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<p><span style="float:right;"><a href="https://github.com/keras-team/keras/blob/master/keras/layers/local.py#L19">[source]</a></span></p>
<h3 id="locallyconnected1d">LocallyConnected1D</h3>
<pre><code class="python">keras.layers.LocallyConnected1D(filters, kernel_size, strides=1, padding='valid', data_format=None, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)
</code></pre>
<p>Locally-connected layer for 1D inputs.</p>
<p>The <code>LocallyConnected1D</code> layer works similarly to
the <code>Conv1D</code> layer, except that weights are unshared,
that is, a different set of filters is applied at each different patch
of the input.</p>
<p><strong>Example</strong></p>
<pre><code class="python"># apply a unshared weight convolution 1d of length 3 to a sequence with
# 10 timesteps, with 64 output filters
model = Sequential()
model.add(LocallyConnected1D(64, 3, input_shape=(10, 32)))
# now model.output_shape == (None, 8, 64)
# add a new conv1d on top
model.add(LocallyConnected1D(32, 3))
# now model.output_shape == (None, 6, 32)
</code></pre>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>filters</strong>: Integer, the dimensionality of the output space
(i.e. the number of output filters in the convolution).</li>
<li><strong>kernel_size</strong>: An integer or tuple/list of a single integer,
specifying the length of the 1D convolution window.</li>
<li><strong>strides</strong>: An integer or tuple/list of a single integer,
specifying the stride length of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any <code>dilation_rate</code> value != 1.</li>
<li><strong>padding</strong>: Currently only supports <code>"valid"</code> (case-insensitive).
<code>"same"</code> may be supported in the future.</li>
<li><strong>data_format</strong>: String, one of <code>channels_first</code>, <code>channels_last</code>.</li>
<li><strong>activation</strong>: Activation function to use
(see <a href="../../activations/">activations</a>).
If you don't specify anything, no activation is applied
(ie. "linear" activation: <code>a(x) = x</code>).</li>
<li><strong>use_bias</strong>: Boolean, whether the layer uses a bias vector.</li>
<li><strong>kernel_initializer</strong>: Initializer for the <code>kernel</code> weights matrix
(see <a href="../../initializers/">initializers</a>).</li>
<li><strong>bias_initializer</strong>: Initializer for the bias vector
(see <a href="../../initializers/">initializers</a>).</li>
<li><strong>kernel_regularizer</strong>: Regularizer function applied to
the <code>kernel</code> weights matrix
(see <a href="../../regularizers/">regularizer</a>).</li>
<li><strong>bias_regularizer</strong>: Regularizer function applied to the bias vector
(see <a href="../../regularizers/">regularizer</a>).</li>
<li><strong>activity_regularizer</strong>: Regularizer function applied to
the output of the layer (its "activation").
(see <a href="../../regularizers/">regularizer</a>).</li>
<li><strong>kernel_constraint</strong>: Constraint function applied to the kernel matrix
(see <a href="../../constraints/">constraints</a>).</li>
<li><strong>bias_constraint</strong>: Constraint function applied to the bias vector
(see <a href="../../constraints/">constraints</a>).</li>
</ul>
<p><strong>Input shape</strong></p>
<p>3D tensor with shape: <code>(batch_size, steps, input_dim)</code></p>
<p><strong>Output shape</strong></p>
<p>3D tensor with shape: <code>(batch_size, new_steps, filters)</code>
<code>steps</code> value might have changed due to padding or strides.</p>
<hr />
<p><span style="float:right;"><a href="https://github.com/keras-team/keras/blob/master/keras/layers/local.py#L183">[source]</a></span></p>
<h3 id="locallyconnected2d">LocallyConnected2D</h3>
<pre><code class="python">keras.layers.LocallyConnected2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)
</code></pre>
<p>Locally-connected layer for 2D inputs.</p>
<p>The <code>LocallyConnected2D</code> layer works similarly
to the <code>Conv2D</code> layer, except that weights are unshared,
that is, a different set of filters is applied at each
different patch of the input.</p>
<p><strong>Examples</strong></p>
<pre><code class="python"># apply a 3x3 unshared weights convolution with 64 output filters
# on a 32x32 image with `data_format="channels_last"`:
model = Sequential()
model.add(LocallyConnected2D(64, (3, 3), input_shape=(32, 32, 3)))
# now model.output_shape == (None, 30, 30, 64)
# notice that this layer will consume (30*30)*(3*3*3*64)
# + (30*30)*64 parameters
# add a 3x3 unshared weights convolution on top, with 32 output filters:
model.add(LocallyConnected2D(32, (3, 3)))
# now model.output_shape == (None, 28, 28, 32)
</code></pre>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>filters</strong>: Integer, the dimensionality of the output space
(i.e. the number of output filters in the convolution).</li>
<li><strong>kernel_size</strong>: An integer or tuple/list of 2 integers, specifying the
width and height of the 2D convolution window.
Can be a single integer to specify the same value for
all spatial dimensions.</li>
<li><strong>strides</strong>: An integer or tuple/list of 2 integers,
specifying the strides of the convolution along the width and height.
Can be a single integer to specify the same value for
all spatial dimensions.</li>
<li><strong>padding</strong>: Currently only support <code>"valid"</code> (case-insensitive).
<code>"same"</code> will be supported in future.</li>
<li><strong>data_format</strong>: A string,
one of <code>channels_last</code> (default) or <code>channels_first</code>.
The ordering of the dimensions in the inputs.
<code>channels_last</code> corresponds to inputs with shape
<code>(batch, height, width, channels)</code> while <code>channels_first</code>
corresponds to inputs with shape
<code>(batch, channels, height, width)</code>.
It defaults to the <code>image_data_format</code> value found in your
Keras config file at <code>~/.keras/keras.json</code>.
If you never set it, then it will be "channels_last".</li>
<li><strong>activation</strong>: Activation function to use
(see <a href="../../activations/">activations</a>).
If you don't specify anything, no activation is applied
(ie. "linear" activation: <code>a(x) = x</code>).</li>
<li><strong>use_bias</strong>: Boolean, whether the layer uses a bias vector.</li>
<li><strong>kernel_initializer</strong>: Initializer for the <code>kernel</code> weights matrix
(see <a href="../../initializers/">initializers</a>).</li>
<li><strong>bias_initializer</strong>: Initializer for the bias vector
(see <a href="../../initializers/">initializers</a>).</li>
<li><strong>kernel_regularizer</strong>: Regularizer function applied to
the <code>kernel</code> weights matrix
(see <a href="../../regularizers/">regularizer</a>).</li>
<li><strong>bias_regularizer</strong>: Regularizer function applied to the bias vector
(see <a href="../../regularizers/">regularizer</a>).</li>
<li><strong>activity_regularizer</strong>: Regularizer function applied to
the output of the layer (its "activation").
(see <a href="../../regularizers/">regularizer</a>).</li>
<li><strong>kernel_constraint</strong>: Constraint function applied to the kernel matrix
(see <a href="../../constraints/">constraints</a>).</li>
<li><strong>bias_constraint</strong>: Constraint function applied to the bias vector
(see <a href="../../constraints/">constraints</a>).</li>
</ul>
<p><strong>Input shape</strong></p>
<p>4D tensor with shape:
<code>(samples, channels, rows, cols)</code> if data_format='channels_first'
or 4D tensor with shape:
<code>(samples, rows, cols, channels)</code> if data_format='channels_last'.</p>
<p><strong>Output shape</strong></p>
<p>4D tensor with shape:
<code>(samples, filters, new_rows, new_cols)</code> if data_format='channels_first'
or 4D tensor with shape:
<code>(samples, new_rows, new_cols, filters)</code> if data_format='channels_last'.
<code>rows</code> and <code>cols</code> values might have changed due to padding.</p>
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