2s 0us/step
Convert the type of data to float
Compiling the model
Epoch 1/2
25/25 [==============================] - 9s 332ms/step - loss: 2.3163 - acc: 0.1028 -
val_loss: 2.2962 - val_acc: 0.1175
Epoch 2/2
25/25 [==============================] - 12s 492ms/step - loss: 2.3155 - acc: 0.1175 -
val_loss: 2.2606 - val_acc: 0.1200
<tensorflow.python.keras.callbacks.History at 0x7f48d3ecfb00>

Explanation

• The input data is split into training and test dataset.

• The data type is converted to the ‘float’ type.

• The model is compiled using the ‘compile’ method.

• The ‘fit’ method is used to fit the model to the training data.