Stay in the Loop: Experimentation and Iteration Workflows with TF 2#

Why Experimentation and Iteration Matter#

Model development is rarely a linear path from idea to success. Iteration is vital to refining insights, identifying pitfalls, and improving performance. In TF 2, you have multiple ways to rapidly experiment:

• High-Level Keras API: Quick prototyping, easy model construction, and off-the-shelf training methods.
• Custom Low-Level APIs: Using lower-level TensorFlow primitives for full control over model training.
• Rapid Debugging: With eager execution, you can check array shapes, slice data, and tweak the code quickly.

Eager Execution#

Eager execution evaluates operations immediately as they are called. Instead of building an abstract execution graph that only runs when session.run() is called (as in TF 1.x), TF 2 allows you to see results and debug right away:

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import tensorflow as tf
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# Eager execution is enabled by default in TF 2
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x = [[2.0]]
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m = tf.matmul(x, x)
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print("Result of matrix multiplication:", m)

This immediate execution style simplifies the code and feels much like standard Python.

Autograph#

Autograph is a system that allows TensorFlow to convert eager-executed Python code (like loops and conditionals) into an equivalent graph format under the hood. This means that even though we write code in a more Pythonic way, TensorFlow can still effectively leverage graph optimizations.

For instance, you can create a function with Python for loops and if statements, decorate it with @tf.function, and TensorFlow automatically transforms it into a graph:

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@tf.function
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def add_until_n(x, n):
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    while x < n:
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        x = x + 1
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    return x
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print(add_until_n(tf.constant(0), tf.constant(5)))  # Outputs 5

Eager Execution vs. Graph Execution#

TF 2 primarily operates in eager mode, though you can leverage graph mode optimizations underneath by using @tf.function.

Basic Model Definition#

A typical tf.keras workflow involves building a sequential model, compiling it, and then calling fit() on your training data:

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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras import layers
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model = keras.Sequential([
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    layers.Dense(16, activation='relu', input_shape=(4,)),
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    layers.Dense(8, activation='relu'),
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    layers.Dense(3, activation='softmax')
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])
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model.compile(optimizer='adam',
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              loss='sparse_categorical_crossentropy',
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              metrics=['accuracy'])
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# Example data
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import numpy as np
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x_train = np.random.random((1000, 4))
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y_train = np.random.randint(3, size=(1000,))
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model.fit(x_train, y_train, epochs=5, batch_size=32)

In just a few lines of code, you have built a neural network and run a training job. Achieving the same in TF 1.x would have involved more boilerplate for graph creation and session management.

Custom Layers and Models#

You can define your own layers or build more complex architectures using the Model subclassing feature:

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class MyModel(keras.Model):
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    def __init__(self):
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        super(MyModel, self).__init__()
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        self.dense1 = layers.Dense(16, activation='relu')
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        self.dense2 = layers.Dense(8, activation='relu')
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        self.dense3 = layers.Dense(3, activation='softmax')
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    def call(self, inputs):
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        x = self.dense1(inputs)
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        x = self.dense2(x)
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        return self.dense3(x)
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model = MyModel()
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model.compile(optimizer='adam',
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              loss='sparse_categorical_crossentropy',
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              metrics=['accuracy'])
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model.fit(x_train, y_train, epochs=5, batch_size=32)

By subclassing Model, you can create more sophisticated architectures while maintaining the ability to call standard Keras methods on them.

Dense (Fully Connected) Layers#

Used for tasks where the input features are vectors, typically within classification and regression contexts. In Keras, you specify the layer size and activation function:

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dense_layer = layers.Dense(32, activation='relu')

Convolutional Layers#

Commonly used for images and time-series data. Convolutional layers reduce computation and overfitting by exploiting spatial or temporal correlations.

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conv_layer = layers.Conv2D(64, kernel_size=(3, 3), activation='relu')

Recurrent Layers#

For sequential data tasks, such as natural language or time series forecasting. Keras offers multiple cell types, like LSTM or GRU:

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rnn_layer = layers.LSTM(64, return_sequences=False)

Attention Mechanisms#

Useful for capturing global context in sequences. TF 2 includes a variety of pre-built attention mechanisms you can incorporate, though advanced usage may require building custom layers.

Importance of Version Control#

In addition to this iteration cycle, it’s wise to keep your experiments organized:

• Use Git or another version control system to store your training scripts and model definitions.
• Store hyperparameters, evaluation metrics, and logs so you can systematically compare runs.

Logging and Checkpoints#

TensorFlow provides utilities like tf.train.Checkpoint and the Keras ModelCheckpoint callback for ensuring your model parameters are saved as you train. Additionally, the TensorBoard logging mechanism allows for easy visual analysis of metrics over time.

Example: Custom Training Loop#

Below is a simplified example of how to create your own training loop using tf.GradientTape:

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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras import layers
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import numpy as np
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# Example model
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model = keras.Sequential([
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    layers.Dense(16, activation='relu', input_shape=(4,)),
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    layers.Dense(3, activation='softmax')
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])
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# Sample data
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x_train = np.random.random((1000, 4)).astype(np.float32)
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y_train = np.random.randint(3, size=(1000,))
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# Learning rate and optimizer
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learning_rate = 0.001
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optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
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# Custom training loop
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def train_step(x, y):
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    with tf.GradientTape() as tape:
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        predictions = model(x, training=True)
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        loss = tf.keras.losses.sparse_categorical_crossentropy(y, predictions)
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        loss = tf.reduce_mean(loss)
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    grads = tape.gradient(loss, model.trainable_variables)
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    optimizer.apply_gradients(zip(grads, model.trainable_variables))
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    return loss
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# Training pipeline
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epochs = 5
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batch_size = 32
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num_batches = x_train.shape[0] // batch_size
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for epoch in range(epochs):
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    epoch_loss = 0.0
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    for i in range(num_batches):
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        batch_x = x_train[i*batch_size : (i+1)*batch_size]
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        batch_y = y_train[i*batch_size : (i+1)*batch_size]
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        loss_value = train_step(batch_x, batch_y)
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        epoch_loss += loss_value.numpy()
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    print(f"Epoch {epoch+1}, Loss: {epoch_loss / num_batches}")

This custom approach lets you:

• Inject specialized regularization far beyond simple weight decay or dropout.
• Experiment with advanced training schemes such as adversarial training, meta-learning, or multi-task.

Popular Callbacks#

Mixed and matched effectively, callbacks provide powerful guardrails and insights during training.

Debugging with Eager Execution#

Since TF 2 is eagerly executed, you can use standard Python debugging techniques (like print() statements, pdb or IDE-based debuggers). You can also quickly isolate shape inconsistencies:

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def debug_example(x):
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    print("Input shape:", x.shape)
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    # Insert your suspicious code here
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    return x
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# Example usage
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debugged_output = debug_example(tf.random.normal((16, 4)))

Profiling in TensorFlow#

Performance profiling can be done using the TensorBoard Profile tool:

1. Load TensorBoard:

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   tensorboard --logdir=/path/to/logs
   

2. In your code, add the tf.profiler.experimental or tf.keras.callbacks.TensorBoard callback to start capturing data.

This profiling data helps identify bottlenecks in CPU, GPU, or TPU usage so you can optimize training times.

Manual Tuning#

You can manually adjust parameters in your code and re-run experiments, though this approach gets cumbersome if you have many hyperparameters to test.

Grid Search and Random Search#

Popular classical techniques:

• Grid Search explores a grid of hyperparameter values in a systematic manner.
• Random Search picks random combinations of hyperparameters from a specified search space.

Bayesian Optimization#

A more advanced technique that builds a probability model of the objective function and uses that model to decide where to sample next. Packages like Keras Tuner integrate seamlessly with TF 2:

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import keras_tuner as kt
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def build_model(hp):
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    model = keras.Sequential()
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    model.add(layers.Dense(
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        units=hp.Int('units', min_value=16, max_value=128, step=16),
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        activation='relu',
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        input_shape=(4,)
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    ))
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    model.add(layers.Dense(3, activation='softmax'))
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    model.compile(
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        optimizer=keras.optimizers.Adam(
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            hp.Float('learning_rate', 1e-4, 1e-2, sampling='log')
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        ),
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        loss='sparse_categorical_crossentropy',
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        metrics=['accuracy']
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    )
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    return model
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tuner = kt.Hyperband(
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    build_model,
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    objective='val_accuracy',
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    max_epochs=10,
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    factor=3,
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    directory='my_dir',
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    project_name='helloworld'
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)
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tuner.search(x_train, y_train, epochs=10, validation_split=0.2)
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best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]
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print(best_hps.values)

The above snippet sets up a hyperparameter search using Keras Tuner’s Hyperband approach. You specify a search space for the number of neurons in the first Dense layer and the learning rate. The tuner then searches through the space trying to maximize val_accuracy.

SavedModel Format and Serving#

TensorFlow’s recommended export format is the SavedModel. It encapsulates the model architecture alongside the weights, allowing you to load and serve it effectively:

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model.save("path/to/saved_model")
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reloaded_model = tf.keras.models.load_model("path/to/saved_model")

TensorFlow Extended (TFX)#

TFX is an end-to-end platform for deploying ML pipelines. It encompasses data validation, model analysis, and model serving. Typically, you break down the pipeline into modular components (e.g., data ingestion, training, validation).

Distributed Training#

Scaling up training to multiple GPUs or distributed clusters is often vital for large datasets. TensorFlow’s tf.distribute API allows you to define a strategy, wrap your model creation and training steps, and seamlessly scale out.

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strategy = tf.distribute.MirroredStrategy()
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with strategy.scope():
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    model = build_model(...)
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    model.compile(...)
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model.fit(...)

Model Optimization#

When deploying to mobile or edge devices, reduce the model size and latency via pruning, quantization, or weight clustering. Tools like tfmot (TensorFlow Model Optimization Toolkit) help compress and accelerate inference.