Customizing what happens in `fit()` with JAX

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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='/guides/'>Developer guides</a> / Customizing what happens in `fit()` with JAX </div> <div class='k-content'> <h1 id="customizing-what-happens-in-fit-with-jax">Customizing what happens in <code>fit()</code> with JAX</h1> Author: <a href="https://twitter.com/fchollet">fchollet</a> Date created: 2023/06/27 Last modified: 2023/06/27 Description: Overriding the training step of the Model class with JAX. <img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> <a href="https://colab.research.google.com/github/keras-team/keras-io/blob/master/guides/ipynb/custom_train_step_in_jax.ipynb">View in Colab</a> •<img class="k-inline-icon" src="https://github.com/favicon.ico"/> <a href="https://github.com/keras-team/keras-io/blob/master/guides/custom_train_step_in_jax.py">GitHub source</a> <hr /> <h2 id="introduction">Introduction</h2> When you're doing supervised learning, you can use <code>fit()</code> and everything works smoothly. When you need to take control of every little detail, you can write your own training loop entirely from scratch. But what if you need a custom training algorithm, but you still want to benefit from the convenient features of <code>fit()</code>, such as callbacks, built-in distribution support, or step fusing? A core principle of Keras is progressive disclosure of complexity. You should always be able to get into lower-level workflows in a gradual way. You shouldn't fall off a cliff if the high-level functionality doesn't exactly match your use case. You should be able to gain more control over the small details while retaining a commensurate amount of high-level convenience. When you need to customize what <code>fit()</code> does, you should override the training step function of the <code>Model</code> class. This is the function that is called by <code>fit()</code> for every batch of data. You will then be able to call <code>fit()</code> as usual – and it will be running your own learning algorithm. Note that this pattern does not prevent you from building models with the Functional API. You can do this whether you're building <code>Sequential</code> models, Functional API models, or subclassed models. Let's see how that works. <hr /> <h2 id="setup">Setup</h2> <div class="codehilite"><pre><code>import os # This guide can only be run with the JAX backend. os.environ["KERAS_BACKEND"] = "jax" import jax import keras import numpy as np </code></pre></div> <hr /> <h2 id="a-first-simple-example">A first simple example</h2> Let's start from a simple example: <ul> <li>We create a new class that subclasses <a href="/api/models/model#model-class"><code>keras.Model</code></a>.</li> <li>We implement a fully-stateless <code>compute_loss_and_updates()</code> method to compute the loss as well as the updated values for the non-trainable variables of the model. Internally, it calls <code>stateless_call()</code> and the built-in <code>compute_loss()</code>.</li> <li>We implement a fully-stateless <code>train_step()</code> method to compute current metric values (including the loss) as well as updated values for the trainable variables, the optimizer variables, and the metric variables.</li> </ul> Note that you can also take into account the <code>sample_weight</code> argument by: <ul> <li>Unpacking the data as <code>x, y, sample_weight = data</code></li> <li>Passing <code>sample_weight</code> to <code>compute_loss()</code></li> <li>Passing <code>sample_weight</code> alongside <code>y</code> and <code>y_pred</code> to metrics in <code>stateless_update_state()</code></li> </ul> <div class="codehilite"><pre><code>class CustomModel(keras.Model): def compute_loss_and_updates( self, trainable_variables, non_trainable_variables, x, y, training=False, ): y_pred, non_trainable_variables = self.stateless_call( trainable_variables, non_trainable_variables, x, training=training, ) loss = self.compute_loss(x, y, y_pred) return loss, (y_pred, non_trainable_variables) def train_step(self, state, data): ( trainable_variables, non_trainable_variables, optimizer_variables, metrics_variables, ) = state x, y = data # Get the gradient function. grad_fn = jax.value_and_grad(self.compute_loss_and_updates, has_aux=True) # Compute the gradients. (loss, (y_pred, non_trainable_variables)), grads = grad_fn( trainable_variables, non_trainable_variables, x, y, training=True, ) # Update trainable variables and optimizer variables. ( trainable_variables, optimizer_variables, ) = self.optimizer.stateless_apply( optimizer_variables, grads, trainable_variables ) # Update metrics. new_metrics_vars = [] logs = {} for metric in self.metrics: this_metric_vars = metrics_variables[ len(new_metrics_vars) : len(new_metrics_vars) + len(metric.variables) ] if metric.name == "loss": this_metric_vars = metric.stateless_update_state(this_metric_vars, loss) else: this_metric_vars = metric.stateless_update_state( this_metric_vars, y, y_pred ) logs[metric.name] = metric.stateless_result(this_metric_vars) new_metrics_vars += this_metric_vars # Return metric logs and updated state variables. state = ( trainable_variables, non_trainable_variables, optimizer_variables, new_metrics_vars, ) return logs, state </code></pre></div> Let's try this out: <div class="codehilite"><pre><code># Construct and compile an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(optimizer="adam", loss="mse", metrics=["mae"]) # Just use `fit` as usual x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=3) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 1/3 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - mae: 1.0022 - loss: 1.2464 Epoch 2/3 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 198us/step - mae: 0.5811 - loss: 0.4912 Epoch 3/3 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 231us/step - mae: 0.4386 - loss: 0.2905 <keras.src.callbacks.history.History at 0x14da599c0> </code></pre></div> </div> <hr /> <h2 id="going-lowerlevel">Going lower-level</h2> Naturally, you could just skip passing a loss function in <code>compile()</code>, and instead do everything manually in <code>train_step</code>. Likewise for metrics. Here's a lower-level example, that only uses <code>compile()</code> to configure the optimizer: <div class="codehilite"><pre><code>class CustomModel(keras.Model): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.loss_tracker = keras.metrics.Mean(name="loss") self.mae_metric = keras.metrics.MeanAbsoluteError(name="mae") self.loss_fn = keras.losses.MeanSquaredError() def compute_loss_and_updates( self, trainable_variables, non_trainable_variables, x, y, training=False, ): y_pred, non_trainable_variables = self.stateless_call( trainable_variables, non_trainable_variables, x, training=training, ) loss = self.loss_fn(y, y_pred) return loss, (y_pred, non_trainable_variables) def train_step(self, state, data): ( trainable_variables, non_trainable_variables, optimizer_variables, metrics_variables, ) = state x, y = data # Get the gradient function. grad_fn = jax.value_and_grad(self.compute_loss_and_updates, has_aux=True) # Compute the gradients. (loss, (y_pred, non_trainable_variables)), grads = grad_fn( trainable_variables, non_trainable_variables, x, y, training=True, ) # Update trainable variables and optimizer variables. ( trainable_variables, optimizer_variables, ) = self.optimizer.stateless_apply( optimizer_variables, grads, trainable_variables ) # Update metrics. loss_tracker_vars = metrics_variables[: len(self.loss_tracker.variables)] mae_metric_vars = metrics_variables[len(self.loss_tracker.variables) :] loss_tracker_vars = self.loss_tracker.stateless_update_state( loss_tracker_vars, loss ) mae_metric_vars = self.mae_metric.stateless_update_state( mae_metric_vars, y, y_pred ) logs = {} logs[self.loss_tracker.name] = self.loss_tracker.stateless_result( loss_tracker_vars ) logs[self.mae_metric.name] = self.mae_metric.stateless_result(mae_metric_vars) new_metrics_vars = loss_tracker_vars + mae_metric_vars # Return metric logs and updated state variables. state = ( trainable_variables, non_trainable_variables, optimizer_variables, new_metrics_vars, ) return logs, state @property def metrics(self): # We list our `Metric` objects here so that `reset_states()` can be # called automatically at the start of each epoch # or at the start of `evaluate()`. return [self.loss_tracker, self.mae_metric] # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) # We don't pass a loss or metrics here. model.compile(optimizer="adam") # Just use `fit` as usual -- you can use callbacks, etc. x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=5) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 1/5 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.6085 - mae: 0.6580 Epoch 2/5 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 215us/step - loss: 0.2630 - mae: 0.4141 Epoch 3/5 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 202us/step - loss: 0.2271 - mae: 0.3835 Epoch 4/5 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 192us/step - loss: 0.2093 - mae: 0.3714 Epoch 5/5 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 194us/step - loss: 0.2188 - mae: 0.3818 <keras.src.callbacks.history.History at 0x14de01420> </code></pre></div> </div> <hr /> <h2 id="providing-your-own-evaluation-step">Providing your own evaluation step</h2> What if you want to do the same for calls to <code>model.evaluate()</code>? Then you would override <code>test_step</code> in exactly the same way. Here's what it looks like: <div class="codehilite"><pre><code>class CustomModel(keras.Model): def test_step(self, state, data): # Unpack the data. x, y = data ( trainable_variables, non_trainable_variables, metrics_variables, ) = state # Compute predictions and loss. y_pred, non_trainable_variables = self.stateless_call( trainable_variables, non_trainable_variables, x, training=False, ) loss = self.compute_loss(x, y, y_pred) # Update metrics. new_metrics_vars = [] for metric in self.metrics: this_metric_vars = metrics_variables[ len(new_metrics_vars) : len(new_metrics_vars) + len(metric.variables) ] if metric.name == "loss": this_metric_vars = metric.stateless_update_state(this_metric_vars, loss) else: this_metric_vars = metric.stateless_update_state( this_metric_vars, y, y_pred ) logs = metric.stateless_result(this_metric_vars) new_metrics_vars += this_metric_vars # Return metric logs and updated state variables. state = ( trainable_variables, non_trainable_variables, new_metrics_vars, ) return logs, state # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(loss="mse", metrics=["mae"]) # Evaluate with our custom test_step x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.evaluate(x, y) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code> 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 973us/step - mae: 0.7887 - loss: 0.8385 [0.8385222554206848, 0.7956181168556213] </code></pre></div> </div> That's it! </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#customizing-what-happens-in-fit-with-jax'>Customizing what happens in <code>fit()</code> with JAX</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#introduction'>Introduction</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#setup'>Setup</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#a-first-simple-example'>A first simple example</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#going-lowerlevel'>Going lower-level</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#providing-your-own-evaluation-step'>Providing your own evaluation step</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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Customizing what happens in `fit()` with JAX