import tensorflow as tf
from tensorflow import keras
from keras import Sequential,datasets, layers, optimizers, metrics def preprocess(x, y):
"""数据处理函数"""
x = tf.cast(x, dtype=tf.float32) / 255.
y = tf.cast(y, dtype=tf.int32)
return x, y # 加载数据
(x, y), (x_test, y_test) = datasets.fashion_mnist.load_data()
print(x.shape, y.shape) # 处理train数据
batch_size = 128
db = tf.data.Dataset.from_tensor_slices((x, y))
db = db.map(preprocess).shuffle(10000).batch(batch_size) # 处理test数据
db_test = tf.data.Dataset.from_tensor_slices((x_test, y_test))
db_test = db_test.map(preprocess).batch(batch_size) # # 生成train数据的迭代器
db_iter = iter(db)
sample = next(db_iter)
print(f'batch: {sample[0].shape,sample[1].shape}') # 设计网络结构
model = Sequential([
layers.Dense(256, activation=tf.nn.relu), # [b,784] --> [b,256]
layers.Dense(128, activation=tf.nn.relu), # [b,256] --> [b,128]
layers.Dense(64, activation=tf.nn.relu), # [b,128] --> [b,64]
layers.Dense(32, activation=tf.nn.relu), # [b,64] --> [b,32]
layers.Dense(10) # [b,32] --> [b,10], 330=32*10+10
]) model.build(input_shape=[None, 28 * 28])
model.summary() # 调试
# w = w - lr*grad
optimizer = optimizers.Adam(lr=1e-3) # 优化器，加快训练速度 def main():
"""主运行函数"""
for epoch in range(10):
for step, (x, y) in enumerate(db):
# x:[b,28,28] --> [b,784]
# y:[b]
x = tf.reshape(x, [-1, 28 * 28])
with tf.GradientTape() as tape:
# [b,784] --> [b,10]
logits = model(x)
y_onehot = tf.one_hot(y, depth=10)
# [b]
loss_mse = tf.reduce_mean(tf.losses.MSE(y_onehot, logits))
loss_ce = tf.reduce_mean(tf.losses.categorical_crossentropy(y_onehot,logits,from_logits=True))
grads = tape.gradient(loss_ce, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
if step % 100 == 0:
print(epoch, step, f'loss: {float(loss_ce),float(loss_mse)}') # test
total_correct = 0
total_num = 0
for x, y in db_test:
# x:[b,28,28] --> [b,784]
# y:[b]
x = tf.reshape(x, [-1, 28 * 28])
# [b,10]
logits = model(x)
# logits --> prob [b,10]
prob = tf.nn.softmax(logits, axis=1)
# [b,10] --> [b], int32
pred = tf.argmax(prob, axis=1)
pred = tf.cast(pred, dtype=tf.int32)
# pred:[b]
# y:[b]
# correct: [b], True: equal; False: not equal
correct = tf.equal(pred, y)
correct = tf.reduce_sum(tf.cast(correct, dtype=tf.int32))
total_correct += int(correct)
total_num += x.shape[0]
acc = total_correct / total_num
print(epoch, f'test acc: {acc}') if __name__ == '__main__':
main()