I am using fit_generator() as follows:
history = model.fit_generator(generator = trainGenerator,
steps_per_epoch = trainGenerator.samples //nBatches, # total number of steps (batches of samples)
epochs = nEpochs, # number of epochs to train the model verbose = 2, # verbosity mode.0 = silent, 1 = progress bar, 2 = one line per epoch callbacks = callback, # keras.callbacks.Callback instances to apply during training validation_data = valGenerator, # generator or tuple on which to evaluate the loss and any model metrics at the end of each epoch validation_steps =
valGenerator.samples //nBatches, # number of steps (batches of samples) to yield from validation_data generator before stopping at the end of every epoch
class_weight = classWeights, # optional dictionary mapping class indices(integers) to a weight(float) value, used
for weighting the loss
function max_queue_size = 10, # maximum size
for the generator queue workers = 1, # maximum number of processes to spin up when using process - based threading use_multiprocessing = False, # whether to use process - based threading shuffle = True, # whether to shuffle the order of the batches at the beginning of each epoch initial_epoch = 0)The specs of my machine are:
CPU: 2 xXeon E5 - 2260 2.6 GHz Cores: 10 Graphic card: Titan X, Maxwell, GM200 RAM: 128 GB HDD: 4 TB SSD: 512 GB
Last Updated : 25 Jun, 2020
Syntax:
fit(object, x = NULL, y = NULL, batch_size = NULL, epochs = 10,
verbose = getOption("keras.fit_verbose",
default = 1),
callbacks = NULL, view_metrics = getOption("keras.view_metrics",
default = "auto"), validation_split = 0, validation_data = NULL,
shuffle = TRUE, class_weight = NULL, sample_weight = NULL,
initial_epoch = 0, steps_per_epoch = NULL, validation_steps = NULL,
...)Understanding few important arguments:
- > object: the model to train. -
> X: our training data.Can be Vector, array or matrix -
> Y: our training labels.Can be Vector, array or matrix -
> Batch_size: it can take any integer value or NULL and by
default, it will
be set to 32. It specifies no.of samples per gradient. -
> Epochs: an integer and number of epochs we want to train our model
for. -
> Verbose: specifies verbosity mode(0 = silent, 1 = progress bar, 2 = one line per epoch). -
> Shuffle: whether we want to shuffle our training data before each epoch. -
> steps_per_epoch: it specifies the total number of steps taken before
one epoch has finished and started the next epoch.By
default it values is set to NULL.How to use Keras fit:
model.fit(Xtrain, Ytrain, batch_size = 32, epochs = 100)
Detailed explanation of model.fit_generator() parameters: queue size, workers and use_multiprocessing,I am applying transfer-learning on a pre-trained network using the GPU version of keras. I don't understand how to define the parameters max_queue_size, workers, and use_multiprocessing. If I change these parameters (primarily to speed-up learning), I am unsure whether all data is still seen per epoch.,maximum size of the internal training queue which is used to "precache" samples from the generator ,number of threads generating batches in parallel. Batches are computed in parallel on the CPU and passed on the fly onto the GPU for neural network computations
I am using fit_generator() as follows:
history = model.fit_generator(generator = trainGenerator,
steps_per_epoch = trainGenerator.samples //nBatches, # total number of steps (batches of samples)
epochs = nEpochs, # number of epochs to train the model verbose = 2, # verbosity mode.0 = silent, 1 = progress bar, 2 = one line per epoch callbacks = callback, # keras.callbacks.Callback instances to apply during training validation_data = valGenerator, # generator or tuple on which to evaluate the loss and any model metrics at the end of each epoch validation_steps =
valGenerator.samples //nBatches, # number of steps (batches of samples) to yield from validation_data generator before stopping at the end of every epoch
class_weight = classWeights, # optional dictionary mapping class indices(integers) to a weight(float) value, used
for weighting the loss
function max_queue_size = 10, # maximum size
for the generator queue workers = 1, # maximum number of processes to spin up when using process - based threading use_multiprocessing = False, # whether to use process - based threading shuffle = True, # whether to shuffle the order of the batches at the beginning of each epoch initial_epoch = 0)The specs of my machine are:
CPU: 2 xXeon E5 - 2260 2.6 GHz Cores: 10 Graphic card: Titan X, Maxwell, GM200 RAM: 128 GB HDD: 4 TB SSD: 512 GB
fit_generator(self, generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None, validation_data=None, validation_steps=None, validation_freq=1, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0),fit(self, x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False, **kwargs),Keras deep learning library provides three different methods to train Deep Learning models. Each model has its own specialized property to train a deep neural network. Here we will discuss keras.fit() and keras.,We have seen that keras.fit () is used where all learning information can be entered into memory and data can be illuminated while keras.fit_generator () is used when either we have big data to enter into memory or when data addition needs to be used.
- Returns the `History` item. `History.history` records the training loss rates, metric values, the guaranteed loss rates and the validation metric values per epoch.
- How to use:
model.fit(xtrain, ytrain, batch_size = 32, epochs = 100)
max_queue_size: Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.,max_queue_size: Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.,workers: Integer. Used for generator or keras.utils.Sequence input only. Maximum number of processes to spin up when using process-based threading. If unspecified, workers will default to 1.,workers: Integer. Used for generator or keras.utils.Sequence input only. Maximum number of processes to spin up when using process-based threading. If unspecified, workers will default to 1.
Model.compile(
optimizer = "rmsprop",
loss = None,
metrics = None,
loss_weights = None,
weighted_metrics = None,
run_eagerly = None,
steps_per_execution = None,
jit_compile = None, **
kwargs
)model.compile(optimizer = tf.keras.optimizers.Adam(learning_rate = 1e-3),
loss = tf.keras.losses.BinaryCrossentropy(),
metrics = [tf.keras.metrics.BinaryAccuracy(),
tf.keras.metrics.FalseNegatives()
])Model.fit(
x = None,
y = None,
batch_size = None,
epochs = 1,
verbose = "auto",
callbacks = None,
validation_split = 0.0,
validation_data = None,
shuffle = True,
class_weight = None,
sample_weight = None,
initial_epoch = 0,
steps_per_epoch = None,
validation_steps = None,
validation_batch_size = None,
validation_freq = 1,
max_queue_size = 10,
workers = 1,
use_multiprocessing = False,
)Model.evaluate(
x = None,
y = None,
batch_size = None,
verbose = "auto",
sample_weight = None,
steps = None,
callbacks = None,
max_queue_size = 10,
workers = 1,
use_multiprocessing = False,
return_dict = False, **
kwargs
)Model.predict(
x,
batch_size = None,
verbose = "auto",
steps = None,
callbacks = None,
max_queue_size = 10,
workers = 1,
use_multiprocessing = False,
)Model.train_on_batch( x, y = None, sample_weight = None, class_weight = None, reset_metrics = True, return_dict = False, )