import tensorflow as tf
import matplotlib.pyplot as plt
fashion_mnist = tf.keras.datasets.fashion_mnist
(train_images,train_labels),(test_images,test_labels) = fashion_mnist.load_data() #导入数据
plt.imshow(train_images[0])
#print(train_images[0])
#print(train_labels[0])
plt.show()
print(train_images.shape)
print(test_images.shape)
train_images = train_images/255.0
test_images = test_images/255.0
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128,activation = tf.nn.relu),
tf.keras.layers.Dense(10,activation = tf.nn.softmax)])
model.compile(optimizer = 'Adam',loss='sparse_categorical_crossentropy')
model.fit(train_images,train_labels,epochs=5)
model.evaluate(test_images,test_labels)
如下是我的结果:
用shape测试过训练集是60000和验证集是10000,但最后训练数目和验证数目为1875和313,为原来的1/32,为啥????