routine
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4
.gitignore
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4
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**/.ipynb_checkpoints
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.vscode
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.vscode
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lab/data
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@@ -2,20 +2,70 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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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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"/Users/wolves/Downloads/project/python/AI-learning/lab\n"
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]
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}
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],
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"source": [
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"import os\n",
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"print(os.getcwd())"
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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": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-01-20T09:22:08.756378Z",
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"start_time": "2025-01-20T09:22:08.738301Z"
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}
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},
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"outputs": [],
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"source": [
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"# 导库\n",
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"from sklearn.datasets import make_classification\n",
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"import tensorflow as tf\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from matplotlib.lines import Line2D\n",
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"from matplotlib.colors import ListedColormap\n",
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"from tensorflow.keras.layers import Dense\n",
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"\n",
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"# 使用GPU 6\n",
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"from sklearn.metrics import accuracy_score\n",
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"from tensorflow.keras.models import Sequential"
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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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"source": [
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"# 限制每个GPU使用4GB显存 通用gpu启用\n",
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"gpus = tf.config.experimental.list_physical_devices('GPU')\n",
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"if gpus:\n",
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" try:\n",
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" for gpu in gpus:\n",
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" tf.config.experimental.set_virtual_device_configuration(\n",
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" gpu,\n",
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" [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=4096)] # 限制每个GPU使用4GB显存\n",
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" )\n",
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" except RuntimeError as e:\n",
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" print(e)"
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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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"# 使用GPU 6 cuda启用\n",
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"gpus = tf.config.experimental.list_physical_devices('GPU')\n",
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"if gpus:\n",
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" try:\n",
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@@ -85,13 +135,14 @@
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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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"execution_count": 17,
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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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"模型运行在GPU设备: /device:GPU:0\n",
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"Epoch 1/100\n"
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]
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},
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@@ -99,287 +150,255 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
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"I0000 00:00:1736497931.531534 21216 service.cc:146] XLA service 0x7f49f4954650 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
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"I0000 00:00:1736497931.531557 21216 service.cc:154] StreamExecutor device (0): NVIDIA A800 80GB PCIe, Compute Capability 8.0\n",
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"2025-01-10 08:32:11.540274: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
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"2025-01-10 08:32:11.571961: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 90600\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"I0000 00:00:1736497931.646086 21216 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n"
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"2025-01-20 17:58:23.192972: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
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"2025-01-20 17:58:23.192998: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
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]
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},
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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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"2/2 [==============================] - 2s 10ms/step - loss: 0.7364\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 103ms/step - loss: 60.6063\n",
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"Epoch 2/100\n",
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"2/2 [==============================] - 0s 5ms/step - loss: 0.7210\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 28.2659\n",
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"Epoch 3/100\n",
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"2/2 [==============================] - 0s 6ms/step - loss: 0.7066\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 1.4893\n",
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"Epoch 4/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.6929\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 4.0465\n",
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"Epoch 5/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.6786\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 6.5380\n",
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"Epoch 6/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.6668\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 8.9314\n",
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"Epoch 7/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.6531\n",
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||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 8.9564\n",
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"Epoch 8/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.6411\n",
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"Epoch 9/100\n"
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]
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},
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{
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||||
"name": "stderr",
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||||
"output_type": "stream",
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"text": [
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"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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||||
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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||||
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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||||
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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||||
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
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||||
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n"
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||||
]
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||||
},
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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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"2/2 [==============================] - 0s 7ms/step - loss: 0.6245\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 10.6051\n",
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"Epoch 9/100\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 12.0071\n",
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"Epoch 10/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.6015\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 10.6521\n",
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"Epoch 11/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.5786\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 14.1705\n",
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"Epoch 12/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.5240\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 13.1806\n",
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"Epoch 13/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.5058\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 12.1059\n",
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"Epoch 14/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.4971\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 13.4850\n",
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"Epoch 15/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.4868\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 12.3003\n",
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"Epoch 16/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.4798\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 11.7990\n",
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"Epoch 17/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.4722\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 10.3683\n",
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"Epoch 18/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.4654\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 9.7598 \n",
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"Epoch 19/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.4600\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 10.2508\n",
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"Epoch 20/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.4560\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 8.2063\n",
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"Epoch 21/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.4506\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 6.3501\n",
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"Epoch 22/100\n",
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"2/2 [==============================] - 0s 6ms/step - loss: 0.4460\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 6.1186\n",
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"Epoch 23/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.4432\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 4.8664\n",
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"Epoch 24/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.4404\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 4.0431\n",
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"Epoch 25/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.4380\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 3.5386\n",
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"Epoch 26/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.4349\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 2.7600\n",
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"Epoch 27/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.4335\n",
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"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 1.3724\n",
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"Epoch 28/100\n",
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"2/2 [==============================] - 0s 7ms/step - loss: 0.4313\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.5045\n",
|
||||
"Epoch 29/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4301\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 1.9855\n",
|
||||
"Epoch 30/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4293\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 1.0879\n",
|
||||
"Epoch 31/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4278\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.6851\n",
|
||||
"Epoch 32/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4270\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 1.2047\n",
|
||||
"Epoch 33/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4259\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 1.5541\n",
|
||||
"Epoch 34/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4257\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 1.2758\n",
|
||||
"Epoch 35/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4249\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 1.3003\n",
|
||||
"Epoch 36/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4245\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.8837\n",
|
||||
"Epoch 37/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4247\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.5647\n",
|
||||
"Epoch 38/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4239\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.6855\n",
|
||||
"Epoch 39/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4238\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.5609\n",
|
||||
"Epoch 40/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4238\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.4828\n",
|
||||
"Epoch 41/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4232\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 0.6055\n",
|
||||
"Epoch 42/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4232\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.5680\n",
|
||||
"Epoch 43/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4232\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.3890\n",
|
||||
"Epoch 44/100\n",
|
||||
"2/2 [==============================] - 0s 8ms/step - loss: 0.4229\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.4543\n",
|
||||
"Epoch 45/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4229\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.4878\n",
|
||||
"Epoch 46/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4230\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.4287\n",
|
||||
"Epoch 47/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4229\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.4933\n",
|
||||
"Epoch 48/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.5080\n",
|
||||
"Epoch 49/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4485\n",
|
||||
"Epoch 50/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.4195\n",
|
||||
"Epoch 51/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4215\n",
|
||||
"Epoch 52/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.4477\n",
|
||||
"Epoch 53/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 0.4759\n",
|
||||
"Epoch 54/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4077\n",
|
||||
"Epoch 55/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.4073\n",
|
||||
"Epoch 56/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.3936\n",
|
||||
"Epoch 57/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4006\n",
|
||||
"Epoch 58/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4254\n",
|
||||
"Epoch 59/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.4196\n",
|
||||
"Epoch 60/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.4221\n",
|
||||
"Epoch 61/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.4271\n",
|
||||
"Epoch 62/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4239\n",
|
||||
"Epoch 63/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.3905\n",
|
||||
"Epoch 64/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4415\n",
|
||||
"Epoch 65/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.4230\n",
|
||||
"Epoch 66/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.3950\n",
|
||||
"Epoch 67/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.4236\n",
|
||||
"Epoch 68/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4291\n",
|
||||
"Epoch 69/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4066\n",
|
||||
"Epoch 70/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 0.4228\n",
|
||||
"Epoch 71/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.3788\n",
|
||||
"Epoch 72/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.4248\n",
|
||||
"Epoch 73/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4265\n",
|
||||
"Epoch 74/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4165\n",
|
||||
"Epoch 75/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4262\n",
|
||||
"Epoch 76/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4238\n",
|
||||
"Epoch 77/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4055\n",
|
||||
"Epoch 78/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4483\n",
|
||||
"Epoch 79/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4379\n",
|
||||
"Epoch 80/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4697\n",
|
||||
"Epoch 81/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4096\n",
|
||||
"Epoch 82/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.4069\n",
|
||||
"Epoch 83/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.4518\n",
|
||||
"Epoch 84/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.4145\n",
|
||||
"Epoch 85/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.3853\n",
|
||||
"Epoch 86/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.4064\n",
|
||||
"Epoch 87/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.4533\n",
|
||||
"Epoch 88/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.4798\n",
|
||||
"Epoch 89/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.4093\n",
|
||||
"Epoch 90/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.4176\n",
|
||||
"Epoch 91/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.4351\n",
|
||||
"Epoch 92/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4422\n",
|
||||
"Epoch 93/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4550\n",
|
||||
"Epoch 94/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.4387\n",
|
||||
"Epoch 95/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.4847\n",
|
||||
"Epoch 96/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4777\n",
|
||||
"Epoch 97/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4309\n",
|
||||
"Epoch 98/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4221\n",
|
||||
"Epoch 99/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.4159\n",
|
||||
"Epoch 100/100\n",
|
||||
"2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
|
||||
"2/2 [==============================] - 0s 2ms/step\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.3821\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
|
||||
"模型的精准度: 0.85\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 输出模型当时是在什么设备上运行\n",
|
||||
"device_name = tf.test.gpu_device_name()\n",
|
||||
"if device_name:\n",
|
||||
" print(f\"模型运行在GPU设备: {device_name}\")\n",
|
||||
"else:\n",
|
||||
" print(\"模型运行在CPU设备\")\n",
|
||||
"\n",
|
||||
"# 写法1\n",
|
||||
"# model = tf.keras.Sequential()\n",
|
||||
"# layer_1 = Dense(units=3,activation='sigmoid')\n",
|
||||
"# layer_2 = Dense(units=1,activation='sigmoid')\n",
|
||||
"# model.add(layer_1)\n",
|
||||
"# model.add(layer_2)\n",
|
||||
"\n",
|
||||
"#写法2\n",
|
||||
"# layer_1 = Dense(units=3,activation='sigmoid')\n",
|
||||
"# layer_2 = Dense(units=1,activation='sigmoid')\n",
|
||||
"# model = Sequential([layer_1, layer_2])\n",
|
||||
"\n",
|
||||
"# 写法3\n",
|
||||
"model = Sequential([Dense(units=10,activation='relu'),\n",
|
||||
" Dense(units=5,activation='relu'),\n",
|
||||
" Dense(units=1,activation='sigmoid')])\n",
|
||||
"\n",
|
||||
"model = tf.keras.Sequential()\n",
|
||||
"layer_1 = Dense(units=3,activation='sigmoid')\n",
|
||||
"layer_2 = Dense(units=1,activation='sigmoid')\n",
|
||||
"model.add(layer_1)\n",
|
||||
"model.add(layer_2)\n",
|
||||
"model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.01),loss='binary_crossentropy')\n",
|
||||
"model.fit(x,y,epochs=100)\n",
|
||||
"a = model.predict(x)\n",
|
||||
"\n",
|
||||
"# 计算并输出模型的精准度\n",
|
||||
"from sklearn.metrics import accuracy_score\n",
|
||||
"\n",
|
||||
"# 将预测结果转换为二进制标签\n",
|
||||
"predicted_labels = (a > 0.5).astype(int)\n",
|
||||
"\n",
|
||||
"# 计算精准度\n",
|
||||
"accuracy = accuracy_score(y, predicted_labels)\n",
|
||||
"print(f\"模型的精准度: {accuracy:.2f}\")\n",
|
||||
"\n",
|
||||
"\n"
|
||||
"print(f\"模型的精准度: {accuracy:.2f}\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -388,6 +407,18 @@
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.16"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -2,8 +2,54 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-01-20T07:32:36.354335Z",
|
||||
"start_time": "2025-01-20T07:32:35.224080Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import torch\n",
|
||||
"import torch.nn as nn"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"# 设置使用gpu7 cuda\n",
|
||||
"device = torch.device(\"cuda:7\" if torch.cuda.is_available() and torch.cuda.get_device_properties(0).total_memory >= 6*1024**3 else \"cpu\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-01-20T07:32:38.297401Z",
|
||||
"start_time": "2025-01-20T07:32:38.261009Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 设置使用mps mps设备当前未支持限制内存\n",
|
||||
"device = torch.device(\"mps\" if torch.backends.mps.is_available() else \"cpu\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-01-20T07:32:39.972353Z",
|
||||
"start_time": "2025-01-20T07:32:39.958549Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
@@ -20,12 +66,6 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import torch\n",
|
||||
"import torch.nn as nn\n",
|
||||
"\n",
|
||||
"# 设置使用gpu7\n",
|
||||
"device = torch.device(\"cuda:7\" if torch.cuda.is_available() else \"cpu\")\n",
|
||||
"\n",
|
||||
"# 定义一个简单的神经元层\n",
|
||||
"class MultiLayerPerceptron(nn.Module):\n",
|
||||
" def __init__(self, input_size, hidden_size1, hidden_size2, output_size):\n",
|
||||
@@ -55,7 +95,7 @@
|
||||
"mlp = MultiLayerPerceptron(input_size=10, hidden_size1=20, hidden_size2=10, output_size=2)\n",
|
||||
"\n",
|
||||
"# 打印模型结构\n",
|
||||
"print(mlp)\n"
|
||||
"print(mlp)"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -75,7 +115,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.9"
|
||||
"version": "3.10.14"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
327
lab/7_Muticlass.ipynb
Normal file
327
lab/7_Muticlass.ipynb
Normal file
@@ -0,0 +1,327 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 多分类问题 - 手写数字识别\n",
|
||||
"\n",
|
||||
"## 数据集\n",
|
||||
"- minst数据集(手写数字数据集)\n",
|
||||
"\n",
|
||||
"## 激活函数\n",
|
||||
"- softmax\n",
|
||||
"\n",
|
||||
"## 损失函数\n",
|
||||
"- 交叉熵\n",
|
||||
"\n",
|
||||
"## 优化器\n",
|
||||
"- 梯度下降\n",
|
||||
"\n",
|
||||
"## 模型\n",
|
||||
"- 全连接层\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 导库\n",
|
||||
"import tensorflow as tf\n",
|
||||
"from tensorflow.keras import Sequential\n",
|
||||
"from tensorflow.keras.layers import Dense\n",
|
||||
"from tensorflow.keras.losses import SparseCategoricalCrossentropy"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
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"text/html": [
|
||||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
|
||||
"</pre>\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
|
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]
|
||||
},
|
||||
"metadata": {},
|
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"output_type": "display_data"
|
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},
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
|
||||
"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃\n",
|
||||
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
|
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"│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ ? │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
|
||||
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
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"│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ ? │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
|
||||
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
||||
"│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ ? │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
|
||||
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
|
||||
"</pre>\n"
|
||||
],
|
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"text/plain": [
|
||||
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
|
||||
"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
|
||||
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
|
||||
"│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
|
||||
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
||||
"│ dense_5 (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
|
||||
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
||||
"│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
|
||||
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
|
||||
"</pre>\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
|
||||
"</pre>\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
|
||||
"</pre>\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"None\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# TEST\n",
|
||||
"model = Sequential([Dense(units=25,activation='relu'),\n",
|
||||
" Dense(units=15,activation='relu'),\n",
|
||||
" Dense(units=10,activation='softmax')])\n",
|
||||
"model.compile(loss=SparseCategoricalCrossentropy())\n",
|
||||
"# 输出模型\n",
|
||||
"print(model.summary())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1/5\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2025-01-21 11:03:07.974903: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 8ms/step - accuracy: 0.8340 - loss: 0.5514\n",
|
||||
"Epoch 2/5\n",
|
||||
"\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 8ms/step - accuracy: 0.8933 - loss: 0.3836\n",
|
||||
"Epoch 3/5\n",
|
||||
"\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 8ms/step - accuracy: 0.8902 - loss: 0.4045\n",
|
||||
"Epoch 4/5\n",
|
||||
"\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 8ms/step - accuracy: 0.8889 - loss: 0.4049\n",
|
||||
"Epoch 5/5\n",
|
||||
"\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 8ms/step - accuracy: 0.8840 - loss: 0.4177\n",
|
||||
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9039 - loss: 0.3451\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[0.30404922366142273, 0.9150999784469604]"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 官方实例\n",
|
||||
"\n",
|
||||
"import tensorflow as tf\n",
|
||||
"mnist = tf.keras.datasets.mnist\n",
|
||||
"\n",
|
||||
"(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
|
||||
"x_train, x_test = x_train / 255.0, x_test / 255.0\n",
|
||||
"\n",
|
||||
"model = tf.keras.models.Sequential([\n",
|
||||
" tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
|
||||
" tf.keras.layers.Dense(128, activation='relu'),\n",
|
||||
" tf.keras.layers.Dropout(0.2),\n",
|
||||
" tf.keras.layers.Dense(10, activation='softmax')\n",
|
||||
"])\n",
|
||||
"\n",
|
||||
"model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
|
||||
" loss=SparseCategoricalCrossentropy(),\n",
|
||||
" metrics=['accuracy'])\n",
|
||||
"\n",
|
||||
"model.fit(x_train, y_train, epochs=5)\n",
|
||||
"model.evaluate(x_test, y_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1/5 - Loss: 0.4415533185513543\n",
|
||||
"Epoch 2/5 - Loss: 0.24540251612599726\n",
|
||||
"Epoch 3/5 - Loss: 0.19663310029716696\n",
|
||||
"Epoch 4/5 - Loss: 0.17048093510557338\n",
|
||||
"Epoch 5/5 - Loss: 0.16070798563876196\n",
|
||||
"Accuracy: 95.38%\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 用torch实现\n",
|
||||
"\n",
|
||||
"import torch\n",
|
||||
"import torch.nn as nn\n",
|
||||
"import torch.optim as optim\n",
|
||||
"from torchvision import datasets, transforms\n",
|
||||
"\n",
|
||||
"# 数据预处理\n",
|
||||
"transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\n",
|
||||
"\n",
|
||||
"# 加载数据集\n",
|
||||
"trainset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
|
||||
"trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
|
||||
"\n",
|
||||
"testset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)\n",
|
||||
"testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)\n",
|
||||
"\n",
|
||||
"# 定义模型\n",
|
||||
"class SimpleNet(nn.Module):\n",
|
||||
" def __init__(self):\n",
|
||||
" super(SimpleNet, self).__init__()\n",
|
||||
" self.flatten = nn.Flatten()\n",
|
||||
" self.fc1 = nn.Linear(28 * 28, 128)\n",
|
||||
" self.dropout = nn.Dropout(0.2)\n",
|
||||
" self.fc2 = nn.Linear(128, 10)\n",
|
||||
"\n",
|
||||
" def forward(self, x):\n",
|
||||
" x = self.flatten(x)\n",
|
||||
" x = torch.relu(self.fc1(x))\n",
|
||||
" x = self.dropout(x)\n",
|
||||
" x = self.fc2(x)\n",
|
||||
" return x\n",
|
||||
"\n",
|
||||
"model = SimpleNet()\n",
|
||||
"\n",
|
||||
"if torch.backends.mps.is_available():\n",
|
||||
" device = torch.device(\"mps\")\n",
|
||||
"else:\n",
|
||||
" device = torch.device(\"cpu\")\n",
|
||||
"\n",
|
||||
"model.to(device)\n",
|
||||
"\n",
|
||||
"# 定义损失函数和优化器\n",
|
||||
"criterion = nn.CrossEntropyLoss()\n",
|
||||
"optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
|
||||
"\n",
|
||||
"# 训练模型\n",
|
||||
"epochs = 5\n",
|
||||
"for epoch in range(epochs):\n",
|
||||
" running_loss = 0\n",
|
||||
" for images, labels in trainloader:\n",
|
||||
" images, labels = images.to(device), labels.to(device) # 将数据移动到设备上\n",
|
||||
" optimizer.zero_grad()\n",
|
||||
" output = model(images)\n",
|
||||
" loss = criterion(output, labels)\n",
|
||||
" loss.backward()\n",
|
||||
" optimizer.step()\n",
|
||||
" running_loss += loss.item()\n",
|
||||
" print(f\"Epoch {epoch+1}/{epochs} - Loss: {running_loss/len(trainloader)}\")\n",
|
||||
"\n",
|
||||
"# 测试模型\n",
|
||||
"correct = 0\n",
|
||||
"total = 0\n",
|
||||
"with torch.no_grad():\n",
|
||||
" for images, labels in testloader:\n",
|
||||
" images, labels = images.to(device), labels.to(device) # 将数据移动到设备上\n",
|
||||
" output = model(images)\n",
|
||||
" _, predicted = torch.max(output, 1)\n",
|
||||
" total += labels.size(0)\n",
|
||||
" correct += (predicted == labels).sum().item()\n",
|
||||
"\n",
|
||||
"print(f\"Accuracy: {100 * correct / total}%\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "ail",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.16"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
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@@ -4,4 +4,5 @@ pillow
|
||||
matplotlib
|
||||
ipywidgets
|
||||
jupyter
|
||||
scikit-learn
|
||||
scikit-learn
|
||||
mnist
|
||||
Reference in New Issue
Block a user