Function Differences with tf.keras.Model.fit and tf.keras.Model.fit_generator

tf.keras.Model.fit

tf.keras.Model.fit(
    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
)

For more information, see tf.keras.Model.fit.

tf.keras.Model.fit_generator

tf.keras.Model.fit_generator(
    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
)

For more information, see tf.keras.Model.fit_generator.

mindspore.train.Model.train

mindspore.train.Model.train(epoch, train_dataset, callbacks=None, dataset_sink_mode=True, sink_size=-1)

For more information, see mindspore.train.Model.train.

Usage

tf.keras.Model.fit and tf.keras.Model.fit_generator support different methods for loading datasets, in addition to the basic epoch and callbacks respectively. Set the output information format during training by verbose and configure the validation set by validation* and other parameters for simultaneous validation during training. The number of processes in multi-threaded scenarios is configured by workers and use_multiprocessing. Whether the data set is shuffled during training is set by shuffle. Other inputs are not detailed, and please refer to the official API documentation for details.

mindspore.train.Model.train can be configured with dataset_sink_mode, sink_size for sinking in addition to the basic training parameters epoch, train_dataset, callback. Other functions are not provided yet.

Code Example

The following code results are random.

import tensorflow as tf
import numpy as np

inputs = tf.keras.Input(shape=(3,))
x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.compile(loss='mae', optimizer='adam')

# fit
inputs_x = np.random.rand(10, 3)
inputs_y = np.random.rand(10, 5)
model.fit(inputs_x, inputs_y, batch_size=2)
# output:
# 10/10 [==============================] - 0s 18ms/sample - loss: 0.3080


# fit generator
def generate_data(data_num):
    for _ in range(data_num):
        yield np.random.rand(2, 3), np.random.rand(2, 5)
model.fit_generator(generate_data(5), steps_per_epoch=5)

# output:
# 5/5 [==============================] - 0s 77ms/step - loss: 0.3292

import mindspore as ms
from mindspore.train import Model
from mindspore import nn
import numpy as np
from mindspore import dataset as ds

def get_data(num):
    for _ in range(num):
        yield np.random.rand(3).astype(np.float32), np.random.rand(4).astype(np.float32)

def create_dataset(num_data=16, batch_size=4):
    dataset = ds.GeneratorDataset(list(get_data(num_data)), column_names=['data', 'label'])
    dataset = dataset.batch(batch_size)
    return dataset

class LinearNet(nn.Cell):
    def __init__(self):
        super().__init__()
        self.fc = nn.Dense(3, 4)

    def construct(self, x):
        return self.fc(x)

net = LinearNet()
crit = nn.MSELoss()
opt = nn.Momentum(net.trainable_params(), learning_rate=0.05, momentum=0.9)
model = Model(network=net, loss_fn=crit, optimizer=opt, metrics={"mae"})

train_dataset = create_dataset()
model.train(2, train_dataset)