目录

* CIFAR100 <https://www.cnblogs.com/nickchen121/p/10939750.html#cifar100>
* 13 Layers <https://www.cnblogs.com/nickchen121/p/10939750.html#layers>
* cafar100_train
<https://www.cnblogs.com/nickchen121/p/10939750.html#cafar100_train>
CIFAR100



13 Layers



cafar100_train
import tensorflow as tf from tensorflow.keras import layers, optimizers,
datasets, Sequential import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
conv_layers = [ # 5 units of conv + max polling # unit 1 layers.Conv2D(64,
kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.Conv2D(64,
kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), # unit2
layers.Conv2D(128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.Conv2D(128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), # unit3
layers.Conv2D(256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.Conv2D(256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), # unit4
layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), # unit5
layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), ] def
preprocess(x, y): # [0-1] x = tf.cast(x, dtype=tf.float32) / 255. y =
tf.cast(y, dtype=tf.int32) return x, y (x, y), (x_test, y_test) =
datasets.cifar100.load_data() y = tf.squeeze(y, axis=1) y_test =
tf.squeeze(y_test, axis=1) print(x.shape, y.shape, x_test.shape, y_test.shape)
train_db = tf.data.Dataset.from_tensor_slices((x, y)) train_db =
train_db.shuffle(1000).map(preprocess).batch(64) test_db =
tf.data.Dataset.from_tensor_slices((x_test, y_test)) test_db =
test_db.map(preprocess).batch(64) def main(): # [b,32,32,3]-->[b,1,1,512]
conv_net = Sequential(conv_layers) conv_net.build(input_shape=[None, 32, 32,
3]) # x = tf.random.normal([4, 32, 32, 3]) # out = conv_net(x) #
print(out.shape) fc_net = Sequential([ layers.Dense(256,
activation=tf.nn.relu), layers.Dense(128, activation=tf.nn.relu),
layers.Dense(100, activation=None), ]) conv_net.build(input_shape=[None, 32,
32, 3]) fc_net.build(input_shape=[None, 512]) optimizer =
optimizers.Adam(lr=1e-4) # [1,2]+[3,4] = [1,2,3,4] variables =
conv_net.trainable_variables + fc_net.trainable_variables for epoch in
range(3): for step, (x, y) in enumerate(train_db): with tf.GradientTape() as
tape: # [b,32,32,3] out = conv_net(x) # flatten ==> [b,512] out =
tf.reshape(out, [-1, 512]) # [b,512] --> [b,100] logits = fc_net(out) # [b] -->
[b,100] y_onehot = tf.one_hot(y, depth=100) # compute loss loss =
tf.losses.categorical_crossentropy(y_onehot,logits,from_logits=True) loss =
tf.reduce_mean(loss) grads = tape.gradient(loss,variables)
optimizer.apply_gradients(zip(grads,variables)) if step % 100 ==0:
print(epoch,step,'loss:',float(loss)) total_num = 0 total_correct = 0 for x,y
in test_db: out = conv_net(x) out = tf.reshape(out, [-1, 512]) logits =
fc_net(out) prob = tf.nn.softmax(logits, axis=1) pred = tf.argmax(prob, axis=1)
pred = tf.cast(pred, dtype=tf.int32) correct = tf.cast(tf.equal(pred, y),
dtype=tf.int32) correct = tf.reduce_sum(correct) total_num += x.shape[0]
total_correct += int(correct) acc = total_correct / total_num print(epoch,
'acc:', acc) if __name__ == '__main__': main()