Keras 在 1 个完整的 epoch 后停止

Question

尝试使用简单的 CNN 在 CIFAR-10 数据集上运行分类。然而，模型在完成第一个时期后停止，并且不会继续完成所有五个时期。请帮忙。

输入：

cifar10 = tf.keras.datasets.cifar10
(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()

import os
import matplotlib.pyplot as plt
import numpy as np
import time
import tensorflow as tf
from tensorflow import keras 
from tensorflow.keras import layers
from tensorflow.keras import models
from tensorflow.keras import optimizers
from tensorflow.keras.applications import VGG16
from tensorflow.keras.preprocessing.image import ImageDataGenerator

model = models.Sequential()

# Convolutional base (feature extractor)
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))

# Deep feed-forward classifier
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))

model.compile(loss='sparse_categorical_crossentropy', optimizer=optimizers.RMSprop(lr=1e-4), metrics=['acc'])

history = model.fit(
    x=train_images,
    y=train_labels,
    steps_per_epoch=100,
    epochs=5,
    verbose=1,
    validation_data=(test_images, test_labels),
    validation_steps=50)

输出：

Train on 50000 samples, validate on 10000 samples
Epoch 1/5
50000/50000 [==============================] - 28s 564us/sample - loss: 2.1455 - acc: 0.2945 - val_loss: 2.0011 - val_acc: 0.3038

Answer 1

您应该删除steps_per_epoh和validation_steps并使用batch_size参数。