1、自定义层
class MyDense(layers.Layer):
def __init__(self, inp_dim, outp_dim):
super(MyDense, self).__init__()
self.kernel = self.add_weight('w', [inp_dim, outp_dim])
self.bias = self.add_weight('b', [outp_dim])
def call(self, inputs, training=None):
out = inputs @ self.kernel + self.bias
return out
2、自定义模型
class MyModel(keras.Model):
def __init__(self):
super(MyModel, self).__init__()
self.fc1 = MyDense(28*28, 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):
x = self.fc1(inputs)
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 = MyModel()
3 模型加载与保存
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)
4 完整code
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
from tensorflow import keras
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
batchsz = 128
# [50k, 32, 32, 3], [10k, 1]
(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) # [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(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)
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):
"""
:param inputs: [b, 32, 32, 3]
:param training:
:return:
"""
x = tf.reshape(inputs, [-1, 32*32*3])
# [b, 32*32*3] => [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=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)
