Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Keras
Build, train, and debug deep learning models with Keras patterns, layer recipes, and training diagnostics.
skill openclawclawhub Free
Build, train, and debug deep learning models with Keras patterns, layer recipes, and training diagnostics.
β¬ 0 downloads β
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, architectures.md, layers.md, memory-template.md, setup.md, training.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- ivangdavila
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Setup
On first use, check setup.md for integration guidelines. The skill stores preferences in ~/keras/ when the user confirms.
When to Use
User builds neural networks with Keras or TensorFlow. Agent handles model architecture, layer configuration, training loops, callbacks, debugging loss issues, and deployment preparation.
Architecture
Memory lives in ~/keras/. See memory-template.md for setup. ~/keras/ βββ memory.md # Preferred architectures, hyperparams βββ models/ # Saved model configs (optional)
Quick Reference
TopicFileSetup processsetup.mdMemory templatememory-template.mdLayer patternslayers.mdTraining diagnosticstraining.mdCommon architecturesarchitectures.md
1. Sequential vs Functional API
Sequential: simple stacks, no branching Functional: multi-input/output, skip connections, shared layers Subclassing: custom forward pass, dynamic architectures # Sequential - simple stack model = keras.Sequential([ layers.Dense(64, activation='relu'), layers.Dense(10, activation='softmax') ]) # Functional - flexible graphs inputs = keras.Input(shape=(784,)) x = layers.Dense(64, activation='relu')(inputs) outputs = layers.Dense(10, activation='softmax')(x) model = keras.Model(inputs, outputs)
2. Input Shape Patterns
First layer needs input_shape (exclude batch) Images: (height, width, channels) for channels_last Sequences: (timesteps, features) Tabular: (features,) # Image input layers.Conv2D(32, 3, input_shape=(224, 224, 3)) # Sequence input layers.LSTM(64, input_shape=(100, 50)) # 100 timesteps, 50 features # Tabular input layers.Dense(64, input_shape=(20,)) # 20 features
3. Activation Functions
TaskOutput ActivationLossBinary classificationsigmoidbinary_crossentropyMulti-classsoftmaxcategorical_crossentropyMulti-labelsigmoidbinary_crossentropyRegressionlinear (none)mse or mae
4. Regularization Stack
Apply in this order for overfitting: Dropout - after dense/conv layers (0.2-0.5) BatchNorm - before or after activation L2 regularization - in layer (0.01-0.001) Early stopping - callback with patience layers.Dense(64, activation='relu', kernel_regularizer=keras.regularizers.l2(0.01)) layers.Dropout(0.3) layers.BatchNormalization()
5. Callbacks Essentials
callbacks = [ keras.callbacks.EarlyStopping( monitor='val_loss', patience=5, restore_best_weights=True ), keras.callbacks.ModelCheckpoint( 'best_model.keras', save_best_only=True ), keras.callbacks.ReduceLROnPlateau( monitor='val_loss', factor=0.5, patience=3 ), keras.callbacks.TensorBoard(log_dir='./logs') ]
6. Data Pipeline
# tf.data for performance dataset = tf.data.Dataset.from_tensor_slices((x, y)) dataset = dataset.shuffle(10000).batch(32).prefetch(tf.data.AUTOTUNE) # ImageDataGenerator for augmentation datagen = keras.preprocessing.image.ImageDataGenerator( rotation_range=20, horizontal_flip=True, validation_split=0.2 )
7. Compile Checklist
model.compile( optimizer=keras.optimizers.Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'] ) Learning rate: start 0.001, reduce on plateau Batch size: 32-128 typical, larger = smoother gradients
Common Traps
Input shape mismatch β check data shape vs model input_shape, exclude batch dim Loss is NaN β reduce learning rate, check for inf/nan in data, add gradient clipping Validation loss diverges β add regularization, reduce model capacity, more data Model not learning β check labels are correct, verify loss function matches task GPU OOM β reduce batch size, use mixed precision, gradient checkpointing Slow training β use tf.data pipeline with prefetch, enable XLA compilation
External Endpoints
EndpointData SentPurposeTensorFlow model hubNone (download only)Pretrained weights when using weights='imagenet' Note: Transfer learning examples download pretrained weights on first use. Use weights=None for fully offline operation.
Security & Privacy
Data that stays local: Model architectures and configs in ~/keras/ Training preferences and hyperparameters This skill does NOT: Upload models or data anywhere Access files outside ~/keras/ and working directory Store training data
Related Skills
Install with clawhub install <slug> if user confirms: tensorflow β TensorFlow operations and deployment pytorch β Alternative deep learning framework ai β General AI and ML patterns models β Model architecture design
Feedback
If useful: clawhub star keras Stay updated: clawhub sync
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
6 Docs
- SKILL.md
- architectures.md
- layers.md
- memory-template.md
- setup.md
- training.md