CIFAR10自定义网络实战 - 好文

目录

* CIFAR10 <https://www.cnblogs.com/nickchen121/p/10923333.html#cifar10>
* MyDenseLayer
<https://www.cnblogs.com/nickchen121/p/10923333.html#mydenselayer>
CIFAR10

MyDenseLayer

import os import tensorflow as tf from tensorflow.keras import datasets,
layers, optimizers, Sequential, metrics from tensorflow import keras
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' def preprocess(x, y): # [0, 255] -->
[-1,1] x = 2 * tf.cast(x, dtype=tf.float32) / 255. - 1 y = tf.cast(y,
dtype=tf.int32) return x, y batch_size = 128 # x --> [32,32,3], y --> [10k, 1]
(x, y), (x_val, y_val) = datasets.cifar10.load_data() y = tf.squeeze(y) # [10k,
1] --> [10k] y_val = tf.squeeze(y_val) y = tf.one_hot(y, depth=10) # [50k, 10]
y_val = tf.one_hot(y_val, depth=10) # [10k, 10] print('datasets:', x.shape,
y.shape, x_val.shape, y_val.shape, x.min(), x.max()) train_db =
tf.data.Dataset.from_tensor_slices((x, y)) train_db =
train_db.map(preprocess).shuffle(10000).batch(batch_size) test_db =
tf.data.Dataset.from_tensor_slices((x_val, y_val)) test_db =
test_db.map(preprocess).batch(batch_size) sample = next(iter(train_db))
print('batch:', sample[0].shape, sample[1].shape) class MyDense(layers.Layer):
# to replace standard layers.Dense() 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, 128) self.fc3 =
MyDense(128, 64) self.fc4 = MyDense(64, 32) self.fc5 = MyDense(32, 10) def
call(self, inputs, training=None): """inputs: [b,32,32,32,3]""" x =
tf.reshape(inputs, [-1, 32 * 32 * 3]) # [b,32*32*32] --> [b, 256] x =
self.fc1(x) x = tf.nn.relu(x) # [b, 256] --> [b,128] x = self.fc2(x) x =
tf.nn.relu(x) # [b, 128] --> [b,64] x = self.fc3(x) x = tf.nn.relu(x) # [b, 64]
--> [b,32] x = self.fc4(x) x = tf.nn.relu(x) # [b, 32] --> [b,10] 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=5, validation_data=test_db, validation_freq=1)
network.evaluate(test_db) network.save_weights('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), metircs=['accuracy'])
network.fit(train_db, epochs=5, validation_data=test_db, validation_freq=1)
network.load_weights('weights.ckpt') print('loaded weights from file.')
network.evaluate(test_db) datasets: (50000, 32, 32, 3) (50000, 10) (10000, 32,
32, 3) (10000, 10) 0 255 batch: (128, 32, 32, 3) (128, 10) Epoch 1/5 391/391
[==============================] - 7s 19ms/step - loss: 1.7276 - accuracy:
0.3358 - val_loss: 1.5801 - val_accuracy: 0.4427 Epoch 2/5 391/391
[==============================] - 7s 18ms/step - loss: 1.5045 - accuracy:
0.4606 - val_loss: 1.4808 - val_accuracy: 0.4812 Epoch 3/5 391/391
[==============================] - 6s 17ms/step - loss: 1.3919 - accuracy:
0.5019 - val_loss: 1.4596 - val_accuracy: 0.4921 Epoch 4/5 391/391
[==============================] - 7s 18ms/step - loss: 1.3039 - accuracy:
0.5364 - val_loss: 1.4651 - val_accuracy: 0.4950 Epoch 5/5 391/391
[==============================] - 6s 16ms/step - loss: 1.2270 - accuracy:
0.5622 - val_loss: 1.4483 - val_accuracy: 0.5030 79/79
[==============================] - 1s 11ms/step - loss: 1.4483 - accuracy:
0.5030 saved to ckpt/weights.ckpt Epoch 1/5 391/391
[==============================] - 7s 19ms/step - loss: 1.7216 - val_loss:
1.5773 Epoch 2/5 391/391 [==============================] - 10s 26ms/step -
loss: 1.5010 - val_loss: 1.5111 Epoch 3/5 391/391
[==============================] - 8s 21ms/step - loss: 1.3868 - val_loss:
1.4657 Epoch 4/5 391/391 [==============================] - 8s 20ms/step -
loss: 1.3021 - val_loss: 1.4586 Epoch 5/5 391/391
[==============================] - 7s 17ms/step - loss: 1.2276 - val_loss:
1.4583 loaded weights from file. 79/79 [==============================] - 1s
12ms/step - loss: 1.4483 1.4482733222502697

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