以下情況是否應該為標簽創建熱編碼？
我還嘗試創建一個熱編碼，但一直出現錯誤。這是怎么做的？
注意：我在 googles colab 作業。
謝謝你。
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)
要添加單熱編碼，我嘗試將資料更改為：
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)
這給出了錯誤：
InvalidArgumentError Traceback (最近一次呼叫最后一次) in () 27 28 ---> 29 model.fit(train_images, train_labels, epochs=10, batch_size=512, shuffle=True, validation_split=0.1) 30
uj5u.com熱心網友回復：
我認為這段代碼應該可以作業。它沒有單熱編碼，但效果很好。
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)
uj5u.com熱心網友回復：
我找到了答案。請參閱Sparse_categorical_crossentropy 與 categorical_crossentropy（keras，準確性）
要修復單熱編碼的代碼，您應該修復代碼：
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
到：
model.fit(train_images, one_hot_train_labels, epochs=10, batch_size=128, shuffle=True, validation_split=0.1)
轉載請註明出處，本文鏈接：https://www.uj5u.com/ruanti/401994.html
上一篇：在多個資料集上訓練神經網路模型