使用tensorFlow创建了一个热门的时尚MNIST数据集

Question

下面的情况是一个场景吗？应该为标签创建一个热的编码？

我还试图创建一个热门的编码，但不断出现错误。这是怎么做到的？

注：我在谷歌实验室工作。

谢谢。

import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
fashion = keras.datasets.fashion_mnist
(train_images,train_labels),(test_images,test_labels) = fashion.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress','Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images =  tf.cast(train_images, tf.float32) / 255.0
test_images = tf.cast(test_images, tf.float32) / 255.0
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=10, batch_size=512, shuffle=True, validation_split=0.1)

为了添加一个热编码，我尝试将数据更改为：

train_images =  tf.cast(train_images, tf.float32) / 255.0
test_images = tf.cast(test_images, tf.float32) / 255.0
train_labels = tf.one_hot(tf.cast(train_labels, tf.int64), depth=10)
test_labels = tf.one_hot(tf.cast(test_labels, tf.int64), depth=10)

哪个给出了错误：

InvalidArgumentError Traceback(上次调用的最近一次(在((2728--->29 model.fit(train_images，train_labels，历元=10，batch_size=512，shuffle=True，validation_split=0.1(30

Answer 1

我认为这段代码应该可以工作。它没有一个热编码，但它工作得很完美。

import tensorflow as tf    
from tensorflow import keras    
import numpy as np    
import matplotlib.pyplot as plt 

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

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] 
train_images = train_images / 255.0    
test_images = test_images / 255.0

model = keras.Sequential([keras.layers.Flatten(input_shape=(28, 28)),keras.layers.Dense(128, activation=tf.nn.relu),   keras.layers.Dense(10, activation=tf.nn.softmax)])
model.compile(optimizer=tf.train.AdamOptimizer(),    loss='sparse_categorical_crossentropy',metrics=['accuracy'])        

model.fit(train_images, train_labels, epochs=20)

Answer 2

我找到了答案。请参阅稀疏类别交叉熵与类别交叉熵(keras，准确性(

要修复一个热编码的代码，您应该修复代码：

model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])

收件人：

model.fit(train_images, one_hot_train_labels, epochs=10, batch_size=128, shuffle=True, validation_split=0.1)