一小时学会TensorFlow2之自定义层

目录

• 概述
• Sequential
• Model & Layer
• 案例
• 数据集介绍
• 完整代码

概述

通过自定义网络, 我们可以自己创建网络并和现有的网络串联起来, 从而实现各种各样的网络结构.

Sequential

Sequential 是 Keras 的一个网络容器. 可以帮助我们将多层网络封装在一起.

通过 Sequential 我们可以把现有的层已经我们自己的层实现结合, 一次前向传播就可以实现数据从第一层到最后一层的计算.

格式:

tf.keras.Sequential(layers=None, name=None)

例子:

# 5层网络模型model = tf.keras.Sequential([tf.keras.layers.Dense(256, activation=tf.nn.relu),tf.keras.layers.Dense(128, activation=tf.nn.relu),tf.keras.layers.Dense(64, activation=tf.nn.relu),tf.keras.layers.Dense(32, activation=tf.nn.relu),tf.keras.layers.Dense(10)])

Model & Layer

通过 Model 和 Layer 的__init__和call()我们可以自定义层和模型.

Model:

class My_Model(tf.keras.Model):  # 继承Modeldef __init__(self):\"\"\"初始化\"\"\"super(My_Model, self).__init__()self.fc1 = My_Dense(784, 256)  # 第一层self.fc2 = My_Dense(256, 128)  # 第二层self.fc3 = My_Dense(128, 64)  # 第三层self.fc4 = My_Dense(64, 32)  # 第四层self.fc5 = My_Dense(32, 10)  # 第五层def call(self, inputs, training=None):\"\"\"在Model被调用的时候执行:param inputs: 输入:param training: 默认为None:return: 返回输出\"\"\"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

Layer:

class My_Dense(tf.keras.layers.Layer):  # 继承Layerdef __init__(self, input_dim, output_dim):\"\"\"初始化:param input_dim::param output_dim:\"\"\"super(My_Dense, self).__init__()# 添加变量self.kernel = self.add_variable(\"w\", [input_dim, output_dim])  # 权重self.bias = self.add_variable(\"b\", [output_dim])  # 偏置def call(self, inputs, training=None):\"\"\"在Layer被调用的时候执行, 计算结果:param inputs: 输入:param training: 默认为None:return: 返回计算结果\"\"\"# y = w * x + bout = inputs @ self.kernel + self.biasreturn out

案例

数据集介绍

CIFAR-10 是由 10 类不同的物品组成的 6 万张彩色图片的数据集. 其中 5 万张为训练集, 1 万张为测试集.

完整代码

import tensorflow as tfdef pre_process(x, y):# 转换xx = 2 * tf.cast(x, dtype=tf.float32) / 255 - 1  # 转换为-1~1的形式x = tf.reshape(x, [-1, 32 * 32 * 3])  # 把x铺平# 转换yy = tf.convert_to_tensor(y)  # 转换为0~1的形式y = tf.one_hot(y, depth=10)  # 转成one_hot编码# 返回x, yreturn x, ydef get_data():\"\"\"获取数据:return:\"\"\"# 获取数据(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()# 调试输出维度print(X_train.shape)  # (50000, 32, 32, 3)print(y_train.shape)  # (50000, 1)# squeezey_train = tf.squeeze(y_train)  # (50000, 1) => (50000,)y_test = tf.squeeze(y_test)  # (10000, 1) => (10000,)# 分割训练集train_db = tf.data.Dataset.from_tensor_slices((X_train, y_train)).shuffle(10000, seed=0)train_db = train_db.batch(batch_size).map(pre_process).repeat(iteration_num)  # 迭代20次# 分割测试集test_db = tf.data.Dataset.from_tensor_slices((X_test, y_test)).shuffle(10000, seed=0)test_db = test_db.batch(batch_size).map(pre_process)return train_db, test_dbclass My_Dense(tf.keras.layers.Layer):  # 继承Layerdef __init__(self, input_dim, output_dim):\"\"\"初始化:param input_dim::param output_dim:\"\"\"super(My_Dense, self).__init__()# 添加变量self.kernel = self.add_weight(\"w\", [input_dim, output_dim])  # 权重self.bias = self.add_weight(\"b\", [output_dim])  # 偏置def call(self, inputs, training=None):\"\"\"在Layer被调用的时候执行, 计算结果:param inputs: 输入:param training: 默认为None:return: 返回计算结果\"\"\"# y = w * x + bout = inputs @ self.kernel + self.biasreturn outclass My_Model(tf.keras.Model):  # 继承Modeldef __init__(self):\"\"\"初始化\"\"\"super(My_Model, self).__init__()self.fc1 = My_Dense(32 * 32 * 3, 256)  # 第一层self.fc2 = My_Dense(256, 128)  # 第二层self.fc3 = My_Dense(128, 64)  # 第三层self.fc4 = My_Dense(64, 32)  # 第四层self.fc5 = My_Dense(32, 10)  # 第五层def call(self, inputs, training=None):\"\"\"在Model被调用的时候执行:param inputs: 输入:param training: 默认为None:return: 返回输出\"\"\"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# 定义超参数batch_size = 256  # 一次训练的样本数目learning_rate = 0.001  # 学习率iteration_num = 20  # 迭代次数optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)  # 优化器loss = tf.losses.CategoricalCrossentropy(from_logits=True)  # 损失network = My_Model()  # 实例化网络# 调试输出summarynetwork.build(input_shape=[None, 32 * 32 * 3])print(network.summary())# 组合network.compile(optimizer=optimizer,loss=loss,metrics=[\"accuracy\"])if __name__ == \"__main__\":# 获取分割的数据集train_db, test_db = get_data()# 拟合network.fit(train_db, epochs=5, validation_data=test_db, validation_freq=1)

输出结果:

Model: \”my__model\”
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
my__dense (My_Dense) multiple 786688
_________________________________________________________________
my__dense_1 (My_Dense) multiple 32896
_________________________________________________________________
my__dense_2 (My_Dense) multiple 8256
_________________________________________________________________
my__dense_3 (My_Dense) multiple 2080
_________________________________________________________________
my__dense_4 (My_Dense) multiple 330
=================================================================
Total params: 830,250
Trainable params: 830,250
Non-trainable params: 0
_________________________________________________________________
None
(50000, 32, 32, 3)
(50000, 1)
2021-06-15 14:35:26.600766: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
Epoch 1/5
3920/3920 [==============================] – 39s 10ms/step – loss: 0.9676 – accuracy: 0.6595 – val_loss: 1.8961 – val_accuracy: 0.5220
Epoch 2/5
3920/3920 [==============================] – 41s 10ms/step – loss: 0.3338 – accuracy: 0.8831 – val_loss: 3.3207 – val_accuracy: 0.5141
Epoch 3/5
3920/3920 [==============================] – 41s 10ms/step – loss: 0.1713 – accuracy: 0.9410 – val_loss: 4.2247 – val_accuracy: 0.5122
Epoch 4/5
3920/3920 [==============================] – 41s 10ms/step – loss: 0.1237 – accuracy: 0.9581 – val_loss: 4.9458 – val_accuracy: 0.5050
Epoch 5/5
3920/3920 [==============================] – 42s 11ms/step – loss: 0.1003 – accuracy: 0.9666 – val_loss: 5.2425 – val_accuracy: 0.5097

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