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‎02_Keras/02_01_The_sequential_model.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"WARNING:tensorflow:From c:\\Users\\yuanshuai\\AppData\\Local\\anaconda3\\envs\\tensorflow2\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
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"\n"
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]
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}
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],
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"source": [
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"import tensorflow as tf\n",
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"import keras\n",
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"from keras import layers\n",
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"\n",
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"### Sequential模型"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"WARNING:tensorflow:From c:\\Users\\yuanshuai\\AppData\\Local\\anaconda3\\envs\\tensorflow2\\Lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
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"\n"
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]
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}
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],
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"source": [
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"### 2.1.1 何时使用顺序模型\n",
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"#序列模型适用于每个层恰好有一个输入张量和一个输出张量的普通层堆栈。\n",
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"\n",
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"#示意性地，以下模型：Sequential\n",
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"# Define Sequential model with 3 layers\n",
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"model = keras.Sequential(\n",
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" [\n",
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" layers.Dense(2, activation=\"relu\", name=\"layer1\"),\n",
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" layers.Dense(3, activation=\"relu\", name=\"layer2\"),\n",
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" layers.Dense(4, name=\"layer3\"),\n",
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" ]\n",
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")\n",
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"# Call model on a test input\n",
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"x = tf.ones((3, 3))\n",
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"y = model(x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"#等效于此函数：\n",
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"# Create 3 layers\n",
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"layer1 = layers.Dense(2, activation=\"relu\", name=\"layer1\")\n",
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"layer2 = layers.Dense(3, activation=\"relu\", name=\"layer2\")\n",
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"layer3 = layers.Dense(4, name=\"layer3\")\n",
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"\n",
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"# Call layers on a test input\n",
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"x = tf.ones((3, 3))\n",
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"y = layer3(layer2(layer1(x)))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#TODO\n",
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"#INFO: demo用，单输入单输出 "
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"## 在以下情况下，顺序模型不适用：\n",
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"# 您的模型具有多个输入或多个输出\n",
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"# 任何图层都具有多个输入或多个输出\n",
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"# 您需要进行图层共享\n",
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"# 您需要非线性拓扑（例如残差连接、多分支 模型）\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"### 2.1.2 创建顺序模型\n",
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"#您可以通过将层列表传递给 Sequentiald的构造函数来创建 Sequential模型：\n",
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"model = keras.Sequential(\n",
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" [\n",
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" layers.Dense(2, activation=\"relu\"),\n",
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" layers.Dense(3, activation=\"relu\"),\n",
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" layers.Dense(4),\n",
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" ]\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<keras.src.layers.core.dense.Dense at 0x1ba1a83a010>,\n",
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" <keras.src.layers.core.dense.Dense at 0x1ba1a6e1410>,\n",
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" <keras.src.layers.core.dense.Dense at 0x1ba18782190>]"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#其图层可通过以下属性访问：layers\n",
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"model.layers"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"#您还可以通过以下方法以增量方式创建顺序模型：add()\n",
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"model = keras.Sequential()\n",
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"model.add(layers.Dense(2, activation=\"relu\"))\n",
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"model.add(layers.Dense(3, activation=\"relu\"))\n",
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"model.add(layers.Dense(4))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2\n"
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]
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}
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],
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"source": [
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"#请注意，还有一种相应的方法可以删除图层： 顺序模型的行为非常类似于层列表。pop()\n",
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"model.pop()\n",
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"print(len(model.layers)) # 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"#另请注意，Sequential 构造函数接受一个参数，就像 Keras 中的任何图层或模型。\n",
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"#这对于注释 TensorBoard 图形很有用 具有语义上有意义的名称。name\n",
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"\n",
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"model = keras.Sequential(name=\"my_sequential\")\n",
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"model.add(layers.Dense(2, activation=\"relu\", name=\"layer1\"))\n",
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"model.add(layers.Dense(3, activation=\"relu\", name=\"layer2\"))\n",
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"model.add(layers.Dense(4, name=\"layer3\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[]"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"### 2.1.3 提前指定输入形状\n",
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"#通常，Keras中的所有层都需要知道其输入的形状，以便能够创建其权重。\n",
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"#因此，当您创建这样的层时，最初它没有权重：\n",
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"layer = layers.Dense(3)\n",
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"layer.weights # Empty"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<tf.Variable 'dense_6/kernel:0' shape=(4, 3) dtype=float32, numpy=\n",
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" array([[ 0.51047623, -0.17564982, -0.6607744 ],\n",
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" [ 0.7689657 , 0.17553306, 0.82802343],\n",
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" [-0.13806897, -0.24232322, 0.18856204],\n",
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" [ 0.36269712, -0.23856878, -0.25139934]], dtype=float32)>,\n",
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" <tf.Variable 'dense_6/bias:0' shape=(3,) dtype=float32, numpy=array([0., 0., 0.], dtype=float32)>]"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#它在第一次调用输入时创建其权重，因为形状 权重取决于输入的形状：\n",
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"# Call layer on a test input\n",
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"x = tf.ones((1, 4))\n",
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"y = layer(x)\n",
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"layer.weights # Now it has weights, of shape (4, 3) and (3,)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Number of weights after calling the model: 6\n"
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]
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}
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],
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"source": [
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"#当然，这也适用于Sequential模型。当您实例化一个没有输入形状的Sequential模型时，\n",
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"#它不是“构建”的：它没有权重（调用model.weights会导致一个错误，说明这一点）。\n",
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"#权重是在模型第一次看到一些输入数据时创建的：\n",
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"model = keras.Sequential(\n",
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" [\n",
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" layers.Dense(2, activation=\"relu\"),\n",
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" layers.Dense(3, activation=\"relu\"),\n",
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" layers.Dense(4),\n",
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" ]\n",
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") # No weights at this stage!\n",
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"\n",
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"# At this point, you can't do this:\n",
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"# model.weights\n",
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"\n",
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"# You also can't do this:\n",
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"# model.summary()\n",
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"\n",
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"# Call the model on a test input\n",
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"x = tf.ones((1, 4))\n",
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"y = model(x)\n",
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"print(\"Number of weights after calling the model:\", len(model.weights)) # 6"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Model: \"sequential_3\"\n",
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"_________________________________________________________________\n",
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" Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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" dense_7 (Dense) (1, 2) 10 \n",
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" \n",
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" dense_8 (Dense) (1, 3) 9 \n",
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" \n",
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" dense_9 (Dense) (1, 4) 16 \n",
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" \n",
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"=================================================================\n",
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"Total params: 35 (140.00 Byte)\n",
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"Trainable params: 35 (140.00 Byte)\n",
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"Non-trainable params: 0 (0.00 Byte)\n",
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"_________________________________________________________________\n"
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]
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}
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],
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"source": [
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"#“构建”模型后，可以调用其方法来显示其 内容：summary()\n",
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"model.summary()"
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]
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}
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],
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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