比较与tf.keras.Model.predict、tf.keras.Model.predict_generator的功能差异
tf.keras.Model.predict
tf.keras.Model.predict(
x, batch_size=None, verbose=0, steps=None, callbacks=None, max_queue_size=10,
workers=1, use_multiprocessing=False
)
更多内容详见tf.keras.Model.predict。
tf.keras.Model.predict_generator
tf.keras.Model.predict_generator(
generator, steps=None, callbacks=None, max_queue_size=10, workers=1,
use_multiprocessing=False, verbose=0
)
mindspore.train.Model.eval
mindspore.train.Model.eval(valid_dataset, callbacks=None, dataset_sink_mode=True)
更多内容详见mindspore.train.Model.eval。
使用方式
tf.keras.Model.predict
和tf.keras.Model.predict_generator
分别支持数据集的不同载入方式,除基本的callbacks
等,还可通过workers
、 use_multiprocessing
配置多线程场景下的进程数等。
mindspore.train.Model.train
除了可配置基本的参数valid_dataset
、callbacks
,还可以配置dataset_sink_mode
设置是否下沉。
代码示例
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)
# predict
inputs_x = np.random.rand(10, 3)
pred_result = model.predict(inputs_x, batch_size=1)
# predict_generator
def generate_data(data_num):
for _ in range(data_num):
yield np.random.rand(2, 3), np.random.rand(2, 5)
model.predict_generator(generate_data(5), steps=2)
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"})
test_dataset = create_dataset()
model.eval(test_dataset)