Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
TensorFlow
Avoid common TensorFlow mistakes — tf.function retracing, GPU memory, data pipeline bottlenecks, and gradient traps.
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Avoid common TensorFlow mistakes — tf.function retracing, GPU memory, data pipeline bottlenecks, and gradient traps.
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Install for OpenClaw
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Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- ivangdavila
- Source page
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Documentation
tf.function Retracing
New input shape/dtype causes retrace — expensive, prints warning Use input_signature for fixed shapes — @tf.function(input_signature=[tf.TensorSpec(...)]) Python values retrace — pass as tensors, not Python ints/floats Avoid Python side effects in tf.function — only runs once during tracing
GPU Memory
TensorFlow grabs all GPU memory by default — set memory_growth=True before any ops tf.config.experimental.set_memory_growth(gpu, True) — must be called before GPU init OOM with large models — reduce batch size or use gradient checkpointing CUDA_VISIBLE_DEVICES="" to force CPU — for testing without GPU
Data Pipeline
tf.data.Dataset without .prefetch() — CPU/GPU idle time between batches .cache() after expensive ops — but before random augmentation .batch() before .map() for vectorized ops — faster than per-element num_parallel_calls=tf.data.AUTOTUNE — parallel preprocessing Dataset iteration in eager mode is slow — use in tf.function or model.fit
Shape Issues
First dimension is batch — None for variable batch size in Input layer model.build(input_shape) if not using Input layer — or first call errors Reshape errors unclear — tf.debugging.assert_shapes() for debugging Broadcasting silently succeeds — may hide shape bugs
Gradient Tape
Variables watched by default — tensors need tape.watch(tensor) persistent=True for multiple gradients — otherwise tape consumed after first use tape.gradient returns None if no path — check for disconnected graph @tf.custom_gradient for custom backward — not all ops have gradients
Training Gotchas
model.trainable = False after compile does nothing — set before compile BatchNorm behaves differently in training vs inference — training=True/False matters model.fit shuffles by default — shuffle=False for time series validation_split takes from end — shuffle data first if order matters
Saving Models
model.save() saves everything — architecture, weights, optimizer state model.save_weights() only weights — need model code to restore SavedModel format for serving — tf.saved_model.save(model, path) H5 format limited — doesn't save custom objects well, use SavedModel
Common Mistakes
Mixing Keras and raw tf ops incorrectly — use layers.Lambda to wrap tf ops in Sequential tf.print vs Python print — Python print only runs at trace time in tf.function NumPy ops in graph — use tf ops, numpy executes eagerly only Loss returns scalar per sample — Keras averages, custom loops may need tf.reduce_mean
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
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Package contents
Included in package
1 Docs
- SKILL.md