文章目录

  • CIFAR10自定义网络实战

CIFAR10自定义网络实战


import osos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics# 数据预处理
def preprocess(x,y):# [-1,1]x = 2 * tf.cast(x,dtype=tf.float32) / 255. - 1y = tf.cast(y,dtype=tf.int32)return x,ybatchsz = 128
# 数据集的加载
# x[b,32,32,3]  y[b,1]
(x,y),(x_val,y_val) = datasets.cifar10.load_data()# 消去[b,1]的1这个维度
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.min(),x.max())
# datasets: (50000, 32, 32, 3) (50000, 10) 0 255# 构建两个数据集
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)# 创建自己的层
# replace standard layers.Dense
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])# 构建前向传播def call(self,input,training = None):x = input @ self.kernelreturn x# 构建自定义网络(5层)
class MyNetwork(keras.Model):def __init__(self):super(MyNetwork,self).__init__()# 优化-使参数变大-但容易造成过拟合self.fc1 = MyDense(32*32*3,256)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):""":param inputs: [b,32,32,3]:param training::return:"""# 打平操作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[b,32]->[b,10]x = self.fc5(x)return xnetwork = 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)# 保存模型权值
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'])# 加载模型权值
network.load_weights('ckpt/weights.ckpt')
print('load weights from file')
network.evaluate(test_db)

Epoch 14/151/391 [..............................] - ETA: 2:59 - loss: 0.6248 - accuracy: 0.80478/391 [..............................] - ETA: 24s - loss: 0.6025 - accuracy: 0.7744 14/391 [>.............................] - ETA: 15s - loss: 0.5613 - accuracy: 0.795220/391 [>.............................] - ETA: 11s - loss: 0.5669 - accuracy: 0.796926/391 [>.............................] - ETA: 9s - loss: 0.5580 - accuracy: 0.8029 32/391 [=>............................] - ETA: 8s - loss: 0.5757 - accuracy: 0.793238/391 [=>............................] - ETA: 7s - loss: 0.5719 - accuracy: 0.792644/391 [==>...........................] - ETA: 6s - loss: 0.5721 - accuracy: 0.793350/391 [==>...........................] - ETA: 5s - loss: 0.5669 - accuracy: 0.796256/391 [===>..........................] - ETA: 5s - loss: 0.5710 - accuracy: 0.793962/391 [===>..........................] - ETA: 5s - loss: 0.5740 - accuracy: 0.794168/391 [====>.........................] - ETA: 4s - loss: 0.5731 - accuracy: 0.794575/391 [====>.........................] - ETA: 4s - loss: 0.5753 - accuracy: 0.792281/391 [=====>........................] - ETA: 4s - loss: 0.5745 - accuracy: 0.793688/391 [=====>........................] - ETA: 4s - loss: 0.5727 - accuracy: 0.793694/391 [======>.......................] - ETA: 3s - loss: 0.5742 - accuracy: 0.7927
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376/391 [===========================>..] - ETA: 0s - loss: 0.5684 - accuracy: 0.7957
389/391 [============================>.] - ETA: 0s - loss: 0.5698 - accuracy: 0.7956
391/391 [==============================] - 4s 10ms/step - loss: 0.5697 - accuracy: 0.7956 - val_loss: 1.9200 - val_accuracy: 0.5195
Epoch 15/151/391 [..............................] - ETA: 2:55 - loss: 0.6455 - accuracy: 0.78128/391 [..............................] - ETA: 24s - loss: 0.5190 - accuracy: 0.8135 15/391 [>.............................] - ETA: 14s - loss: 0.5051 - accuracy: 0.816122/391 [>.............................] - ETA: 10s - loss: 0.4930 - accuracy: 0.822429/391 [=>............................] - ETA: 8s - loss: 0.4935 - accuracy: 0.8217 36/391 [=>............................] - ETA: 7s - loss: 0.4941 - accuracy: 0.823843/391 [==>...........................] - ETA: 6s - loss: 0.4999 - accuracy: 0.821250/391 [==>...........................] - ETA: 5s - loss: 0.5044 - accuracy: 0.818157/391 [===>..........................] - ETA: 5s - loss: 0.5097 - accuracy: 0.817764/391 [===>..........................] - ETA: 4s - loss: 0.5112 - accuracy: 0.817471/391 [====>.........................] - ETA: 4s - loss: 0.5097 - accuracy: 0.816878/391 [====>.........................] - ETA: 4s - loss: 0.5115 - accuracy: 0.817285/391 [=====>........................] - ETA: 4s - loss: 0.5161 - accuracy: 0.814892/391 [======>.......................] - ETA: 3s - loss: 0.5176 - accuracy: 0.814599/391 [======>.......................] - ETA: 3s - loss: 0.5187 - accuracy: 0.8149
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383/391 [============================>.] - ETA: 0s - loss: 0.5197 - accuracy: 0.8126
391/391 [==============================] - 4s 10ms/step - loss: 0.5200 - accuracy: 0.8126 - val_loss: 2.0124 - val_accuracy: 0.51891/79 [..............................] - ETA: 0s - loss: 1.6155 - accuracy: 0.5625
10/79 [==>...........................] - ETA: 0s - loss: 1.8749 - accuracy: 0.5273
19/79 [======>.......................] - ETA: 0s - loss: 1.9776 - accuracy: 0.5169
27/79 [=========>....................] - ETA: 0s - loss: 1.9817 - accuracy: 0.5194
36/79 [============>.................] - ETA: 0s - loss: 1.9576 - accuracy: 0.5252
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63/79 [======================>.......] - ETA: 0s - loss: 1.9572 - accuracy: 0.5255
72/79 [==========================>...] - ETA: 0s - loss: 1.9786 - accuracy: 0.5215
79/79 [==============================] - 0s 6ms/step - loss: 2.0124 - accuracy: 0.5189
saved to ckpt/weights.ckpt
load weights from file1/79 [..............................] - ETA: 5s - loss: 1.6155 - accuracy: 0.5625
10/79 [==>...........................] - ETA: 0s - loss: 1.8749 - accuracy: 0.5273
19/79 [======>.......................] - ETA: 0s - loss: 1.9776 - accuracy: 0.5169
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37/79 [=============>................] - ETA: 0s - loss: 1.9426 - accuracy: 0.5283
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64/79 [=======================>......] - ETA: 0s - loss: 1.9604 - accuracy: 0.5242
73/79 [==========================>...] - ETA: 0s - loss: 1.9814 - accuracy: 0.5205
79/79 [==============================] - 1s 7ms/step - loss: 2.0124 - accuracy: 0.5189

在不使用卷积神经网络的情况下，效果也就这样

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