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<h2 id="usage-of-metrics">Usage of metrics</h2>
<p>A metric is a function that is used to judge the performance of your model. Metric functions are to be supplied in the <code>metrics</code> parameter when a model is compiled. </p>
<pre><code class="python">model.compile(loss='mean_squared_error',
optimizer='sgd',
metrics=['mae', 'acc'])
</code></pre>
<pre><code class="python">from keras import metrics
model.compile(loss='mean_squared_error',
optimizer='sgd',
metrics=[metrics.mae, metrics.categorical_accuracy])
</code></pre>
<p>A metric function is similar to a <a href="/losses">loss function</a>, except that the results from evaluating a metric are not used when training the model. You may use any of the loss functions as a metric function.</p>
<p>You can either pass the name of an existing metric, or pass a Theano/TensorFlow symbolic function (see <a href="#custom-metrics">Custom metrics</a>).</p>
<h4 id="arguments">Arguments</h4>
<ul>
<li><strong>y_true</strong>: True labels. Theano/TensorFlow tensor.</li>
<li><strong>y_pred</strong>: Predictions. Theano/TensorFlow tensor of the same shape as y_true.</li>
</ul>
<h4 id="returns">Returns</h4>
<p>Single tensor value representing the mean of the output array across all
datapoints.</p>
<hr />
<h2 id="available-metrics">Available metrics</h2>
<h3 id="accuracy">accuracy</h3>
<pre><code class="python">keras.metrics.accuracy(y_true, y_pred)
</code></pre>
<hr />
<h3 id="binary_accuracy">binary_accuracy</h3>
<pre><code class="python">keras.metrics.binary_accuracy(y_true, y_pred, threshold=0.5)
</code></pre>
<hr />
<h3 id="categorical_accuracy">categorical_accuracy</h3>
<pre><code class="python">keras.metrics.categorical_accuracy(y_true, y_pred)
</code></pre>
<hr />
<h3 id="sparse_categorical_accuracy">sparse_categorical_accuracy</h3>
<pre><code class="python">keras.metrics.sparse_categorical_accuracy(y_true, y_pred)
</code></pre>
<hr />
<h3 id="top_k_categorical_accuracy">top_k_categorical_accuracy</h3>
<pre><code class="python">keras.metrics.top_k_categorical_accuracy(y_true, y_pred, k=5)
</code></pre>
<hr />
<h3 id="sparse_top_k_categorical_accuracy">sparse_top_k_categorical_accuracy</h3>
<pre><code class="python">keras.metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=5)
</code></pre>
<hr />
<h3 id="cosine_proximity">cosine_proximity</h3>
<pre><code class="python">keras.metrics.cosine_proximity(y_true, y_pred, axis=-1)
</code></pre>
<hr />
<h3 id="clone_metric">clone_metric</h3>
<pre><code class="python">keras.metrics.clone_metric(metric)
</code></pre>
<h2 id="returns-a-clone-of-the-metric-if-stateful-otherwise-returns-it-as-is">Returns a clone of the metric if stateful, otherwise returns it as is.</h2>
<h3 id="clone_metrics">clone_metrics</h3>
<pre><code class="python">keras.metrics.clone_metrics(metrics)
</code></pre>
<p>Clones the given metric list/dict.</p>
<p>In addition to the metrics above, you may use any of the loss functions described in the <a href="/losses">loss function</a> page as metrics.</p>
<hr />
<h2 id="custom-metrics">Custom metrics</h2>
<p>Custom metrics can be passed at the compilation step. The
function would need to take <code>(y_true, y_pred)</code> as arguments and return
a single tensor value.</p>
<pre><code class="python">import keras.backend as K
def mean_pred(y_true, y_pred):
return K.mean(y_pred)
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy', mean_pred])
</code></pre>
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