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<h1 id="about-keras-models">About Keras models</h1>
<p>There are two main types of models available in Keras: <a href="/models/sequential">the Sequential model</a>, and <a href="/models/model">the Model class used with the functional API</a>.</p>
<p>These models have a number of methods and attributes in common:</p>
<ul>
<li><code>model.layers</code> is a flattened list of the layers comprising the model.</li>
<li><code>model.inputs</code> is the list of input tensors of the model.</li>
<li><code>model.outputs</code> is the list of output tensors of the model.</li>
<li><code>model.summary()</code> prints a summary representation of your model. For layers with multiple outputs, <code>multiple</code> is displayed instead of each individual output shape due to size limitations. Shortcut for <a href="/utils/#print_summary">utils.print_summary</a></li>
<li><code>model.get_config()</code> returns a dictionary containing the configuration of the model. The model can be reinstantiated from its config via:</li>
</ul>
<pre><code class="python">config = model.get_config()
model = Model.from_config(config)
# or, for Sequential:
model = Sequential.from_config(config)
</code></pre>
<ul>
<li><code>model.get_weights()</code> returns a list of all weight tensors in the model, as Numpy arrays.</li>
<li><code>model.set_weights(weights)</code> sets the values of the weights of the model, from a list of Numpy arrays. The arrays in the list should have the same shape as those returned by <code>get_weights()</code>.</li>
<li><code>model.to_json()</code> returns a representation of the model as a JSON string. Note that the representation does not include the weights, only the architecture. You can reinstantiate the same model (with reinitialized weights) from the JSON string via:</li>
</ul>
<pre><code class="python">from keras.models import model_from_json
json_string = model.to_json()
model = model_from_json(json_string)
</code></pre>
<ul>
<li><code>model.to_yaml()</code> returns a representation of the model as a YAML string. Note that the representation does not include the weights, only the architecture. You can reinstantiate the same model (with reinitialized weights) from the YAML string via:</li>
</ul>
<pre><code class="python">from keras.models import model_from_yaml
yaml_string = model.to_yaml()
model = model_from_yaml(yaml_string)
</code></pre>
<ul>
<li><code>model.save_weights(filepath)</code> saves the weights of the model as a HDF5 file.</li>
<li><code>model.load_weights(filepath, by_name=False)</code> loads the weights of the model from a HDF5 file (created by <code>save_weights</code>). By default, the architecture is expected to be unchanged. To load weights into a different architecture (with some layers in common), use <code>by_name=True</code> to load only those layers with the same name.</li>
</ul>
<p>Note: Please also see <a href="/getting-started/faq/#how-can-i-install-HDF5-or-h5py-to-save-my-models-in-Keras">How can I install HDF5 or h5py to save my models in Keras?</a> in the FAQ for instructions on how to install <code>h5py</code>.</p>
<h2 id="model-subclassing">Model subclassing</h2>
<p>In addition to these two types of models, you may create your own fully-customizable models by subclassing the <code>Model</code> class
and implementing your own forward pass in the <code>call</code> method (the <code>Model</code> subclassing API was introduced in Keras 2.2.0).</p>
<p>Here's an example of a simple multi-layer perceptron model written as a <code>Model</code> subclass:</p>
<pre><code class="python">import keras
class SimpleMLP(keras.Model):
def __init__(self, use_bn=False, use_dp=False, num_classes=10):
super(SimpleMLP, self).__init__(name='mlp')
self.use_bn = use_bn
self.use_dp = use_dp
self.num_classes = num_classes
self.dense1 = keras.layers.Dense(32, activation='relu')
self.dense2 = keras.layers.Dense(num_classes, activation='softmax')
if self.use_dp:
self.dp = keras.layers.Dropout(0.5)
if self.use_bn:
self.bn = keras.layers.BatchNormalization(axis=-1)
def call(self, inputs):
x = self.dense1(inputs)
if self.use_dp:
x = self.dp(x)
if self.use_bn:
x = self.bn(x)
return self.dense2(x)
model = SimpleMLP()
model.compile(...)
model.fit(...)
</code></pre>
<p>Layers are defined in <code>__init__(self, ...)</code>, and the forward pass is specified in <code>call(self, inputs)</code>. In <code>call</code>, you may specify custom losses by calling <code>self.add_loss(loss_tensor)</code> (like you would in a custom layer).</p>
<p>In subclassed models, the model's topology is defined as Python code (rather than as a static graph of layers).
That means the model's topology cannot be inspected or serialized. As a result, the following methods and attributes are <strong>not available for subclassed models</strong>:</p>
<ul>
<li><code>model.inputs</code> and <code>model.outputs</code>.</li>
<li><code>model.to_yaml()</code> and <code>model.to_json()</code></li>
<li><code>model.get_config()</code> and <code>model.save()</code>.</li>
</ul>
<p><strong>Key point:</strong> use the right API for the job. The <code>Model</code> subclassing API can provide you with greater flexbility for implementing complex models,
but it comes at a cost (in addition to these missing features):
it is more verbose, more complex, and has more opportunities for user errors. If possible, prefer using the functional API, which is more user-friendly.</p>
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