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<h1 id="writing-your-own-keras-layers">Writing your own Keras layers</h1>
<p>For simple, stateless custom operations, you are probably better off using <code>layers.core.Lambda</code> layers. But for any custom operation that has trainable weights, you should implement your own layer.</p>
<p>Here is the skeleton of a Keras layer, <strong>as of Keras 2.0</strong> (if you have an older version, please upgrade). There are only three methods you need to implement:</p>
<ul>
<li><code>build(input_shape)</code>: this is where you will define your weights. This method must set <code>self.built = True</code> at the end, which can be done by calling <code>super([Layer], self).build()</code>.</li>
<li><code>call(x)</code>: this is where the layer's logic lives. Unless you want your layer to support masking, you only have to care about the first argument passed to <code>call</code>: the input tensor.</li>
<li><code>compute_output_shape(input_shape)</code>: in case your layer modifies the shape of its input, you should specify here the shape transformation logic. This allows Keras to do automatic shape inference.</li>
</ul>
<pre><code class="python">from keras import backend as K
from keras.layers import Layer
class MyLayer(Layer):
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(MyLayer, self).__init__(**kwargs)
def build(self, input_shape):
# Create a trainable weight variable for this layer.
self.kernel = self.add_weight(name='kernel',
shape=(input_shape[1], self.output_dim),
initializer='uniform',
trainable=True)
super(MyLayer, self).build(input_shape) # Be sure to call this at the end
def call(self, x):
return K.dot(x, self.kernel)
def compute_output_shape(self, input_shape):
return (input_shape[0], self.output_dim)
</code></pre>
<p>It is also possible to define Keras layers which have multiple input tensors and multiple output tensors. To do this, you should assume that the inputs and outputs of the methods <code>build(input_shape)</code>, <code>call(x)</code> and <code>compute_output_shape(input_shape)</code> are lists. Here is an example, similar to the one above:</p>
<pre><code class="python">from keras import backend as K
from keras.layers import Layer
class MyLayer(Layer):
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(MyLayer, self).__init__(**kwargs)
def build(self, input_shape):
assert isinstance(input_shape, list)
# Create a trainable weight variable for this layer.
self.kernel = self.add_weight(name='kernel',
shape=(input_shape[0][1], self.output_dim),
initializer='uniform',
trainable=True)
super(MyLayer, self).build(input_shape) # Be sure to call this at the end
def call(self, x):
assert isinstance(x, list)
a, b = x
return [K.dot(a, self.kernel) + b, K.mean(b, axis=-1)]
def compute_output_shape(self, input_shape):
assert isinstance(input_shape, list)
shape_a, shape_b = input_shape
return [(shape_a[0], self.output_dim), shape_b[:-1]]
</code></pre>
<p>The existing Keras layers provide examples of how to implement almost anything. Never hesitate to read the source code!</p>
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