• CIFAR10
• 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)
<span class="hljs-keyword">return</span> 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(<span class="hljs-string">'w'</span>, [inp_dim, outp_dim])

#         self.bias = self.add_variable('b', [outp_dim])
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">call</span>(<span class="hljs-params">self, inputs, training=<span class="hljs-literal">None</span></span>):</span>
    x = inputs @ self.kernel
    <span class="hljs-keyword">return</span> 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)
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">call</span>(<span class="hljs-params">self, inputs, training=<span class="hljs-literal">None</span></span>):</span>
    <span class="hljs-string">"""inputs: [b,32,32,32,3]"""</span>
    x = tf.reshape(inputs, [<span class="hljs-number">-1</span>, <span class="hljs-number">32</span> * <span class="hljs-number">32</span> * <span class="hljs-number">3</span>])
    <span class="hljs-comment"># [b,32*32*32] --&gt; [b, 256]</span>
    x = self.fc1(x)
    x = tf.nn.relu(x)
    <span class="hljs-comment"># [b, 256] --&gt; [b,128]</span>
    x = self.fc2(x)
    x = tf.nn.relu(x)
    <span class="hljs-comment"># [b, 128] --&gt; [b,64]</span>
    x = self.fc3(x)
    x = tf.nn.relu(x)
    <span class="hljs-comment"># [b, 64] --&gt; [b,32]</span>
    x = self.fc4(x)
    x = tf.nn.relu(x)
    <span class="hljs-comment"># [b, 32] --&gt; [b,10]</span>
    x = self.fc5(x)

    <span class="hljs-keyword">return</span> 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

标签：实战,loss,自定义,val,CIFAR10,32,self,391,tf
来源： https://www.cnblogs.com/abdm-989/p/14123365.html