FashionMNIST
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, layers, optimizers, Sequential, 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()
(60000, 28, 28) (60000,)
batch: (TensorShape([128, 28, 28]), TensorShape([128]))
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) multiple 200960
_________________________________________________________________
dense_1 (Dense) multiple 32896
_________________________________________________________________
dense_2 (Dense) multiple 8256
_________________________________________________________________
dense_3 (Dense) multiple 2080
_________________________________________________________________
dense_4 (Dense) multiple 330
=================================================================
Total params: 244,522
Trainable params: 244,522
Non-trainable params: 0
_________________________________________________________________
0 0 loss: (2.317634105682373, 0.240666925907135)
0 100 loss: (0.5373745560646057, 22.973751068115234)
0 200 loss: (0.43246397376060486, 23.848735809326172)
0 300 loss: (0.46746426820755005, 25.622543334960938)
0 400 loss: (0.35051512718200684, 25.582551956176758)
0 test acc: 0.8411
1 0 loss: (0.3928898572921753, 25.729997634887695)
1 100 loss: (0.3873934745788574, 28.322147369384766)
1 200 loss: (0.3231426477432251, 23.980030059814453)
1 300 loss: (0.38196271657943726, 26.83534812927246)
1 400 loss: (0.32163679599761963, 23.470108032226562)
1 test acc: 0.8522
2 0 loss: (0.37968552112579346, 24.739742279052734)
2 100 loss: (0.3641986846923828, 32.74003601074219)
2 200 loss: (0.28391164541244507, 27.59665870666504)
2 300 loss: (0.35625365376472473, 32.84230422973633)
2 400 loss: (0.2914122939109802, 28.2105712890625)
2 test acc: 0.8643
3 0 loss: (0.32174795866012573, 28.54513168334961)
3 100 loss: (0.33406931161880493, 35.23341369628906)
3 200 loss: (0.2978339195251465, 31.21166229248047)
3 300 loss: (0.34828150272369385, 37.68536376953125)
3 400 loss: (0.2958236336708069, 34.39887619018555)
3 test acc: 0.8657
4 0 loss: (0.2884419858455658, 28.723865509033203)
4 100 loss: (0.30416643619537354, 42.262481689453125)
4 200 loss: (0.2996847927570343, 35.41472244262695)
4 300 loss: (0.312608003616333, 43.87290954589844)
4 400 loss: (0.2881354093551636, 39.22924041748047)
4 test acc: 0.8671
5 0 loss: (0.27839434146881104, 35.540130615234375)
5 100 loss: (0.2806701958179474, 48.4008903503418)
5 200 loss: (0.2766285836696625, 42.87168884277344)
5 300 loss: (0.2809426784515381, 52.62693786621094)
5 400 loss: (0.26189112663269043, 47.885108947753906)
5 test acc: 0.8735
6 0 loss: (0.2579110264778137, 43.07761764526367)
6 100 loss: (0.2582871615886688, 59.50879669189453)
6 200 loss: (0.28057757019996643, 48.108917236328125)
6 300 loss: (0.27566733956336975, 59.61842346191406)
6 400 loss: (0.25613951683044434, 58.044837951660156)
6 test acc: 0.8777
7 0 loss: (0.2313823103904724, 51.310028076171875)
7 100 loss: (0.2618938088417053, 68.77056884765625)
7 200 loss: (0.2880491614341736, 57.71855163574219)
7 300 loss: (0.2529357075691223, 68.95218658447266)
7 400 loss: (0.22080641984939575, 67.93892669677734)
7 test acc: 0.8817
8 0 loss: (0.2174786627292633, 67.77418518066406)
8 100 loss: (0.24183037877082825, 73.86817932128906)
8 200 loss: (0.2777296304702759, 70.0999755859375)
8 300 loss: (0.24576255679130554, 75.03533935546875)
8 400 loss: (0.22443264722824097, 77.09339904785156)
8 test acc: 0.8838
9 0 loss: (0.1982460469007492, 67.42424011230469)
9 100 loss: (0.18205790221691132, 84.63094329833984)
9 200 loss: (0.2785370349884033, 78.5411376953125)
9 300 loss: (0.2484734058380127, 94.95791625976562)
9 400 loss: (0.20044317841529846, 85.65432739257812)
9 test acc: 0.8833
