TensorFlow2_200729系列---20、自定义层
一、总结
一句话总结：
继承layers.Layer，初始化方法中可以定义变量，call方法中可以实现神经网络矩阵乘法
# 自定义层（比如之前的全连接dense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
1、自定义神经网络model？
继承keras.Model就好，模型的那些方法都会继承过来，初始化方法和call方法中实现自己的初始化和逻辑
# 自定义model 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
二、自定义层
博客对应课程的视频位置：
import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
from tensorflow import keras
def preprocess(x, y):
"""
x is a simple image, not a batch
"""
x = tf.cast(x, dtype=tf.float32) / 255.
x = tf.reshape(x, [28*28])
y = tf.cast(y, dtype=tf.int32)
y = tf.one_hot(y, depth=10)
return x,y
batchsz = 128
(x, y), (x_val, y_val) = datasets.mnist.load_data()
print('datasets:', x.shape, y.shape, x.min(), x.max())
db = tf.data.Dataset.from_tensor_slices((x,y))
db = db.map(preprocess).shuffle(60000).batch(batchsz)
ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.map(preprocess).batch(batchsz)
# sample = next(iter(db))
# print(sample[0].shape, sample[1].shape)
# network = Sequential([layers.Dense(256, activation='relu'),
# layers.Dense(128, activation='relu'),
# layers.Dense(64, activation='relu'),
# layers.Dense(32, activation='relu'),
# layers.Dense(10)])
# network.build(input_shape=(None, 28*28))
# network.summary()
# 自定义层（比如之前的全连接dense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