The Sequential class

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form.onsubmit = function(e) { e.preventDefault(); var query = document.getElementById('search-input').value; window.location.href = '/search.html?query=' + query; return False } </script> </div> <div class='k-main-inner' id='k-main-id'> <div class='k-location-slug'> ► <a href='/api/'>Keras 3 API documentation</a> / <a href='/api/models/'>Models API</a> / The Sequential class </div> <div class='k-content'> <h1 id="the-sequential-class">The Sequential class</h1> <a href="https://github.com/keras-team/keras/tree/v3.7.0/keras/src/models/sequential.py#L19">[source]</a> <h3 id="sequential-class"><code>Sequential</code> class</h3> <div class="codehilite"><pre><code>keras.Sequential(layers=None, trainable=True, name=None) </code></pre></div> <code>Sequential</code> groups a linear stack of layers into a <code>Model</code>. Examples <div class="codehilite"><pre><code>model = keras.Sequential() model.add(keras.Input(shape=(16,))) model.add(keras.layers.Dense(8)) # Note that you can also omit the initial `Input`. # In that case the model doesn't have any weights until the first call # to a training/evaluation method (since it isn't yet built): model = keras.Sequential() model.add(keras.layers.Dense(8)) model.add(keras.layers.Dense(4)) # model.weights not created yet # Whereas if you specify an `Input`, the model gets built # continuously as you are adding layers: model = keras.Sequential() model.add(keras.Input(shape=(16,))) model.add(keras.layers.Dense(8)) len(model.weights) # Returns "2" # When using the delayed-build pattern (no input shape specified), you can # choose to manually build your model by calling # `build(batch_input_shape)`: model = keras.Sequential() model.add(keras.layers.Dense(8)) model.add(keras.layers.Dense(4)) model.build((None, 16)) len(model.weights) # Returns "4" # Note that when using the delayed-build pattern (no input shape specified), # the model gets built the first time you call `fit`, `eval`, or `predict`, # or the first time you call the model on some input data. model = keras.Sequential() model.add(keras.layers.Dense(8)) model.add(keras.layers.Dense(1)) model.compile(optimizer='sgd', loss='mse') # This builds the model for the first time: model.fit(x, y, batch_size=32, epochs=10) </code></pre></div> <hr /> <a href="https://github.com/keras-team/keras/tree/v3.7.0/keras/src/models/sequential.py#L78">[source]</a> <h3 id="add-method"><code>add</code> method</h3> <div class="codehilite"><pre><code>Sequential.add(layer, rebuild=True) </code></pre></div> Adds a layer instance on top of the layer stack. Arguments <ul> <li>layer: layer instance.</li> </ul> <hr /> <a href="https://github.com/keras-team/keras/tree/v3.7.0/keras/src/models/sequential.py#L127">[source]</a> <h3 id="pop-method"><code>pop</code> method</h3> <div class="codehilite"><pre><code>Sequential.pop(rebuild=True) </code></pre></div> Removes the last layer in the model. <hr /> </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#the-sequential-class'>The Sequential class</a> </div> <div class='k-outline-depth-3'> <a href='#sequential-class'><code>Sequential</code> class</a> </div> <div class='k-outline-depth-3'> <a href='#add-method'><code>add</code> method</a> </div> <div class='k-outline-depth-3'> <a href='#pop-method'><code>pop</code> method</a> </div> </div> </div> </div> </div> </body> <footer style="float: left; width: 100%; padding: 1em; border-top: solid 1px #bbb;"> <a href="https://policies.google.com/terms">Terms</a> | <a href="https://policies.google.com/privacy">Privacy</a> </footer> </html>

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The Sequential class