How to use Keras in Python

2 years ago
William Carter
2 minutes

You can build and train deep learning models on Python using the Keras library. Here are the basic steps to use Keras:

  1. Install the Keras library by using the pip command. Run the following command in the terminal or command prompt: pip install keras.
  2. Import the Keras library: In a Python script, import the Keras library using the following code: import keras.
  3. Model Building: The Sequential model class in Keras can be used to create a sequential model, where layers are stacked in order. For example, a simple neural network model can be created using the following code:

    Building a model: The Sequential model class from Keras enables the creation of a sequential model, where layers are stacked in a specific order. For instance, a basic neural network model can be generated with the code provided below:

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

model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))
  1. bring together and organize
model.compile(loss='categorical_crossentropy',
              optimizer='sgd',
              metrics=['accuracy'])
  1. suitable
model.fit(x_train, y_train, epochs=10, batch_size=32)
  1. assess
loss_and_metrics = model.evaluate(x_test, y_test, batch_size=128)
  1. foretell
classes = model.predict(x_new)

The above are the basic steps for building and training models using Keras. Depending on the specific task, more Keras functionalities and layers can be utilized to construct more complex models.

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