### 1、complie
**因为在深度学习中，很多代码都有重复性，所以用complie组件，代替了重复代码**
```py
network.compile(
optimizer=optimizers.Adam(lr=0.01),
loss=tf.losses.CategoricalCrossentropy(from_logits=True),
metrics=['accuracy']
)
```
### 2、fit
**训练数据，省略epoch 和step的操作,同时增加test data accuracy的验证**
```py
network.fit(db, epochs=5, validation_data=ds_val, validation_freq=2)
```
### 3、evaluate 评估模型
**<font color='red'>注意：</font>在fit的时候已经验证了test_data accuracy，为什么最后还要验证？**
**因为fit的时候验证的模型结果，是为了在循环中，更好的观察模型，当accuracy比较好的时候，就可以通过代码来判断，停止fit循环**
```py
network.evaluate(ds_val)
```
### 4、完整代码
```py
import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
def preprocess(x, y):
"""
x is a simple image, not a batch
"""
x = tf.cast(x, dtype=tf.float32) / 255.
x = tf.reshape(x, [28*28])
y = tf.cast(y, dtype=tf.int32)
y = tf.one_hot(y, depth=10)
return x,y
batchsz = 128
(x, y), (x_val, y_val) = datasets.mnist.load_data()
print('datasets:', x.shape, y.shape, x.min(), x.max())
db = tf.data.Dataset.from_tensor_slices((x,y))
db = db.map(preprocess).shuffle(60000).batch(batchsz)
ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.map(preprocess).batch(batchsz)
sample = next(iter(db))
print(sample[0].shape, sample[1].shape)
network = Sequential([layers.Dense(256, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dense(64, activation='relu'),
layers.Dense(32, activation='relu'),
layers.Dense(10)])
network.build(input_shape=(None, 28*28))
network.summary()
network.compile(optimizer=optimizers.Adam(lr=0.01),
loss=tf.losses.CategoricalCrossentropy(from_logits=True),
metrics=['accuracy']
)
network.fit(db, epochs=5, validation_data=ds_val, validation_freq=2)
network.evaluate(ds_val)
# 提供数据，让模型进行预测
sample = next(iter(ds_val))
x = sample[0]
y = sample[1] # one-hot
pred = network.predict(x) # [b, 10]
# convert back to number
y = tf.argmax(y, axis=1)
pred = tf.argmax(pred, axis=1)
print(pred)
print(y)
```
Tensorflow : complie & fit
