实践笔记 基于TensorFlow的手写体识别

活动链接

【开发者日·创享峰会专场】体验官：体验六大实践项目，领开发者定制好礼

【开发者空间实践指导】基于TensorFlow的手写体识别

操作步骤

进入开发者空间，使用我的云主机

进入桌面，桌面效果如下所示

pycharm下载、解压与运行

按照教程中的实验步骤，下载并安装PyCharm。教程中下载的是PyCharm professional版本，所以会有试用期的说法。

/home/developer/Downloads/pycharm-2024.3.1.1/bin/

新建项目与下载TensorFlow框架

• 新建项目

• 选择python

直接用系统的python，也是一种高危操作。建议初学者使用conda，建立python的虚拟环境，这样的话，能减少系统崩溃的概率。

或者说，直接用云主机，出了问题直接重置，方便又快捷！

• 新建目录与python文件

在安装tensorflow，我忘记指定源了，直接

pip3 install tensorflow

导致出现70KB的下载速度…

Downloading tensorflow-2.18.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (615.3 MB)
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手动停止后，使用新的源进行下载，果然快多了

Downloading tensorflow-2.18.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (615.3 MB)
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ERROR: Operation cancelled by user

[notice] A new release of pip is available: 23.2.1 -> 24.3.1
[notice] To update, run: pip install --upgrade pip

(.venv) developer@developer:~/PycharmProjects/PythonProject$ pip install tensorflow -i http://pypi.doubanio.com/simple --trusted-host pypi.doubanio.com
Looking in indexes: http://pypi.doubanio.com/simple
Collecting tensorflow
  Downloading https://mirrors.cloud.tencent.com/pypi/packages/d4/80/1567ccc375ccda4d28af28c960cca7f709f7c259463ac1436554697e8868/tensorflow-2.18.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (615.3 MB)
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tensorflow安装完成

(.venv) developer@developer:~/PycharmProjects/PythonProject$ pip install tensorflow -i http://pypi.doubanio.com/simple --trusted-host pypi.doubanio.com
Looking in indexes: http://pypi.doubanio.com/simple
Collecting tensorflow
  Downloading https://mirrors.cloud.tencent.com/pypi/packages/d4/80/1567ccc375ccda4d28af28c960cca7f709f7c259463ac1436554697e8868/tensorflow-2.18.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (615.3 MB)
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Collecting absl-py>=1.0.0 (from tensorflow)
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Collecting astunparse>=1.6.0 (from tensorflow)
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Collecting flatbuffers>=24.3.25 (from tensorflow)
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Collecting gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 (from tensorflow)
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Collecting libclang>=13.0.0 (from tensorflow)
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Collecting opt-einsum>=2.3.2 (from tensorflow)
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Collecting packaging (from tensorflow)
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Collecting protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.3 (from tensorflow)
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Collecting requests<3,>=2.21.0 (from tensorflow)
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Requirement already satisfied: setuptools in ./.venv/lib/python3.10/site-packages (from tensorflow) (68.2.0)
Collecting six>=1.12.0 (from tensorflow)
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Collecting termcolor>=1.1.0 (from tensorflow)
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Collecting typing-extensions>=3.6.6 (from tensorflow)
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Collecting wrapt>=1.11.0 (from tensorflow)
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Collecting grpcio<2.0,>=1.24.3 (from tensorflow)
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Collecting tensorboard<2.19,>=2.18 (from tensorflow)
  Downloading https://mirrors.cloud.tencent.com/pypi/packages/b1/de/021c1d407befb505791764ad2cbd56ceaaa53a746baed01d2e2143f05f18/tensorboard-2.18.0-py3-none-any.whl (5.5 MB)
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Collecting keras>=3.5.0 (from tensorflow)
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Collecting numpy<2.1.0,>=1.26.0 (from tensorflow)
  Downloading https://mirrors.cloud.tencent.com/pypi/packages/fa/66/f7177ab331876200ac7563a580140643d1179c8b4b6a6b0fc9838de2a9b8/numpy-2.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (19.5 MB)
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Collecting h5py>=3.11.0 (from tensorflow)
  Downloading https://mirrors.cloud.tencent.com/pypi/packages/85/bc/e76f4b2096e0859225f5441d1b7f5e2041fffa19fc2c16756c67078417aa/h5py-3.12.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.3 MB)
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Collecting ml-dtypes<0.5.0,>=0.4.0 (from tensorflow)
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Collecting tensorflow-io-gcs-filesystem>=0.23.1 (from tensorflow)
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Requirement already satisfied: wheel<1.0,>=0.23.0 in ./.venv/lib/python3.10/site-packages (from astunparse>=1.6.0->tensorflow) (0.41.2)
Collecting rich (from keras>=3.5.0->tensorflow)
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Installing collected packages: namex, libclang, flatbuffers, wrapt, urllib3, typing-extensions, termcolor, tensorflow-io-gcs-filesystem, tensorboard-data-server, six, pygments, protobuf, packaging, opt-einsum, numpy, mdurl, MarkupSafe, markdown, idna, grpcio, gast, charset-normalizer, certifi, absl-py, werkzeug, requests, optree, ml-dtypes, markdown-it-py, h5py, google-pasta, astunparse, tensorboard, rich, keras, tensorflow
Successfully installed MarkupSafe-3.0.2 absl-py-2.1.0 astunparse-1.6.3 certifi-2024.12.14 charset-normalizer-3.4.0 flatbuffers-24.3.25 gast-0.6.0 google-pasta-0.2.0 grpcio-1.68.1 h5py-3.12.1 idna-3.10 keras-3.7.0 libclang-18.1.1 markdown-3.7 markdown-it-py-3.0.0 mdurl-0.1.2 ml-dtypes-0.4.1 namex-0.0.8 numpy-2.0.2 opt-einsum-3.4.0 optree-0.13.1 packaging-24.2 protobuf-5.29.2 pygments-2.18.0 requests-2.32.3 rich-13.9.4 six-1.17.0 tensorboard-2.18.0 tensorboard-data-server-0.7.2 tensorflow-2.18.0 tensorflow-io-gcs-filesystem-0.37.1 termcolor-2.5.0 typing-extensions-4.12.2 urllib3-2.2.3 werkzeug-3.1.3 wrapt-1.17.0

[notice] A new release of pip is available: 23.2.1 -> 24.3.1
[notice] To update, run: pip install --upgrade pip
(.venv) developer@developer:~/PycharmProjects/PythonProject$ 

安装matplotlib

(.venv) developer@developer:~/PycharmProjects/PythonProject$ pip install matplotlib -i http://pypi.doubanio.com/simple --trusted-host pypi.doubanio.com
Looking in indexes: http://pypi.doubanio.com/simple
Collecting matplotlib
  Downloading https://mirrors.cloud.tencent.com/pypi/packages/09/5a/a113495110ae3e3395c72d82d7bc4802902e46dc797f6b041e572f195c56/matplotlib-3.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.6 MB)
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Collecting contourpy>=1.0.1 (from matplotlib)
  Downloading https://mirrors.cloud.tencent.com/pypi/packages/a9/57/86c500d63b3e26e5b73a28b8291a67c5608d4aa87ebd17bd15bb33c178bc/contourpy-1.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (324 kB)
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Collecting cycler>=0.10 (from matplotlib)
  Downloading https://mirrors.cloud.tencent.com/pypi/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl (8.3 kB)
Collecting fonttools>=4.22.0 (from matplotlib)
  Downloading https://mirrors.cloud.tencent.com/pypi/packages/34/db/d423bc646e6703fe3e6aea0edd22a2df47b9d188c5f7f1b49070be4d2205/fonttools-4.55.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.6 MB)
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Collecting kiwisolver>=1.3.1 (from matplotlib)
  Downloading https://mirrors.cloud.tencent.com/pypi/packages/55/91/0a57ce324caf2ff5403edab71c508dd8f648094b18cfbb4c8cc0fde4a6ac/kiwisolver-1.4.7-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB)
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Requirement already satisfied: numpy>=1.23 in ./.venv/lib/python3.10/site-packages (from matplotlib) (2.0.2)
Requirement already satisfied: packaging>=20.0 in ./.venv/lib/python3.10/site-packages (from matplotlib) (24.2)
Collecting pillow>=8 (from matplotlib)
  Downloading https://mirrors.cloud.tencent.com/pypi/packages/41/c3/94f33af0762ed76b5a237c5797e088aa57f2b7fa8ee7932d399087be66a8/pillow-11.0.0-cp310-cp310-manylinux_2_28_x86_64.whl (4.4 MB)
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Collecting pyparsing>=2.3.1 (from matplotlib)
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Requirement already satisfied: six>=1.5 in ./.venv/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)
Installing collected packages: python-dateutil, pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib
Successfully installed contourpy-1.3.1 cycler-0.12.1 fonttools-4.55.3 kiwisolver-1.4.7 matplotlib-3.10.0 pillow-11.0.0 pyparsing-3.2.0 python-dateutil-2.9.0.post0

[notice] A new release of pip is available: 23.2.1 -> 24.3.1
[notice] To update, run: pip install --upgrade pip
(.venv) developer@developer:~/PycharmProjects/PythonProject$ 

运行与效果

使用教程中的代码，点击运行。

建议用云主机的方式来学习AI，当年上学时，有个同学的笔记本散热不行，训练模型时把电脑烧了。

import tensorflow as tf
from tensorflow.keras import layers, models, callbacks
import matplotlib.pyplot as plt
import numpy as np

# 下载MNIST数据集
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 归一化数据
x_train, x_test = x_train / 255.0, x_test / 255.0

# 拓展维度
x_train = x_train[..., tf.newaxis].astype("float32")
x_test = x_test[..., tf.newaxis].astype("float32")


# 数据增强
data_augmentation = tf.keras.Sequential([
    layers.RandomRotation(0.1),
    layers.RandomZoom(0.1),
    layers.RandomTranslation(0.1, 0.1)
])

# 创建数据集对象，并设置每个批次的大小
train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)

# 构建模型
model = models.Sequential([
    data_augmentation,
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(128, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Flatten(),
    layers.Dropout(0.5),
    layers.Dense(128, activation='relu'),
    layers.Dense(10)
])

# 编译模型
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# 回调模型
reduce_lr = callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.0001)
early_stopping = callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
model_checkpoint = callbacks.ModelCheckpoint('best_model.keras', monitor='val_loss', save_best_only=True)

# 训练模型
history = model.fit(train_ds, epochs=10, validation_data=test_ds, callbacks=[reduce_lr, early_stopping, model_checkpoint])

# 加载模型最佳权重
model.load_weights('best_model.keras')

# 评估模型
test_loss, test_acc = model.evaluate(test_ds, verbose=2)
print('\nTest accuracy:', test_acc)


# 绘制实验的损失及准确率
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend(loc='upper right')

plt.subplot(1, 2, 2)
plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')

plt.show()


# 预测并绘制一些预测样本
def plot_image(i, predictions_array, true_label, img):
    true_label, img = true_label[i], img[i].reshape((28, 28))
    plt.grid(False)
    plt.xticks([])
    plt.yticks([])

    plt.imshow(img, cmap=plt.cm.binary)

    predicted_label = np.argmax(predictions_array)
    if predicted_label == true_label:
        color = 'blue'
    else:
        color = 'red'

    plt.xlabel("{} {:2.0f}% ({})".format(true_label, 100*np.max(predictions_array), predicted_label),
                color=color)



# 预测测试样本集
predictions = model.predict(test_ds)

# 绘制前五个测试样本
num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
    plt.subplot(num_rows, 2*num_cols, 2*i+1)
    plot_image(i, predictions[i], y_test, x_test)
plt.tight_layout()
plt.show()

输入内容

/home/developer/PycharmProjects/PythonProject/.venv/bin/python /home/developer/PycharmProjects/PythonProject/demo/my_tensor.py 
2024-12-20 23:06:42.716053: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-12-20 23:06:42.716671: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2024-12-20 23:06:42.720001: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2024-12-20 23:06:42.730849: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1734707202.749757    9184 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1734707202.755327    9184 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-12-20 23:06:42.773968: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11490434/11490434 ━━━━━━━━━━━━━━━━━━━━ 2s 0us/step
2024-12-20 23:06:48.909145: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:152] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)
/home/developer/PycharmProjects/PythonProject/.venv/lib/python3.10/site-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
Epoch 1/10
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 21s 10ms/step - accuracy: 0.6277 - loss: 1.0952 - val_accuracy: 0.9573 - val_loss: 0.1393 - learning_rate: 0.0010
Epoch 2/10
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 19s 10ms/step - accuracy: 0.8885 - loss: 0.3627 - val_accuracy: 0.9693 - val_loss: 0.1067 - learning_rate: 0.0010
Epoch 3/10
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 18s 10ms/step - accuracy: 0.9159 - loss: 0.2726 - val_accuracy: 0.9730 - val_loss: 0.0864 - learning_rate: 0.0010
Epoch 4/10
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 19s 10ms/step - accuracy: 0.9268 - loss: 0.2385 - val_accuracy: 0.9736 - val_loss: 0.0835 - learning_rate: 0.0010
Epoch 5/10
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 18s 10ms/step - accuracy: 0.9350 - loss: 0.2130 - val_accuracy: 0.9812 - val_loss: 0.0582 - learning_rate: 0.0010
Epoch 6/10
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 18s 10ms/step - accuracy: 0.9408 - loss: 0.1941 - val_accuracy: 0.9788 - val_loss: 0.0654 - learning_rate: 0.0010
Epoch 7/10
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 18s 10ms/step - accuracy: 0.9430 - loss: 0.1836 - val_accuracy: 0.9794 - val_loss: 0.0651 - learning_rate: 0.0010
Epoch 8/10
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 19s 10ms/step - accuracy: 0.9473 - loss: 0.1720 - val_accuracy: 0.9819 - val_loss: 0.0590 - learning_rate: 0.0010
Epoch 9/10
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 18s 10ms/step - accuracy: 0.9493 - loss: 0.1694 - val_accuracy: 0.9684 - val_loss: 0.0913 - learning_rate: 0.0010
Epoch 10/10
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 18s 10ms/step - accuracy: 0.9481 - loss: 0.1645 - val_accuracy: 0.9795 - val_loss: 0.0635 - learning_rate: 0.0010
313/313 - 1s - 3ms/step - accuracy: 0.9812 - loss: 0.0582

Test accuracy: 0.9811999797821045
313/313 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step

进程已结束，退出代码为 0

想要学习代码的同学，可以去教程贴，内容比较详细，可以为自学增加助力！