contrib.keras.callbacks.LambdaCallback

tf.contrib.keras.callbacks.LambdaCallback

class tf.contrib.keras.callbacks.LambdaCallback

Defined in tensorflow/contrib/keras/python/keras/callbacks.py.

Callback for creating simple, custom callbacks on-the-fly.

This callback is constructed with anonymous functions that will be called at the appropriate time. Note that the callbacks expects positional arguments, as: - on_epoch_begin and on_epoch_end expect two positional arguments: epoch, logs - on_batch_begin and on_batch_end expect two positional arguments: batch, logs - on_train_begin and on_train_end expect one positional argument: logs

Arguments:

on_epoch_begin: called at the beginning of every epoch.
on_epoch_end: called at the end of every epoch.
on_batch_begin: called at the beginning of every batch.
on_batch_end: called at the end of every batch.
on_train_begin: called at the beginning of model training.
on_train_end: called at the end of model training.

Example: ```python # Print the batch number at the beginning of every batch. batch_print_callback = LambdaCallback( on_batch_begin=lambda batch,logs: print(batch))

# Plot the loss after every epoch.
import numpy as np
import matplotlib.pyplot as plt
plot_loss_callback = LambdaCallback(
    on_epoch_end=lambda epoch, logs: plt.plot(np.arange(epoch),
                                              logs['loss']))

# Terminate some processes after having finished model training.
processes = ...
cleanup_callback = LambdaCallback(
    on_train_end=lambda logs: [
        p.terminate() for p in processes if p.is_alive()])

model.fit(...,
          callbacks=[batch_print_callback,
                     plot_loss_callback,
                     cleanup_callback])
```

Methods

__init__

__init__(
    on_epoch_begin=None,
    on_epoch_end=None,
    on_batch_begin=None,
    on_batch_end=None,
    on_train_begin=None,
    on_train_end=None,
    **kwargs
)

on_batch_begin

on_batch_begin(
    batch,
    logs=None
)

on_batch_end

on_batch_end(
    batch,
    logs=None
)

on_epoch_begin

on_epoch_begin(
    epoch,
    logs=None
)

on_epoch_end

on_epoch_end(
    epoch,
    logs=None
)

on_train_begin

on_train_begin(logs=None)

on_train_end

on_train_end(logs=None)

set_model

set_model(model)

set_params

set_params(params)