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Python数据科学_25_案例：手写数字识别【计算机视觉】

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设置GPU

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from tensorflow.config.experimental import list_physical_devices, set_memory_growth
physical_devices = list_physical_devices('GPU')
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physical_devices
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

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set_memory_growth(physical_devices[0], True)

数据读取

数据集下载

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import numpy as np
import tensorflow as tf
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files = np.load('mnist.npz')
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x_train = files['x_train']
x_test = files['x_test']
y_train = files['y_train']
y_test = files['y_test']
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print(x_train.shape)
print(x_test.shape)
print(y_train.shape)
print(y_test.shape)
(60000, 28, 28)
(10000, 28, 28)
(60000,)
(10000,)

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x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
print(x_train.shape)
print(x_test.shape)
(60000, 28, 28, 1)
(10000, 28, 28, 1)

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n = 3000
# 展示一张图片
import matplotlib.pyplot as plt
plt.title('y=' + str(y_train[n]))
plt.imshow(x_train[n].reshape((28, 28)), cmap='gray')
plt.show()

output_10_0_202303092125

模型的搭建

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from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import losses
from tensorflow.keras import metrics
from tensorflow.keras import optimizers
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# 搭建LeNet5神经网络框架
model = keras.Sequential(name='LeNet5')
# 卷积层
# filters: 卷积核的个数
# kernel_size: 卷积核的大小
# striders: 卷积核移动的步长
# input_shape: 输入到模型中的数据的尺寸，这个只在网络的第一层中指定
# activation: 非线性激活函数
model.add(keras.layers.Conv2D(filters=6, kernel_size=(5, 5), strides=(1, 1), input_shape=(28, 28, 1), activation='relu'))
# 最大池化层
model.add(keras.layers.MaxPool2D())
# 卷积层
model.add(keras.layers.Conv2D(filters=16, kernel_size=(5, 5), strides=(1, 1), activation='relu'))
# 最大池化层
model.add(keras.layers.MaxPool2D())
# 展平层
model.add(keras.layers.Flatten())
# 全连接层
# units: 全连接层的神经元数量
model.add(keras.layers.Dense(units=120, activation='relu'))
# 全连接层
model.add(keras.layers.Dense(units=84, activation='relu'))
# 全连接层
model.add(keras.layers.Dense(units=10, activation='softmax'))
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model.summary()
Model: "LeNet5"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 24, 24, 6)         156       

 max_pooling2d (MaxPooling2D  (None, 12, 12, 6)        0         
 )                                                               

 conv2d_1 (Conv2D)           (None, 8, 8, 16)          2416      

 max_pooling2d_1 (MaxPooling  (None, 4, 4, 16)         0         
 2D)                                                             

 flatten (Flatten)           (None, 256)               0         

 dense (Dense)               (None, 120)               30840     

 dense_1 (Dense)             (None, 84)                10164     

 dense_2 (Dense)             (None, 10)                850       

=================================================================
Total params: 44,426
Trainable params: 44,426
Non-trainable params: 0
_________________________________________________________________

模型的编译

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model.compile(optimizer='adam', loss=losses.sparse_categorical_crossentropy, metrics=metrics.sparse_categorical_accuracy)

模型的训练

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model.fit(x_train, y_train, epochs=20, batch_size=64, validation_split=0.2)
Epoch 1/20
750/750 [==============================] - 7s 5ms/step - loss: 0.7774 - sparse_categorical_accuracy: 0.8722 - val_loss: 0.1533 - val_sparse_categorical_accuracy: 0.9541
Epoch 2/20
750/750 [==============================] - 3s 4ms/step - loss: 0.1345 - sparse_categorical_accuracy: 0.9605 - val_loss: 0.1094 - val_sparse_categorical_accuracy: 0.9671
Epoch 3/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0900 - sparse_categorical_accuracy: 0.9730 - val_loss: 0.0919 - val_sparse_categorical_accuracy: 0.9734
Epoch 4/20
750/750 [==============================] - 4s 5ms/step - loss: 0.0693 - sparse_categorical_accuracy: 0.9791 - val_loss: 0.0940 - val_sparse_categorical_accuracy: 0.9712
Epoch 5/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0582 - sparse_categorical_accuracy: 0.9814 - val_loss: 0.0920 - val_sparse_categorical_accuracy: 0.9747
Epoch 6/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0501 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0806 - val_sparse_categorical_accuracy: 0.9769
Epoch 7/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9863 - val_loss: 0.0805 - val_sparse_categorical_accuracy: 0.9756
Epoch 8/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0390 - sparse_categorical_accuracy: 0.9874 - val_loss: 0.0713 - val_sparse_categorical_accuracy: 0.9808
Epoch 9/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0382 - sparse_categorical_accuracy: 0.9879 - val_loss: 0.0710 - val_sparse_categorical_accuracy: 0.9803
Epoch 10/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0333 - sparse_categorical_accuracy: 0.9893 - val_loss: 0.0630 - val_sparse_categorical_accuracy: 0.9842
Epoch 11/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0324 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.0648 - val_sparse_categorical_accuracy: 0.9828
Epoch 12/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0280 - sparse_categorical_accuracy: 0.9909 - val_loss: 0.0733 - val_sparse_categorical_accuracy: 0.9826
Epoch 13/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0251 - sparse_categorical_accuracy: 0.9923 - val_loss: 0.0714 - val_sparse_categorical_accuracy: 0.9827
Epoch 14/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0276 - sparse_categorical_accuracy: 0.9913 - val_loss: 0.0641 - val_sparse_categorical_accuracy: 0.9847
Epoch 15/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0228 - sparse_categorical_accuracy: 0.9932 - val_loss: 0.0857 - val_sparse_categorical_accuracy: 0.9820
Epoch 16/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0190 - sparse_categorical_accuracy: 0.9941 - val_loss: 0.0786 - val_sparse_categorical_accuracy: 0.9829
Epoch 17/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0203 - sparse_categorical_accuracy: 0.9940 - val_loss: 0.0730 - val_sparse_categorical_accuracy: 0.9834
Epoch 18/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0206 - sparse_categorical_accuracy: 0.9937 - val_loss: 0.0772 - val_sparse_categorical_accuracy: 0.9848
Epoch 19/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0201 - sparse_categorical_accuracy: 0.9941 - val_loss: 0.0881 - val_sparse_categorical_accuracy: 0.9836
Epoch 20/20
750/750 [==============================] - 3s 4ms/step - loss: 0.0182 - sparse_categorical_accuracy: 0.9942 - val_loss: 0.0915 - val_sparse_categorical_accuracy: 0.9830





<keras.callbacks.History at 0x1f66d29fa00>

模型的验证

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model.evaluate(x_test, y_test)
313/313 [==============================] - 1s 3ms/step - loss: 0.0656 - sparse_categorical_accuracy: 0.9858





[0.06561200320720673, 0.98580002784729]

模型的测试

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y_pred = model.predict(x_test[:10]).argmax(axis=1)
print(y_pred)
print(y_test[:10])
1/1 [==============================] - 0s 18ms/step
[7 2 1 0 4 1 4 9 5 9]
[7 2 1 0 4 1 4 9 5 9]

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plt.figure(dpi=200)
for i in range(len(x_test[:10])):
    ax = plt.subplot(2,5,i+1)
    ax.axis('off')
    ax.set_title(f'y_true={y_test[i]}\ny_pred={y_pred[i]}')
    ax.imshow(x_test[i].reshape((28, 28)))
plt.show()

output_23_0_202303092125

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