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

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

def preprocess(x,y):
    # 范围[-1,1]
    x = 2 * tf.cast(x,dtype=tf.float32)/255. - 1
    y = tf.cast(y,dtype=tf.int32)
    return x,y


batchsz = 128
(x,y),(x_val,y_val) = datasets.cifar10.load_data()
y = tf.squeeze(y)
y_val = tf.squeeze(y_val)
y = tf.one_hot(y,depth=10)
y_val = tf.one_hot(y_val,depth=10)
print('datasets:',x.shape,y.shape,x_val.shape,y_val.shape)

train_db = tf.data.Dataset.from_tensor_slices((x,y))
train_db = train_db.map(preprocess).shuffle(10000).batch(batchsz)

test_db = tf.data.Dataset.from_tensor_slices((x_val,y_val))
test_db = test_db.map(preprocess).batch(batchsz)

sample = next(iter(train_db))
print('batch:',sample[0].shape,sample[1].shape)

# 自定义Dense 继承自layers.Layer
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,inputs,training=None):
        x = inputs @ self.kernel
        return x

class MyNetwork(keras.Model):
    def __init__(self):
        super(MyNetwork,self).__init__()

        self.fc1 = MyDense(32*32*3,256)
        self.fc2 = MyDense(256,256)
        self.fc3 = MyDense(256, 256)
        self.fc4 = MyDense(256, 256)
        self.fc5 = MyDense(256, 10)

    def call(self,inputs,training=None):
        x = tf.reshape(inputs,[-1,32*32*3])
        x = self.fc1(x)
        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 = MyNetwork()
network.compile(optimizer=optimizers.Adam(lr=1e-3),
                loss=tf.losses.CategoricalCrossentropy(from_logits=True),
                metrics=['accuracy'])
network.fit(train_db,epochs=15,validation_data=test_db,validation_freq=1)

network.evaluate(test_db)
network.save_weights('ckpt/weights.ckpt')
del network

print('saved to ckpt/weights.ckpt')

network = MyNetwork()
network.compile(optimizer=optimizers.Adam(lr=1e-3),
                loss=tf.losses.CategoricalCrossentropy(from_logits=True),
                metrics=['accuracy'])
network.load_weights('ckpt/weights.ckpt')
print('loaded weights from file')
network.evaluate(test_db)
