【发布时间】：2019-06-13 12:30:05
【问题描述】：
当尝试使用tensorflow.keras API 和tf.Dataset 诱导迭代器来拟合 Keras 模型时，模型会抱怨 steps_per_epoch 参数，即使我已将此参数设置为具体值。
下面是我的模型类
import tensorflow as tf
import numpy as np
from typing import Union, List
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras import layers
from tftools import TFTools
class TestServe():
def __init__(self, tfrecords: Union[List[tf.train.Example], tf.train.Example], batch_size: int = 10, input_shape: tuple = (64, 23)) -> None:
self.tfrecords = tfrecords
self.batch_size = batch_size
self.input_shape = input_shape
def get_model(self):
ins = layers.Input(shape=(64, 23))
l = layers.Reshape((*self.input_shape, 1))(ins)
l = layers.Conv2D(8, (30, 23), padding='same', activation='relu')(l)
l = layers.MaxPool2D((4, 5), strides=(4, 5))(l)
l = layers.Conv2D(16, (3, 3), padding='same', activation='relu')(l)
l = layers.Conv2D(32, (3, 3), padding='same', activation='relu')(l)
l = layers.MaxPool2D((2, 2), strides=(2, 2))(l)
l = layers.Flatten()(l)
out = layers.Dense(1, activation='softmax')(l)
return tf.keras.models.Model(ins, out)
def train(self):
# Create Dataset
dataset = TFTools.create_dataset(self.tfrecords)
dataset = dataset.repeat(6).batch(self.batch_size)
val_iterator = dataset.take(300).make_one_shot_iterator()
train_iterator = dataset.skip(300).make_one_shot_iterator()
model = self.get_model()
model.summary()
model.compile(optimizer='rmsprop',
loss='binary_crossentropy', metrics=['accuracy'])
model.fit(train_iterator, validation_data=val_iterator,
epochs=10, verbose=1, steps_per_epoch=20)
def predict(self, X: np.array) -> np.array:
pass
ts = TestServe(['./ok.tfrecord', './nok.tfrecord'])
ts.train()
但是一旦我开始训练，在第一批完成之前，我从 tensorflow 得到一个异常
2019-06-13 14:22:25.393398: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 1995445000 Hz
2019-06-13 14:22:25.393681: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x2f7d120 executing computations on platform Host. Devices:
2019-06-13 14:22:25.393708: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
Epoch 1/2
19/20 [===========================>..] - ETA: 0s - loss: 1.1921e-07 - acc: 1.0000Traceback (most recent call last):
File "TestServe.py", line 62, in <module>
ts.train()
File "TestServe.py", line 56, in train
epochs=2, verbose=1, callbacks=callbacks, steps_per_epoch=20) #The steps_per_epoch is typically samples_per_epoch / batch_size
File "/home/josef/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 880, in fit
validation_steps=validation_steps)
File "/home/josef/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 364, in model_iteration
validation_in_fit=True)
File "/home/josef/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 202, in model_iteration
steps_per_epoch)
File "/home/josef/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 76, in _get_num_samples_or_steps
'steps_per_epoch')
File "/home/josef/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_utils.py", line 230, in check_num_samples
if check_steps_argument(ins, steps, steps_name):
File "/home/josef/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_utils.py", line 960, in check_steps_argument
input_type=input_type_str, steps_name=steps_name))
ValueError: When using data tensors as input to a model, you should specify the `steps_per_epoch` argument.
原始数据集包含大约 1500 个样本，但我想将多个 tfrecord 文件连接到 TFRecordDataset，因此我不会获得有关长度的信息。
有人见过类似的东西吗？我不知道去哪里寻求帮助，因为tf.keras API 相对较新。 create_dataset 函数只返回使用正确解析函数映射的数据集。
【问题讨论】：
-
您有两个选择：1) 确定数据集的长度 2) 不使用 tf.data.Dataset，而是将数据转换为 NumPy 数组，然后将它们提供给模型（您不需要需要指定steps参数）。
-
我指定属性时为什么会报错？
标签： python tensorflow deep-learning tensorflow-datasets