Save model every 10 epochs tensorflow.keras v2

Save model every 10 epochs tensorflow.keras v2

In TensorFlow 2 using the tensorflow.keras API, you can use a combination of the ModelCheckpoint callback and the fit function's callbacks parameter to save the model every 10 epochs. The ModelCheckpoint callback allows you to save the model's weights or the entire model at specified intervals during training.

Here's an example of how to save the model every 10 epochs using TensorFlow 2 and tensorflow.keras:

import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.callbacks import ModelCheckpoint # Create a simple model model = Sequential([ Dense(64, activation='relu', input_shape=(input_shape,)), Dense(32, activation='relu'), Dense(output_shape, activation='softmax') ]) # Compile the model model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # Create a ModelCheckpoint callback to save the model weights checkpoint_callback = ModelCheckpoint(filepath='model_checkpoint_{epoch:02d}.h5', save_freq='epoch') # Train the model with the ModelCheckpoint callback model.fit(x_train, y_train, epochs=100, batch_size=32, callbacks=[checkpoint_callback])

In this example, the ModelCheckpoint callback is created with the filepath argument specifying the format of the saved model files. The save_freq argument is set to 'epoch' to save the model every epoch. You can modify save_freq to 'epoch' to save every 10 epochs.

The {epoch:02d} placeholder in the filepath argument ensures that the saved model filenames include the epoch number padded with zeros.

Remember to replace input_shape, output_shape, x_train, and y_train with your actual values.

With this setup, the model will be saved as separate files at every 10-epoch interval during training.

Examples

  1. How to Save a TensorFlow Keras Model Every 10 Epochs

    • This query is about saving a model at regular intervals during training in TensorFlow. The snippet demonstrates using the ModelCheckpoint callback with a custom period to save the model every 10 epochs.
    # If TensorFlow is not already installed !pip install tensorflow 
    import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers from tensorflow.keras.callbacks import ModelCheckpoint # Create a simple model model = keras.Sequential([ layers.Dense(10, activation='relu', input_shape=(5,)), layers.Dense(1) ]) # Model checkpoint callback to save every 10 epochs checkpoint = ModelCheckpoint( 'model_at_epoch_{epoch:02d}.h5', # Filename pattern save_freq=10 * 32, # Assuming 32 is the batch size, so every 10 epochs save_weights_only=False, # To save the entire model verbose=1 # Display messages when saving ) # Compile and fit the model model.compile(optimizer='adam', loss='mse') model.fit( tf.random.normal([1000, 5]), # Random training data tf.random.normal([1000, 1]), epochs=30, # Example: 30 epochs callbacks=[checkpoint] )

  2. Using ModelCheckpoint to Save TensorFlow Keras Model Periodically

    • This snippet shows how to use the ModelCheckpoint callback to save a Keras model every 10 epochs.
    import tensorflow as tf from tensorflow.keras.callbacks import ModelCheckpoint # Define a simple sequential model model = tf.keras.Sequential([ tf.keras.layers.Dense(10, activation='relu', input_shape=(5,)), tf.keras.layers.Dense(1) ]) # Create a checkpoint callback to save the model every 10 epochs checkpoint = ModelCheckpoint( filepath='model_checkpoint_{epoch:02d}.h5', save_freq='epoch', period=10, # Save every 10 epochs save_best_only=False, verbose=1 # Print when saving ) model.compile(optimizer='adam', loss='mse') # Train the model with the checkpoint callback model.fit(tf.random.normal([1000, 5]), tf.random.normal([1000, 1]), epochs=30, callbacks=[checkpoint])

  3. Save Weights Every 10 Epochs with TensorFlow Keras

    • This snippet demonstrates how to save only the model weights every 10 epochs using TensorFlow Keras.
    import tensorflow as tf from tensorflow.keras.callbacks import ModelCheckpoint # Define a model model = tf.keras.Sequential([ tf.keras.layers.Dense(16, activation='relu', input_shape=(5,)), tf.keras.layers.Dense(1) ]) # Create a checkpoint callback to save weights every 10 epochs checkpoint = ModelCheckpoint( filepath='weights_checkpoint_{epoch:02d}.h5', save_weights_only=True, # Save only weights save_freq=10 * 32, # Assuming batch size 32 verbose=1 ) model.compile(optimizer='adam', loss='mse') model.fit(tf.random.normal([1000, 5]), tf.random.normal([1000, 1]), epochs=30, callbacks=[checkpoint])

  4. Save TensorFlow Keras Model with Overwriting Enabled Every 10 Epochs

    • This snippet shows how to save a Keras model every 10 epochs with overwriting allowed, ensuring the latest model is saved.
    import tensorflow as tf from tensorflow.keras.callbacks import ModelCheckpoint # Define a simple model model = tf.keras.Sequential([ tf.keras.layers.Dense(16, activation='relu', input_shape=(5,)), tf.keras.layers.Dense(1) ]) # Create a checkpoint callback to save the latest model every 10 epochs, overwriting the previous one checkpoint = ModelCheckpoint( filepath='latest_model_checkpoint.h5', # Same filename to overwrite save_freq=10 * 32, save_weights_only=False, # Save the full model verbose=1, overwrite=True ) model.compile(optimizer='adam', loss='mse') model.fit(tf.random.normal([1000, 5]), tf.random.normal([1000, 1]), epochs=30, callbacks=[checkpoint])

  5. Save TensorFlow Keras Model Every 10 Epochs with a Timestamp

    • This snippet explains how to save a Keras model every 10 epochs with a unique timestamp for each saved model.
    import tensorflow as tf from tensorflow.keras.callbacks import ModelCheckpoint import datetime # Define a simple model model = tf.keras.Sequential([ tf.keras.layers.Dense(16, activation='relu', input_shape=(5,)), tf.keras.layers.Dense(1) ]) # Get a timestamp current_timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # Create a checkpoint callback to save with timestamp every 10 epochs checkpoint = ModelCheckpoint( filepath=f'model_checkpoint_{current_timestamp}_{{epoch:02d}}.h5', save_freq=10 * 32, verbose=1, save_weights_only=False ) model.compile(optimizer='adam', loss='mse') model.fit(tf.random.normal([1000, 5]), tf.random.normal([1000, 1]), epochs=30, callbacks=[checkpoint])

  6. Custom Checkpoint to Save TensorFlow Keras Model Every 10 Epochs

    • This snippet demonstrates creating a custom callback to save a TensorFlow Keras model every 10 epochs.
    import tensorflow as tf from tensorflow.keras.callbacks import Callback import os class CustomCheckpoint(Callback): def __init__(self, interval, filepath): super().__init__() self.interval = interval self.filepath = filepath def on_epoch_end(self, epoch, logs=None): if (epoch + 1) % self.interval == 0: self.model.save(self.filepath.format(epoch=epoch + 1)) print(f"Model saved at epoch {epoch + 1}") # Define a simple model model = tf.keras.Sequential([ tf.keras.layers.Dense(16, activation='relu', input_shape=(5,)), tf.keras.layers.Dense(1) ]) custom_checkpoint = CustomCheckpoint(interval=10, filepath='custom_checkpoint_epoch_{epoch}.h5') model.compile(optimizer='adam', loss='mse') model.fit(tf.random.normal([1000, 5]), tf.random.normal([1000, 1]), epochs=30, callbacks=[custom_checkpoint])

  7. Save TensorFlow Keras Model Every 10 Epochs with Conditions

    • This snippet shows how to save a Keras model every 10 epochs with additional conditions, like a minimum validation accuracy.
    import tensorflow as tf from tensorflow.keras.callbacks import ModelCheckpoint # Define a simple model model = tf.keras.Sequential([ tf.keras.layers.Dense(16, activation='relu', input_shape=(5,)), tf.keras.layers.Dense(1) ]) checkpoint = ModelCheckpoint( filepath='conditional_checkpoint_{epoch:02d}.h5', save_freq=10 * 32, verbose=1, monitor='val_accuracy', # Check validation accuracy mode='max', # Save only if accuracy improves save_best_only=True # Save only if it's the best so far ) model.compile(optimizer='adam', loss='mse', metrics=['accuracy']) model.fit( tf.random.normal([1000, 5]), tf.random.normal([1000, 1]), epochs=30, validation_split=0.2, callbacks=[checkpoint] )

  8. Save TensorFlow Keras Model and Keep Best Every 10 Epochs

    • This snippet demonstrates saving a Keras model every 10 epochs but keeping only the best version based on a given metric.
    import tensorflow as tf from tensorflow.keras.callbacks import ModelCheckpoint model = tf.keras.Sequential([ tf.keras.layers.Dense(16, activation='relu', input_shape=(5,)), tf.keras.layers.Dense(1) ]) checkpoint = ModelCheckpoint( filepath='best_model_checkpoint.h5', save_freq=10 * 32, # Save every 10 epochs verbose=1, monitor='val_loss', # Monitor validation loss mode='min', # Keep model with lowest loss save_best_only=True # Keep only the best version ) model.compile(optimizer='adam', loss='mse') model.fit( tf.random.normal([1000, 5]), tf.random.normal([1000, 1]), epochs=30, validation_split=0.2, callbacks=[checkpoint] )

  9. Save TensorFlow Keras Model Every 10 Epochs and Load

    • This snippet explains how to save a Keras model every 10 epochs and then load it later for evaluation or inference.
    import tensorflow as tf from tensorflow.keras.callbacks import ModelCheckpoint model = tf.keras.Sequential([ tf.keras.layers.Dense(16, activation='relu', input_shape=(5,)), tf.keras.layers.Dense(1) ]) checkpoint = ModelCheckpoint( filepath='model_checkpoint_epoch_{epoch:02d}.h5', save_freq=10 * 32, # Save every 10 epochs verbose=1 ) model.compile(optimizer='adam', loss='mse') model.fit( tf.random.normal([1000, 5]), tf.random.normal([1000, 1]), epochs=30, validation_split=0.2, callbacks=[checkpoint] ) # Load the model from a specific checkpoint loaded_model = tf.keras.models.load_model('model_checkpoint_epoch_10.h5')

  10. Save TensorFlow Keras Model Every 10 Epochs with Advanced Options

    • This snippet demonstrates saving a Keras model every 10 epochs with advanced options like saving in TensorFlow SavedModel format.
    import tensorflow as tf from tensorflow.keras.callbacks import ModelCheckpoint model = tf.keras.Sequential([ tf.keras.layers.Dense(16, activation='relu', input_shape=(5,)), tf.keras.layers.Dense(1) ]) checkpoint = ModelCheckpoint( filepath='model_checkpoint_tf_{epoch:02d}', save_freq=10 * 32, # Save every 10 epochs save_best_only=False, # Save every time, regardless of performance verbose=1, save_format='tf' # Save in TensorFlow SavedModel format ) model.compile(optimizer='adam', loss='mse') model.fit( tf.random.normal([1000, 5]), tf.random.normal([1000, 1]), epochs=30, validation_split=0.2, callbacks=[checkpoint] )

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