kERAS

Página 1

Página 2

Keras makes it easy to turn models into products

Your Keras models can be easily deployed across a greater range of platforms than any other deep learning framework:

On iOS, via Apple’s CoreML (Keras support officially provided by Apple). Here's a tutorial.

On Android, via the TensorFlow Android runtime. Example: Not Hotdog app.

In the browser, via GPU-accelerated JavaScript runtimes such as Keras.js and WebDNN.

On Google Cloud, via TensorFlow-Serving.

In a Python webapp backend (such as a Flask app).

On the JVM, via DL4J model import provided by SkyMind.

On Raspberry Pi.

The Sequential model is a linear stack of layers.

You can create a Sequential model by passing a list of layer instances to the constructor:

from keras.models import Sequential

from keras.layers import Dense, Activation

model = Sequential([ Dense(32, input_shape=(784,)),

Activation('relu'),

Dense(10),

Activation('softmax'), ])

The model needs to know what input shape it should expect.

You can also simply add layers via the .add() method:

model = Sequential() model.add(Dense(32, input_dim=784)) model.add(Activation('relu'))

Compilation

Before training a model, you need to configure the learning process, which is done via the compile method. It receives three arguments:

An optimizer. This could be the string identifier of an existing optimizer (such as rmsprop or adagrad), or an instance of the Optimizer class. See: optimizers

Used for building simple models

simple classification task

encoder decoder models

Página 3