Keras — Unpinned Version Causes 3-5% Accuracy Variation

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# io.thecodeforge: Defining a simple Sequential model
def build_forge_model():
    model = keras.Sequential([
        # A dense layer with 64 units and ReLU activation
        layers.Dense(64, activation='relu', input_shape=(32,)),
        # Output layer for binary classification
        layers.Dense(1, activation='sigmoid')
    ])

    # Compilation configures the learning process
    model.compile(
        optimizer='adam',
        loss='binary_crossentropy',
        metrics=['accuracy']
    )
    return model

if __name__ == '__main__':
    forge_model = build_forge_model()
    forge_model.summary()

# io.thecodeforge: Production DL Environment
FROM tensorflow/tensorflow:2.15.0-gpu

WORKDIR /app

# Standardized library requirements
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

# Run the Keras model training service
CMD ["python", "keras_basics.py"]

-- io.thecodeforge: Schema for model performance tracking
CREATE TABLE IF NOT EXISTS io.thecodeforge.model_logs (
    log_id SERIAL PRIMARY KEY,
    model_name VARCHAR(255) NOT NULL,
    optimizer_type VARCHAR(50),
    final_accuracy DECIMAL(5,4),
    final_loss DECIMAL(5,4),
    deployment_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- Example: Logging a Keras training run
INSERT INTO io.thecodeforge.model_logs (model_name, optimizer_type, final_accuracy, final_loss)
VALUES ('Sequential_V1', 'adam', 0.9821, 0.0412);

# io.thecodeforge: Common Pitfall - Activation/Loss Mismatch
# WRONG: Using sigmoid with categorical_crossentropy for multi-class
# model.add(layers.Dense(10, activation='sigmoid'))
# model.compile(loss='categorical_crossentropy')

# CORRECT: Use softmax for multi-class classification
model = keras.Sequential([
    layers.Dense(128, activation='relu'),
    layers.Dense(10, activation='softmax') 
])

# Ensure labels are one-hot encoded for categorical_crossentropy
model.compile(
    optimizer='rmsprop',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

# io.thecodeforge: Custom Training Loop with GradientTape
import tensorflow as tf
from tensorflow.keras import layers, losses, optimizers, metrics

def train_step(model, x_batch, y_batch, optimizer, loss_fn, train_acc):
    with tf.GradientTape() as tape:
        logits = model(x_batch, training=True)
        loss = loss_fn(y_batch, logits)
    grads = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(grads, model.trainable_variables))
    train_acc.update_state(y_batch, logits)
    return loss

# Usage
model = keras.Sequential([layers.Dense(10, activation='relu'), layers.Dense(1, activation='sigmoid')])
optimizer = optimizers.Adam(0.001)
loss_fn = losses.BinaryCrossentropy()
train_acc = metrics.BinaryAccuracy()

for epoch in range(10):
    for batch in dataset:
        loss = train_step(model, batch[0], batch[1], optimizer, loss_fn, train_acc)
    print(f'Epoch {epoch}: Loss {loss.numpy():.4f}, Acc {train_acc.result().numpy():.4f}')
    train_acc.reset_states()