TensorFlow2.0.0_课时23_神经网络搭建_分类问题

导入mnist数据集

from pathlib import Path
import requests

DATA_PATH = Path("data")
PATH = DATA_PATH / "mnist"

PATH.mkdir(parents=True, exist_ok=True)

URL = "http://deeplearning.net/data/mnist/"
FILENAME = "mnist.pkl.gz"

if not (PATH / FILENAME).exists():
        content = requests.get(URL + FILENAME).content
        (PATH / FILENAME).open("wb").write(content)

28281 拉成784个像素点

开始搭建TensorFlow

import tensorflow as tf
from tensorflow.keras import layers

model = tf.keras.Sequential()
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))		#需要得到10个结果（数字0~9）

pyplot.imshow(x_train[0].reshape((28, 28)), cmap="gray")

# tf.losses.CategoricalCrossentropy

model.compile(optimizer=tf.keras.optimizers.Adam(0.01),
             loss=tf.keras.losses.SparseCategoricalCrossentropy(),
             metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])		#准确率

model.fit(x_train, y_train, epochs=5, batch_size=64,
          validation_data=(x_valid, y_valid))

选择损失和评估函数时候需要选择合适的,Api参考：https://tensorflow.google.cn/api_docs/python/tf/keras/metrics/SparseCategoricalAccuracy?version=stable
一定选择合适的损失函数
不能用交叉熵：CategoricalCrossentropy，数据类型不对，交叉熵为one-hot
应该用SparseCategoricalCrossentropy代替

结果如下，效果一般

… 40256/50000 [=>…] - ETA: 0s -
loss: 0.1138 - sparse_categorical_accuracy: 0.9648 41792/50000
[>…] - ETA: 0s - loss: 0.1151 -
sparse_categorical_accuracy: 0.9645 43392/50000
[=>…] - ETA: 0s - loss: 0.1150 -
sparse_categorical_accuracy: 0.9645 44992/50000
[=>…] - ETA: 0s - loss: 0.1157 -
sparse_categorical_accuracy: 0.9643 46592/50000
[>…] - ETA: 0s - loss: 0.1152 -
sparse_categorical_accuracy: 0.9645 47872/50000
[=>…] - ETA: 0s - loss: 0.1141 -
sparse_categorical_accuracy: 0.9648 48960/50000
[==========================>.] - ETA: 0s - loss: 0.1140 -
sparse_categorical_accuracy: 0.9650