Keras

## Model visualization The `keras.utils.vis_utils` module provides utility functions to plot a Keras model (using `graphviz`). This will plot a graph of the model and save it to a file: “`python from keras.utils import plot_model plot_model(model, to_file=’model.png’) “` `plot_model` takes two optional arguments: – `show_shapes` (defaults to False) controls whether output shapes are shown in […]

## Usage of activations Activations can either be used through an `Activation` layer, or through the `activation` argument supported by all forward layers: “`python from keras.layers import Activation, Dense model.add(Dense(64)) model.add(Activation(‘tanh’)) “` This is equivalent to: “`python model.add(Dense(64, activation=’tanh’)) “` You can also pass an element-wise TensorFlow/Theano/CNTK function as an activation: “`python from keras import

# Getting started with the Keras Sequential model 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: “`python from keras.models import Sequential from keras.layers import Dense, Activation model = Sequential([ Dense(32, input_shape=(784,)), Activation(‘relu’), Dense(10), Activation(‘softmax’), ]) “` You can

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# About Keras models There are two types of models available in Keras: [the Sequential model](/models/sequential) and [the Model class used with functional API](/models/model). These models have a number of methods in common: – `model.summary()`: prints a summary representation of your model. Shortcut for [utils.print_summary](/utils/#print_summary) – `model.get_config()`: returns a dictionary containing the configuration of the

## Usage of loss functions A loss function (or objective function, or optimization score function) is one of the two parameters required to compile a model: “`python model.compile(loss=’mean_squared_error’, optimizer=’sgd’) “` “`python from keras import losses model.compile(loss=losses.mean_squared_error, optimizer=’sgd’) “` You can either pass the name of an existing loss function, or pass a TensorFlow/Theano symbolic function

# Writing your own Keras layers For simple, stateless custom operations, you are probably better off using `layers.core.Lambda` layers. But for any custom operation that has trainable weights, you should implement your own layer. Here is the skeleton of a Keras layer, **as of Keras 2.0** (if you have an older version, please upgrade). There