一、讲解

 

 

 

 

 

二、代码

import tensorflow as tf from tensorflow.python.keras import datasets, layers, optimizers, Sequential, metrics from tensorflow.python import keras import os

os.environ[‘TF_CPP_MIN_LOG_LEVEL‘] = ‘2‘

def preprocess(x, y): """ x is a simple image, not a batch
:param x:
:param y:
:return: """ 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)

iteration = iter(db)
sample = next(iteration) print("迭代器获得为：", sample[0].shape, sample[1].shape) class MyDense(layers.Layer): def __init__(self, inp_dim, outp_dim):
super(MyDense, self).__init__()

    self.kernel \= self.add\_variable(‘w‘, \[inp\_dim, outp\_dim\])
    self.bias \= self.add\_variable(‘b‘, \[outp\_dim\]) def call(self, input, training=None):
    out \= input @ self.kernel + self.bias return out class MyModel(keras.Model): def \_\_init\_\_(self):
    super(MyModel, self).\_\_init\_\_()

    self.fc1 \= MyDense(28\*28, 256)
    self.fc2 \= MyDense(256, 128)
    self.fc3 \= MyDense(128, 64)
    self.fc4 \= MyDense(64, 32)
    self.fc5 \= MyDense(32, 10) def call(self, inputs, training=None):

    x \= self.fc1(inputs)  ##fc1为一个instance,默认调用\_\_call\_\_()==> call()
    x = tf.nn.relu(x)
    x \= self.fc2(x)
    x \= tf.nn.relu(x)
    x \= self.fc3(x)
    x \= tf.nn.relu(x)
    x \= self.fc4(x)
    x \= tf.nn.relu(x)
    x \= self.fc5(x) return x

network = MyModel()

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)

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) # [b, 1]
pred = tf.argmax(pred, axis=1) print(pred) print(y)