Keras: Konsep Secara Umum
Getting started: 30 seconds to Keras
The core data structure of Keras is a model, a way to organize layers. The simplest type of model is the Sequential model, a linear stack of layers. For more complex architectures, you should use the Keras functional API, which allows to build arbitrary graphs of layers.
Here is the Sequential model:
from keras.models import Sequential
model = Sequential()
Stacking layers is as easy as .add():
from keras.layers import Dense model.add(Dense(units=64, activation='relu', input_dim=100)) model.add(Dense(units=10, activation='softmax'))
Once your model looks good, configure its learning process with .compile():
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
If you need to, you can further configure your optimizer. A core principle of Keras is to make things reasonably simple, while allowing the user to be fully in control when they need to (the ultimate control being the easy extensibility of the source code).
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.SGD(lr=0.01, momentum=0.9, nesterov=True))
You can now iterate on your training data in batches:
# x_train and y_train are Numpy arrays --just like in the Scikit-Learn API. model.fit(x_train, y_train, epochs=5, batch_size=32)
Alternatively, you can feed batches to your model manually:
model.train_on_batch(x_batch, y_batch)
Evaluate your performance in one line:
loss_and_metrics = model.evaluate(x_test, y_test, batch_size=128)
Or generate predictions on new data:
classes = model.predict(x_test, batch_size=128)
Building a question answering system, an image classification model, a Neural Turing Machine, or any other model is just as fast. The ideas behind deep learning are simple, so why should their implementation be painful?