HyperParameters

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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/keras_tuner/'>KerasTuner</a> / HyperParameters </div> <div class='k-content'> <h1 id="hyperparameters">HyperParameters</h1> <a href="https://github.com/keras-team/keras-tuner/tree/v1.4.7/keras_tuner/src/engine/hyperparameters/hyperparameters.py#L29">[source]</a> <h3 id="hyperparameters-class"><code>HyperParameters</code> class</h3> <div class="codehilite"><pre><code>keras_tuner.HyperParameters() </code></pre></div> Container for both a hyperparameter space, and current values. A <code>HyperParameters</code> instance can be pass to <code>HyperModel.build(hp)</code> as an argument to build a model. To prevent the users from depending on inactive hyperparameter values, only active hyperparameters should have values in <code>HyperParameters.values</code>. Attributes <ul> <li>space: A list of <code>HyperParameter</code> objects.</li> <li>values: A dict mapping hyperparameter names to current values.</li> </ul> <hr /> <a href="https://github.com/keras-team/keras-tuner/tree/v1.4.7/keras_tuner/src/engine/hyperparameters/hyperparameters.py#L496">[source]</a> <h3 id="boolean-method"><code>Boolean</code> method</h3> <div class="codehilite"><pre><code>HyperParameters.Boolean(name, default=False, parent_name=None, parent_values=None) </code></pre></div> Choice between True and False. Arguments <ul> <li>name: A string. the name of parameter. Must be unique for each <code>HyperParameter</code> instance in the search space.</li> <li>default: Boolean, the default value to return for the parameter. If unspecified, the default value will be False.</li> <li>parent_name: Optional string, specifying the name of the parent <code>HyperParameter</code> to use as the condition to activate the current <code>HyperParameter</code>.</li> <li>parent_values: Optional list of the values of the parent <code>HyperParameter</code> to use as the condition to activate the current <code>HyperParameter</code>.</li> </ul> Returns The value of the hyperparameter, or None if the hyperparameter is not active. <hr /> <a href="https://github.com/keras-team/keras-tuner/tree/v1.4.7/keras_tuner/src/engine/hyperparameters/hyperparameters.py#L258">[source]</a> <h3 id="choice-method"><code>Choice</code> method</h3> <div class="codehilite"><pre><code>HyperParameters.Choice( name, values, ordered=None, default=None, parent_name=None, parent_values=None ) </code></pre></div> Choice of one value among a predefined set of possible values. Arguments <ul> <li>name: A string. the name of parameter. Must be unique for each <code>HyperParameter</code> instance in the search space.</li> <li>values: A list of possible values. Values must be int, float, str, or bool. All values must be of the same type.</li> <li>ordered: Optional boolean, whether the values passed should be considered to have an ordering. Defaults to <code>True</code> for float/int values. Must be <code>False</code> for any other values.</li> <li>default: Optional default value to return for the parameter. If unspecified, the default value will be:<ul> <li>None if None is one of the choices in <code>values</code></li> <li>The first entry in <code>values</code> otherwise.</li> </ul> </li> <li>parent_name: Optional string, specifying the name of the parent <code>HyperParameter</code> to use as the condition to activate the current <code>HyperParameter</code>.</li> <li>parent_values: Optional list of the values of the parent <code>HyperParameter</code> to use as the condition to activate the current <code>HyperParameter</code>.</li> </ul> Returns The value of the hyperparameter, or None if the hyperparameter is not active. <hr /> <a href="https://github.com/keras-team/keras-tuner/tree/v1.4.7/keras_tuner/src/engine/hyperparameters/hyperparameters.py#L525">[source]</a> <h3 id="fixed-method"><code>Fixed</code> method</h3> <div class="codehilite"><pre><code>HyperParameters.Fixed(name, value, parent_name=None, parent_values=None) </code></pre></div> Fixed, untunable value. Arguments <ul> <li>name: A string. the name of parameter. Must be unique for each <code>HyperParameter</code> instance in the search space.</li> <li>value: The value to use (can be any JSON-serializable Python type).</li> <li>parent_name: Optional string, specifying the name of the parent <code>HyperParameter</code> to use as the condition to activate the current <code>HyperParameter</code>.</li> <li>parent_values: Optional list of the values of the parent <code>HyperParameter</code> to use as the condition to activate the current <code>HyperParameter</code>.</li> </ul> Returns The value of the hyperparameter, or None if the hyperparameter is not active. <hr /> <a href="https://github.com/keras-team/keras-tuner/tree/v1.4.7/keras_tuner/src/engine/hyperparameters/hyperparameters.py#L401">[source]</a> <h3 id="float-method"><code>Float</code> method</h3> <div class="codehilite"><pre><code>HyperParameters.Float( name, min_value, max_value, step=None, sampling="linear", default=None, parent_name=None, parent_values=None, ) </code></pre></div> Floating point value hyperparameter. Example #1: <div class="codehilite"><pre><code>hp.Float( "image_rotation_factor", min_value=0, max_value=1) </code></pre></div> All values in interval [0, 1] have equal probability of being sampled. Example #2: <div class="codehilite"><pre><code>hp.Float( "image_rotation_factor", min_value=0, max_value=1, step=0.2) </code></pre></div> <code>step</code> is the minimum distance between samples. The possible values are [0, 0.2, 0.4, 0.6, 0.8, 1.0]. Example #3: <div class="codehilite"><pre><code>hp.Float( "learning_rate", min_value=0.001, max_value=10, step=10, sampling="log") </code></pre></div> When <code>sampling="log"</code>, the <code>step</code> is multiplied between samples. The possible values are [0.001, 0.01, 0.1, 1, 10]. Arguments <ul> <li>name: A string. the name of parameter. Must be unique for each <code>HyperParameter</code> instance in the search space.</li> <li>min_value: Float, the lower bound of the range.</li> <li>max_value: Float, the upper bound of the range.</li> <li>step: Optional float, the distance between two consecutive samples in the range. If left unspecified, it is possible to sample any value in the interval. If <code>sampling="linear"</code>, it will be the minimum additve between two samples. If <code>sampling="log"</code>, it will be the minimum multiplier between two samples.</li> <li>sampling: String. One of "linear", "log", "reverse_log". Defaults to "linear". When sampling value, it always start from a value in range [0.0, 1.0). The <code>sampling</code> argument decides how the value is projected into the range of [min_value, max_value]. "linear": min_value + value * (max_value - min_value) "log": min_value * (max_value / min_value) ^ value "reverse_log": (max_value - min_value * ((max_value / min_value) ^ (1 - value) - 1))</li> <li>default: Float, the default value to return for the parameter. If unspecified, the default value will be <code>min_value</code>.</li> <li>parent_name: Optional string, specifying the name of the parent <code>HyperParameter</code> to use as the condition to activate the current <code>HyperParameter</code>.</li> <li>parent_values: Optional list of the values of the parent <code>HyperParameter</code> to use as the condition to activate the current <code>HyperParameter</code>.</li> </ul> Returns The value of the hyperparameter, or None if the hyperparameter is not active. <hr /> <a href="https://github.com/keras-team/keras-tuner/tree/v1.4.7/keras_tuner/src/engine/hyperparameters/hyperparameters.py#L302">[source]</a> <h3 id="int-method"><code>Int</code> method</h3> <div class="codehilite"><pre><code>HyperParameters.Int( name, min_value, max_value, step=None, sampling="linear", default=None, parent_name=None, parent_values=None, ) </code></pre></div> Integer hyperparameter. Note that unlike Python's <code>range</code> function, <code>max_value</code> is included in the possible values this parameter can take on. Example #1: <div class="codehilite"><pre><code>hp.Int( "n_layers", min_value=6, max_value=12) </code></pre></div> The possible values are [6, 7, 8, 9, 10, 11, 12]. Example #2: <div class="codehilite"><pre><code>hp.Int( "n_layers", min_value=6, max_value=13, step=3) </code></pre></div> <code>step</code> is the minimum distance between samples. The possible values are [6, 9, 12]. Example #3: <div class="codehilite"><pre><code>hp.Int( "batch_size", min_value=2, max_value=32, step=2, sampling="log") </code></pre></div> When <code>sampling="log"</code> the <code>step</code> is multiplied between samples. The possible values are [2, 4, 8, 16, 32]. Arguments <ul> <li>name: A string. the name of parameter. Must be unique for each <code>HyperParameter</code> instance in the search space.</li> <li>min_value: Integer, the lower limit of range, inclusive.</li> <li>max_value: Integer, the upper limit of range, inclusive.</li> <li>step: Optional integer, the distance between two consecutive samples in the range. If left unspecified, it is possible to sample any integers in the interval. If <code>sampling="linear"</code>, it will be the minimum additve between two samples. If <code>sampling="log"</code>, it will be the minimum multiplier between two samples.</li> <li>sampling: String. One of "linear", "log", "reverse_log". Defaults to "linear". When sampling value, it always start from a value in range [0.0, 1.0). The <code>sampling</code> argument decides how the value is projected into the range of [min_value, max_value]. "linear": min_value + value * (max_value - min_value) "log": min_value * (max_value / min_value) ^ value "reverse_log": (max_value - min_value * ((max_value / min_value) ^ (1 - value) - 1))</li> <li>default: Integer, default value to return for the parameter. If unspecified, the default value will be <code>min_value</code>.</li> <li>parent_name: Optional string, specifying the name of the parent <code>HyperParameter</code> to use as the condition to activate the current <code>HyperParameter</code>.</li> <li>parent_values: Optional list of the values of the parent <code>HyperParameter</code> to use as the condition to activate the current <code>HyperParameter</code>.</li> </ul> Returns The value of the hyperparameter, or None if the hyperparameter is not active. <hr /> <a href="https://github.com/keras-team/keras-tuner/tree/v1.4.7/keras_tuner/src/engine/hyperparameters/hyperparameters.py#L87">[source]</a> <h3 id="conditionalscope-method"><code>conditional_scope</code> method</h3> <div class="codehilite"><pre><code>HyperParameters.conditional_scope(parent_name, parent_values) </code></pre></div> Opens a scope to create conditional HyperParameters. All <code>HyperParameter</code>s created under this scope will only be active when the parent <code>HyperParameter</code> specified by <code>parent_name</code> is equal to one of the values passed in <code>parent_values</code>. When the condition is not met, creating a <code>HyperParameter</code> under this scope will register the <code>HyperParameter</code>, but will return <code>None</code> rather than a concrete value. Note that any Python code under this scope will execute regardless of whether the condition is met. This feature is for the <code>Tuner</code> to collect more information of the search space and the current trial. It is especially useful for model selection. If the parent <code>HyperParameter</code> is for model selection, the <code>HyperParameter</code>s in a model should only be active when the model selected, which can be implemented using <code>conditional_scope</code>. Examples <div class="codehilite"><pre><code>def MyHyperModel(HyperModel): def build(self, hp): model = Sequential() model.add(Input(shape=(32, 32, 3))) model_type = hp.Choice("model_type", ["mlp", "cnn"]) with hp.conditional_scope("model_type", ["mlp"]): if model_type == "mlp": model.add(Flatten()) model.add(Dense(32, activation='relu')) with hp.conditional_scope("model_type", ["cnn"]): if model_type == "cnn": model.add(Conv2D(64, 3, activation='relu')) model.add(GlobalAveragePooling2D()) model.add(Dense(10, activation='softmax')) return model </code></pre></div> Arguments <ul> <li>parent_name: A string, specifying the name of the parent <code>HyperParameter</code> to use as the condition to activate the current <code>HyperParameter</code>.</li> <li>parent_values: A list of the values of the parent <code>HyperParameter</code> to use as the condition to activate the current <code>HyperParameter</code>.</li> </ul> <hr /> <a href="https://github.com/keras-team/keras-tuner/tree/v1.4.7/keras_tuner/src/engine/hyperparameters/hyperparameters.py#L238">[source]</a> <h3 id="get-method"><code>get</code> method</h3> <div class="codehilite"><pre><code>HyperParameters.get(name) </code></pre></div> Return the current value of this hyperparameter set. <hr /> </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#hyperparameters'>HyperParameters</a> </div> <div class='k-outline-depth-3'> <a href='#hyperparameters-class'><code>HyperParameters</code> class</a> </div> <div class='k-outline-depth-3'> <a href='#boolean-method'><code>Boolean</code> method</a> </div> <div class='k-outline-depth-3'> <a href='#choice-method'><code>Choice</code> method</a> </div> <div class='k-outline-depth-3'> <a href='#fixed-method'><code>Fixed</code> method</a> </div> <div class='k-outline-depth-3'> <a href='#float-method'><code>Float</code> method</a> </div> <div class='k-outline-depth-3'> <a href='#int-method'><code>Int</code> method</a> </div> <div class='k-outline-depth-3'> <a href='#conditionalscope-method'><code>conditional_scope</code> method</a> </div> <div class='k-outline-depth-3'> <a href='#get-method'><code>get</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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HyperParameters