【2019.09.13】基于TensorFlow2.0，使用Keras + fashion_mnist 数据集进行深度学习实战

快来打我* 2024-04-18 23:23 84阅读 0赞

# 基于TensorFlow2.0，使用Keras + fashion\_mnist 数据集进行深度学习实战 #

> 人工智能、机器学习、深度学习 的关系  
> ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70]

*  导入相关包

tf.__version__

'2.0.0-alpha0'

# 导入数据集
    (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()

print( x_train.shape )
    print( x_test.shape )
    print( y_train.shape )
    print( y_test.shape )

(60000, 28, 28)
    (10000, 28, 28)
    (60000,)
    (10000,)

# 构建简单模型（全连接网络）
    model = Sequential([Flatten(input_shape=[28, 28]),
                        Dense(100, activation='relu'),
                        Dense(10, activation='softmax')
                        ])

model.summary()

Model: "sequential"
    _________________________________________________________________
    Layer (type)                 Output Shape              Param #   
    =================================================================
    flatten (Flatten)            (None, 784)               0         
    _________________________________________________________________
    dense (Dense)                (None, 100)               78500     
    _________________________________________________________________
    dense_1 (Dense)              (None, 10)                1010      
    =================================================================
    Total params: 79,510
    Trainable params: 79,510
    Non-trainable params: 0
    _________________________________________________________________

# 使用modle.layers[1].name 来获取第n层的名称
    model.layers[1].name

'dense'

# get_weights() 来获取每层的权重矩阵 W 和偏置向量 b。
    weights, biases = model.layers[1].get_weights()
    weights

array([[ 0.03753366, -0.07585614,  0.05845281, ..., -0.00696003,
            -0.02985662,  0.0026468 ],
           [-0.02360117,  0.07903261, -0.00201984, ...,  0.01831853,
            -0.05822062,  0.00874755],
           [ 0.03159927, -0.00679947,  0.03076784, ...,  0.06593607,
            -0.00499721,  0.03378649],
           ...,
           [ 0.01089679,  0.04923365,  0.07235795, ...,  0.01033241,
             0.01817431, -0.04198586],
           [ 0.03213584, -0.0057021 ,  0.00929629, ..., -0.03756753,
             0.01735194, -0.01611251],
           [ 0.06783222, -0.04055587, -0.06099807, ..., -0.06757091,
            -0.01999778,  0.00600851]], dtype=float32)

biases

array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
          dtype=float32)

# 当模型还没训练时，W 是随机初始化，而 b 是零初始化。最后检查一下它们的形状。
    print( weights.shape )
    print( biases.shape )

(784, 100)
    (100,)

# 编译模型
    model.compile(loss='sparse_categorical_crossentropy',
                  optimizer='adam',
                  metrics=['acc'])

### 损失函数loss ###

*  二分类问题: 最后一层激活函数是 `sogmoid`,损失函数是`binart_crossentropy`
 *  多分类问题：最后一层激活函数是 `softmax`,损失函数是`categorical_crossentropy`
 *  多标签问题：最后一层激活函数是 `softmoid`,损失函数是`binary_crossentropy`
 *  回归问题 ：最后一层没有激活函数,损失函数是`mse`  
    Fashion\_MNIST 是一个十分类问题，因此损失函数是 categorical\_crossentropy。

### 优化器 optimizer ###

大多数情况下，使用 `adam` 和 `rmsprop` 及其默认的学习率是稳妥的  
除了通过名称来调用优化器 model.compile(‘名称’)，我们还可以通过实例化对象来调用优化器 model.compile(‘优化器’)。选取几个对比如下：

名称：SGD

对象：SGD(lr=0.01, momentum=0.0, decay=0.0, nesterov=False)

名称：RMSprop

对象：RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)

名称：Adagrad

对象：Adagrad(lr=0.01, epsilon=None, decay=0.0)

名称：Adam

对象：

Adam(lr=0.001, beta\_1=0.9, beta\_2=0.999, epsilon=None, decay=0.0, amsgrad=False)

这些优化器对象都在 keras.optimizer 命名空间下。使用优化器对象来编译模型的好处是可以调节里面的超参数比如学习率 lr，使用名称则来编译模型只能采用优化器的默认参数，比如用 Adam 里面的学习率 0.001。

## 指标 metrics ##

指标不会用于训练过程，只是让我们监控模型训练时的表现  
除了 Keras 自带指标，我们还可以自定指标，下列的 mean\_pred 就是自定义指标（该指标计算预测的平均值）。  
def mean\_pred(y\_true, y\_pred):  
return K.mean(y\_pred)

model.compile(optimizer=‘sgd’,  
loss=‘binary\_crossentropy’,  
metrics=\[‘acc’, mean\_pred\])

# 调用函数（回调）
    class myCallback(tf.keras.callbacks.Callback):
        def on_epoch_end(self, epoch, logs={ }):
            if (logs.get('acc') > 0.9):
                print('\nReached 90% accuracy so cancelling training!')
                self.model.stop_training = True
    
    callback = myCallback()

# 拟合模型
    model.fit(x_train, y_train, epochs=20, callbacks=[callback])

Epoch 1/20
    60000/60000 [==============================] - 5s 81us/sample - loss: 2.5439 - acc: 0.6660
    Epoch 2/20
    60000/60000 [==============================] - 4s 72us/sample - loss: 0.7489 - acc: 0.7143
    Epoch 3/20
    60000/60000 [==============================] - 4s 72us/sample - loss: 0.6793 - acc: 0.7381
    Epoch 4/20
    60000/60000 [==============================] - 4s 71us/sample - loss: 0.5989 - acc: 0.7839
    Epoch 5/20
    60000/60000 [==============================] - 4s 72us/sample - loss: 0.5315 - acc: 0.8148
    Epoch 6/20
    60000/60000 [==============================] - 4s 71us/sample - loss: 0.5149 - acc: 0.8244
    Epoch 7/20
    60000/60000 [==============================] - 4s 72us/sample - loss: 0.4978 - acc: 0.8292
    Epoch 8/20
    60000/60000 [==============================] - 4s 71us/sample - loss: 0.4953 - acc: 0.8321
    Epoch 9/20
    60000/60000 [==============================] - 4s 72us/sample - loss: 0.4870 - acc: 0.8366
    Epoch 10/20
    60000/60000 [==============================] - 4s 73us/sample - loss: 0.4856 - acc: 0.8376
    Epoch 11/20
    60000/60000 [==============================] - 4s 71us/sample - loss: 0.4868 - acc: 0.8378
    Epoch 12/20
    60000/60000 [==============================] - 4s 71us/sample - loss: 0.4844 - acc: 0.8399
    Epoch 13/20
    60000/60000 [==============================] - 4s 71us/sample - loss: 0.4689 - acc: 0.8425
    Epoch 14/20
    60000/60000 [==============================] - 4s 71us/sample - loss: 0.4794 - acc: 0.8420
    Epoch 15/20
    60000/60000 [==============================] - 4s 72us/sample - loss: 0.4715 - acc: 0.8451
    Epoch 16/20
    60000/60000 [==============================] - 4s 72us/sample - loss: 0.4695 - acc: 0.8469
    Epoch 17/20
    60000/60000 [==============================] - 4s 72us/sample - loss: 0.4604 - acc: 0.8473
    Epoch 18/20
    60000/60000 [==============================] - 4s 71us/sample - loss: 0.4617 - acc: 0.8481
    Epoch 19/20
    60000/60000 [==============================] - 4s 72us/sample - loss: 0.4678 - acc: 0.8471
    Epoch 20/20
    60000/60000 [==============================] - 4s 72us/sample - loss: 0.4570 - acc: 0.8475
    
    
    
    
    <tensorflow.python.keras.callbacks.History at 0x210647eafd0>

# 预测模型
    prob = model.predict( x_test[0:1] )
    prob

array([[7.6639465e-23, 1.3957777e-23, 0.0000000e+00, 2.8266480e-21,
            0.0000000e+00, 9.7962271e-04, 0.0000000e+00, 8.5630892e-03,
            1.0883533e-21, 9.9045730e-01]], dtype=float32)

在测试集上第一张图上做预测，输出是一个数组，里面 10 个数值代表每个类别预测的概率。看上去是第 10 类（索引为 9）概率最大。用 argmax 验证一下

print(np.argmax(prob))

plt.imshow(x_test[0])

<matplotlib.image.AxesImage at 0x2111b411e48>

![在这里插入图片描述][20190913215507137.png]

最后用 model.evaluate() 来看看模型在所有测试集上的表现。

model.evaluate( x_test, y_test )

10000/10000 [==============================] - 1s 54us/sample - loss: 0.5990 - acc: 0.8217
    
    
    
    
    [0.5990172575473786, 0.8217]

如果训练集准确率大于测试集精度，这样就说明有过拟合的征兆

如果准确率表现不是很好，我们就应该考虑一下我们的模型了，在这里，我们使用单层全连接模型可能是比较简单的，我们使用卷积神经网络尝试一下  
首先导入 二维卷积层Conv2D, 二维最大池化层MaxPooling2D

# 编译模型
    model.compile(loss='sparse_categorical_crossentropy',
                  optimizer='adam',
                  metrics=['acc'])

# 拟合模型
    (x_train_full, y_train_full), (x_test, y_test) = fashion_mnist.load_data()
    x_train_full = x_train_full.reshape(60000, 28, 28, 1)
    x_test = x_test.reshape(10000, 28, 28, 1) 
    x_valid, x_train = x_train_full[:5000]/255.0, x_train_full[5000:] / 255.0
    y_valid, y_train = y_train_full[:5000], y_train_full[5000:]
    
    
    # x_test = x_test.reshape(10000, 28, 28, 1)
    # y_test = y_test.reshape(10000, 28, 28, 1)
    
    
    history = model.fit(x_train, y_train, epochs=20, validation_data=(x_valid, y_valid))

Train on 55000 samples, validate on 5000 samples
    Epoch 1/20
    55000/55000 [==============================] - 9s 166us/sample - loss: 0.1063 - acc: 0.9602 - val_loss: 0.3073 - val_acc: 0.9094
    Epoch 2/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.0950 - acc: 0.9645 - val_loss: 0.3091 - val_acc: 0.9158
    Epoch 3/20
    55000/55000 [==============================] - 9s 162us/sample - loss: 0.0893 - acc: 0.9664 - val_loss: 0.3295 - val_acc: 0.9138
    Epoch 4/20
    55000/55000 [==============================] - 9s 163us/sample - loss: 0.0783 - acc: 0.9704 - val_loss: 0.3551 - val_acc: 0.9138
    Epoch 5/20
    55000/55000 [==============================] - 9s 163us/sample - loss: 0.0712 - acc: 0.9734 - val_loss: 0.3801 - val_acc: 0.9070
    Epoch 6/20
    55000/55000 [==============================] - 9s 162us/sample - loss: 0.0666 - acc: 0.9752 - val_loss: 0.3812 - val_acc: 0.9126
    Epoch 7/20
    55000/55000 [==============================] - 9s 167us/sample - loss: 0.0599 - acc: 0.9775 - val_loss: 0.3997 - val_acc: 0.9122
    Epoch 8/20
    55000/55000 [==============================] - 9s 167us/sample - loss: 0.0543 - acc: 0.9802 - val_loss: 0.4552 - val_acc: 0.9068
    Epoch 9/20
    55000/55000 [==============================] - 9s 165us/sample - loss: 0.0515 - acc: 0.9807 - val_loss: 0.4546 - val_acc: 0.9056
    Epoch 10/20
    55000/55000 [==============================] - 9s 166us/sample - loss: 0.0483 - acc: 0.9822 - val_loss: 0.4561 - val_acc: 0.9122
    Epoch 11/20
    55000/55000 [==============================] - 9s 165us/sample - loss: 0.0427 - acc: 0.9842 - val_loss: 0.4877 - val_acc: 0.9092
    Epoch 12/20
    55000/55000 [==============================] - 9s 167us/sample - loss: 0.0406 - acc: 0.9849 - val_loss: 0.4913 - val_acc: 0.9100
    Epoch 13/20
    55000/55000 [==============================] - 9s 166us/sample - loss: 0.0392 - acc: 0.9853 - val_loss: 0.5382 - val_acc: 0.9102
    Epoch 14/20
    55000/55000 [==============================] - 9s 165us/sample - loss: 0.0361 - acc: 0.9866 - val_loss: 0.5899 - val_acc: 0.9116
    Epoch 15/20
    55000/55000 [==============================] - 9s 167us/sample - loss: 0.0375 - acc: 0.9864 - val_loss: 0.5630 - val_acc: 0.9136
    Epoch 16/20
    55000/55000 [==============================] - 9s 168us/sample - loss: 0.0301 - acc: 0.9891 - val_loss: 0.5853 - val_acc: 0.9140
    Epoch 17/20
    55000/55000 [==============================] - 9s 169us/sample - loss: 0.0347 - acc: 0.9875 - val_loss: 0.6378 - val_acc: 0.9054
    Epoch 18/20
    55000/55000 [==============================] - 9s 169us/sample - loss: 0.0280 - acc: 0.9899 - val_loss: 0.5961 - val_acc: 0.9106
    Epoch 19/20
    55000/55000 [==============================] - 9s 168us/sample - loss: 0.0306 - acc: 0.9890 - val_loss: 0.6319 - val_acc: 0.9100
    Epoch 20/20
    55000/55000 [==============================] - 9s 165us/sample - loss: 0.0252 - acc: 0.9908 - val_loss: 0.6697 - val_acc: 0.9078

acc = history.history.get('acc')
    val_acc = history.history.get('val_acc')
    loss = history.history.get('loss')
    val_loss = history.history.get('val_loss')
    
    epochs = range(1, len(acc)+1)
    plt.figure(figsize=(8,4),dpi=100)
    plt.subplot(1, 2, 1)
    plt.plot(epochs, acc, 'bo', label='Traing acc')
    plt.plot(epochs, val_acc, 'b', label='Validation acc')
    plt.legend()
    
    plt.subplot(1, 2, 2)
    plt.plot(epochs, loss, 'ro', label='Traing loss')
    plt.plot(epochs, val_loss, 'r', label='Validation val_loss')
    plt.legend()

<matplotlib.legend.Legend at 0x211266eb550>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 1]

我们发现，训练集准确率高于验证集准确率，验证集的acc和loss都有点不降反升的感觉，过拟合了，怎么办呢？用 Dropout 试试？

# 构建模型（卷积）
    model = Sequential([Conv2D(64, (3,3), activation='relu', input_shape=(28, 28, 1)),
                               MaxPooling2D(2,2),
                               Conv2D(64, (3,3), activation='relu'),
                               MaxPooling2D(2,2),
                               Flatten(),
                        Dropout(0.5),
                               Dense(128, activation='relu'),
                               Dense(10, activation='softmax')])
    
    # 编译模型
    model.compile(loss='sparse_categorical_crossentropy',
                  optimizer='adam',
                  metrics=['acc'])
    
    history = model.fit(x_train, y_train, epochs=20, validation_data=(x_valid, y_valid))

Train on 55000 samples, validate on 5000 samples
    Epoch 1/20
    55000/55000 [==============================] - 9s 171us/sample - loss: 0.5157 - acc: 0.8105 - val_loss: 0.3606 - val_acc: 0.8676
    Epoch 2/20
    55000/55000 [==============================] - 9s 168us/sample - loss: 0.3601 - acc: 0.8664 - val_loss: 0.2841 - val_acc: 0.8968
    Epoch 3/20
    55000/55000 [==============================] - 9s 168us/sample - loss: 0.3160 - acc: 0.8840 - val_loss: 0.2870 - val_acc: 0.8942
    Epoch 4/20
    55000/55000 [==============================] - 9s 167us/sample - loss: 0.2900 - acc: 0.8914 - val_loss: 0.2527 - val_acc: 0.9056
    Epoch 5/20
    55000/55000 [==============================] - 9s 170us/sample - loss: 0.2703 - acc: 0.8976 - val_loss: 0.2413 - val_acc: 0.9088
    Epoch 6/20
    55000/55000 [==============================] - 10s 174us/sample - loss: 0.2525 - acc: 0.9052 - val_loss: 0.2270 - val_acc: 0.9158
    Epoch 7/20
    55000/55000 [==============================] - 9s 163us/sample - loss: 0.2375 - acc: 0.9115 - val_loss: 0.2294 - val_acc: 0.9172
    Epoch 8/20
    55000/55000 [==============================] - 9s 163us/sample - loss: 0.2306 - acc: 0.9107 - val_loss: 0.2197 - val_acc: 0.9186
    Epoch 9/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.2170 - acc: 0.9173 - val_loss: 0.2165 - val_acc: 0.9198
    Epoch 10/20
    55000/55000 [==============================] - 9s 165us/sample - loss: 0.2110 - acc: 0.9200 - val_loss: 0.2181 - val_acc: 0.9160
    Epoch 11/20
    55000/55000 [==============================] - 9s 163us/sample - loss: 0.2021 - acc: 0.9235 - val_loss: 0.2230 - val_acc: 0.9222
    Epoch 12/20
    55000/55000 [==============================] - 9s 165us/sample - loss: 0.1973 - acc: 0.9248 - val_loss: 0.2282 - val_acc: 0.9200
    Epoch 13/20
    55000/55000 [==============================] - 9s 165us/sample - loss: 0.1899 - acc: 0.9288 - val_loss: 0.2201 - val_acc: 0.9162
    Epoch 14/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.1853 - acc: 0.9291 - val_loss: 0.2140 - val_acc: 0.9236
    Epoch 15/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.1787 - acc: 0.9317 - val_loss: 0.2265 - val_acc: 0.9204
    Epoch 16/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.1756 - acc: 0.9333 - val_loss: 0.2087 - val_acc: 0.9250
    Epoch 17/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.1680 - acc: 0.9357 - val_loss: 0.2250 - val_acc: 0.9170
    Epoch 18/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.1651 - acc: 0.9375 - val_loss: 0.2217 - val_acc: 0.9238
    Epoch 19/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.1588 - acc: 0.9399 - val_loss: 0.2241 - val_acc: 0.9254
    Epoch 20/20
    55000/55000 [==============================] - 9s 165us/sample - loss: 0.1616 - acc: 0.9383 - val_loss: 0.2200 - val_acc: 0.9208

<matplotlib.legend.Legend at 0x2113cca4fd0>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 2]

可以看出。训练精度有所下降，但是验证精度有所提升，Dropout 有效地抑制了过拟合。

# 保存模型
    
    model.save("my_keras_model.h5")

# 加载可用 models 命名空间里面的 load_model() 函数：
    model = tf.keras.models.load_model("my_keras_model.h5")

history = model.fit(x_train, y_train, epochs=20, validation_data=(x_valid, y_valid))

Train on 55000 samples, validate on 5000 samples
    Epoch 1/20
    55000/55000 [==============================] - 10s 185us/sample - loss: 0.1527 - acc: 0.9412 - val_loss: 0.2212 - val_acc: 0.9226
    Epoch 2/20
    55000/55000 [==============================] - 10s 179us/sample - loss: 0.1515 - acc: 0.9425 - val_loss: 0.2161 - val_acc: 0.9268
    Epoch 3/20
    55000/55000 [==============================] - 10s 178us/sample - loss: 0.1459 - acc: 0.9451 - val_loss: 0.2118 - val_acc: 0.9234
    Epoch 4/20
    55000/55000 [==============================] - 10s 180us/sample - loss: 0.1446 - acc: 0.9440 - val_loss: 0.2290 - val_acc: 0.9232
    Epoch 5/20
    55000/55000 [==============================] - 10s 179us/sample - loss: 0.1413 - acc: 0.9461 - val_loss: 0.2303 - val_acc: 0.9186
    Epoch 6/20
    55000/55000 [==============================] - 10s 176us/sample - loss: 0.1401 - acc: 0.9467 - val_loss: 0.2247 - val_acc: 0.9230
    Epoch 7/20
    55000/55000 [==============================] - 10s 179us/sample - loss: 0.1375 - acc: 0.9472 - val_loss: 0.2225 - val_acc: 0.9266
    Epoch 8/20
    55000/55000 [==============================] - 10s 178us/sample - loss: 0.1312 - acc: 0.9489 - val_loss: 0.2249 - val_acc: 0.9248
    Epoch 9/20
    55000/55000 [==============================] - 10s 177us/sample - loss: 0.1300 - acc: 0.9496 - val_loss: 0.2330 - val_acc: 0.9238
    Epoch 10/20
    55000/55000 [==============================] - 10s 179us/sample - loss: 0.1294 - acc: 0.9519 - val_loss: 0.2330 - val_acc: 0.9288
    Epoch 11/20
    55000/55000 [==============================] - 10s 175us/sample - loss: 0.1274 - acc: 0.9515 - val_loss: 0.2265 - val_acc: 0.9262
    Epoch 12/20
    55000/55000 [==============================] - 10s 180us/sample - loss: 0.1264 - acc: 0.9516 - val_loss: 0.2454 - val_acc: 0.9206
    Epoch 13/20
    55000/55000 [==============================] - 10s 177us/sample - loss: 0.1231 - acc: 0.9519 - val_loss: 0.2327 - val_acc: 0.9256
    Epoch 14/20
    55000/55000 [==============================] - 10s 176us/sample - loss: 0.1238 - acc: 0.9531 - val_loss: 0.2210 - val_acc: 0.9262
    Epoch 15/20
    55000/55000 [==============================] - 10s 181us/sample - loss: 0.1185 - acc: 0.9541 - val_loss: 0.2355 - val_acc: 0.9232
    Epoch 16/20
    55000/55000 [==============================] - 10s 178us/sample - loss: 0.1186 - acc: 0.9555 - val_loss: 0.2252 - val_acc: 0.9268
    Epoch 17/20
    55000/55000 [==============================] - 10s 178us/sample - loss: 0.1157 - acc: 0.9553 - val_loss: 0.2346 - val_acc: 0.9260
    Epoch 18/20
    55000/55000 [==============================] - 10s 177us/sample - loss: 0.1195 - acc: 0.9551 - val_loss: 0.2319 - val_acc: 0.9242
    Epoch 19/20
    55000/55000 [==============================] - 10s 179us/sample - loss: 0.1128 - acc: 0.9569 - val_loss: 0.2406 - val_acc: 0.9212
    Epoch 20/20
    55000/55000 [==============================] - 10s 181us/sample - loss: 0.1125 - acc: 0.9573 - val_loss: 0.2504 - val_acc: 0.9276

# 总结 Keras #

keras总体流程  
\[外链图片转存失败(img-JDuioUCs-1568382806197)(attachment:image.png)\]

import pandas as pd 
    import numpy as np
    import tensorflow as tf
    import matplotlib.pyplot as plt
    from tensorflow.keras import Sequential
    from tensorflow.keras.layers import Flatten, Dense, Conv2D, MaxPooling2D, Dropout
    from tensorflow.keras.datasets import fashion_mnist
    %matplotlib inline
    
    # 1、加载数据
    (x_train_full, y_train_full), (x_test, y_test) = fashion_mnist.load_data()
    x_train_full = x_train_full.reshape(60000, 28, 28, 1)
    x_test = x_test.reshape(10000, 28, 28, 1) 
    x_valid, x_train = x_train_full[:5000]/255.0, x_train_full[5000:] / 255.0
    y_valid, y_train = y_train_full[:5000], y_train_full[5000:]
    
    
    # 2、构建模型
    model = Sequential([Conv2D(64, (3,3), activation='relu', input_shape=(28, 28, 1)),
                               MaxPooling2D(2,2),
                               Conv2D(64, (3,3), activation='relu'),
                               MaxPooling2D(2,2),
                               Flatten(),
                               Dropout(0.5),
                               Dense(128, activation='relu'),
                               Dense(10, activation='softmax')])
    
    # 3、编译模型
    model.compile(loss='sparse_categorical_crossentropy',
                  optimizer='adam',
                  metrics=['acc'])
    
    # 4、拟合模型
    history = model.fit(x_train, y_train, epochs=20, validation_data=(x_valid, y_valid))
    
    # 5、评估模型
    # model.predict(x_test)
    # model.predict_class(x_test)
    model.evaluate(x_test, y_test)
    
    # 6、保存模板
    model.save("my_keras_model.h5")
    # 7、调用模板
    model = tf.keras.models.load_model("my_keras_model.h5")

Train on 55000 samples, validate on 5000 samples
    Epoch 1/20
    55000/55000 [==============================] - 10s 176us/sample - loss: 0.5164 - acc: 0.8091 - val_loss: 0.3403 - val_acc: 0.8806
    Epoch 2/20
    55000/55000 [==============================] - 10s 173us/sample - loss: 0.3575 - acc: 0.8678 - val_loss: 0.3001 - val_acc: 0.8872
    Epoch 3/20
    55000/55000 [==============================] - 9s 172us/sample - loss: 0.3096 - acc: 0.8844 - val_loss: 0.2651 - val_acc: 0.9026
    Epoch 4/20
    55000/55000 [==============================] - 9s 166us/sample - loss: 0.2854 - acc: 0.8941 - val_loss: 0.2607 - val_acc: 0.9040
    Epoch 5/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.2641 - acc: 0.9009 - val_loss: 0.2316 - val_acc: 0.9122
    Epoch 6/20
    55000/55000 [==============================] - 9s 162us/sample - loss: 0.2494 - acc: 0.9055 - val_loss: 0.2283 - val_acc: 0.9156
    Epoch 7/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.2356 - acc: 0.9109 - val_loss: 0.2252 - val_acc: 0.9152
    Epoch 8/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.2228 - acc: 0.9153 - val_loss: 0.2217 - val_acc: 0.9220
    Epoch 9/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.2160 - acc: 0.9177 - val_loss: 0.2171 - val_acc: 0.9206
    Epoch 10/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.2028 - acc: 0.9226 - val_loss: 0.2163 - val_acc: 0.9208
    Epoch 11/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.2014 - acc: 0.9228 - val_loss: 0.2114 - val_acc: 0.9196
    Epoch 12/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.1943 - acc: 0.9271 - val_loss: 0.2094 - val_acc: 0.9244
    Epoch 13/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.1854 - acc: 0.9282 - val_loss: 0.2132 - val_acc: 0.9216
    Epoch 14/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.1811 - acc: 0.9312 - val_loss: 0.2205 - val_acc: 0.9144
    Epoch 15/20
    55000/55000 [==============================] - 9s 163us/sample - loss: 0.1745 - acc: 0.9323 - val_loss: 0.2197 - val_acc: 0.9212
    Epoch 16/20
    55000/55000 [==============================] - 9s 165us/sample - loss: 0.1700 - acc: 0.9356 - val_loss: 0.2233 - val_acc: 0.9188
    Epoch 17/20
    55000/55000 [==============================] - 9s 163us/sample - loss: 0.1652 - acc: 0.9359 - val_loss: 0.2164 - val_acc: 0.9240
    Epoch 18/20
    55000/55000 [==============================] - 9s 163us/sample - loss: 0.1596 - acc: 0.9390 - val_loss: 0.2099 - val_acc: 0.9224
    Epoch 19/20
    55000/55000 [==============================] - 9s 164us/sample - loss: 0.1596 - acc: 0.9391 - val_loss: 0.2052 - val_acc: 0.9270
    Epoch 20/20
    55000/55000 [==============================] - 9s 166us/sample - loss: 0.1586 - acc: 0.9395 - val_loss: 0.2287 - val_acc: 0.9242
    10000/10000 [==============================] - 1s 70us/sample - loss: 98.0071 - acc: 0.7226
    
    
    
    
    [98.00711212615967, 0.7226]

# 结果可视化
    acc = history.history.get('acc')
    val_acc = history.history.get('val_acc')
    loss = history.history.get('loss')
    val_loss = history.history.get('val_loss')
    
    epochs = range(1, len(acc)+1)
    plt.figure(figsize=(8,4),dpi=100)
    plt.subplot(1, 2, 1)
    plt.plot(epochs, acc, 'bo', label='Traing acc')
    plt.plot(epochs, val_acc, 'b', label='Validation acc')
    plt.legend()
    
    plt.subplot(1, 2, 2)
    plt.plot(epochs, loss, 'ro', label='Traing loss')
    plt.plot(epochs, val_loss, 'r', label='Validation val_lossc')
    plt.legend()

<matplotlib.legend.Legend at 0x21140646f60>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 3]  
编写简单的深度学习项目完全可以套用此模板  
希望对大家有所帮助

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[20190913215507137.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/feeedecc56b14ef886a8211656878052.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 1]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/eefca3c511a04573bbd88442c8761afe.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 2]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/4bd70add9a5c40dbabdf0b8d0729233f.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 3]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/44d5cf5879ac4a7baa3edaeae6691941.png