1.下载fashion_mnist数据
使用以下三行代码下载fashion mnist数据

from tensorflow import keras fashion_mnist = keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

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查看数据信息：

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构建网络：

import tensorflow as tf from tensorflow import keras fashion_mnist = keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()model = keras.Sequential() model.add(keras.layers.Flatten(input_shape=(28,28))) model.add(keras.layers.Dense(128,activation=tf.nn.relu)) model.add(keras.layers.Dense(10,activation=tf.nn.softmax)) model.summary()

模型参数

Model: "sequential_2" _________________________________________________________________ Layer (type)Output ShapeParam # ================================================================= flatten_1 (Flatten)(None, 784)0 _________________________________________________________________ dense_2 (Dense)(None, 128)100480 _________________________________________________________________ dense_3 (Dense)(None, 10)1290 ================================================================= Total params: 101,770 Trainable params: 101,770 Non-trainable params: 0

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这是一个全链接的神经网络结构,结构如下：

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每一层均是全连接

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中间层的参数计算过程为:
784 * 128(weight) + 128(bias) = 100480.
最终层的参数计算过程为：
128 * 10（weight) + 10(bias) = 1290.
计算结果符合summary方法给出的统计数据。
训练模型

model.compile(optimizer=tf.optimizers.Adam(),loss=tf.losses.sparse_categorical_crossentropy,metrics=['accuracy']) model.fit(train_images,train_labels,epochs=50)

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Epoch 1/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.5283 - accuracy: 0.8235 Epoch 2/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.5076 - accuracy: 0.8300 Epoch 3/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4954 - accuracy: 0.8316 Epoch 4/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4892 - accuracy: 0.8350 Epoch 5/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4853 - accuracy: 0.8364 Epoch 6/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4847 - accuracy: 0.8388 Epoch 7/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4769 - accuracy: 0.8414 Epoch 8/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4733 - accuracy: 0.8407 Epoch 9/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4796 - accuracy: 0.8410 Epoch 10/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4935 - accuracy: 0.8381: 0s - loss: 0.4940 - accura Epoch 11/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4756 - accuracy: 0.8408 Epoch 12/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4745 - accuracy: 0.8424 Epoch 13/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4707 - accuracy: 0.8434 Epoch 14/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4693 - accuracy: 0.8434 Epoch 15/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4614 - accuracy: 0.8463 Epoch 16/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4617 - accuracy: 0.8468 Epoch 17/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4481 - accuracy: 0.8492 Epoch 18/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4638 - accuracy: 0.8464 Epoch 19/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4624 - accuracy: 0.8481 Epoch 20/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4500 - accuracy: 0.8501 Epoch 21/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4546 - accuracy: 0.8479 Epoch 22/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4560 - accuracy: 0.8495 Epoch 23/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4552 - accuracy: 0.8490 Epoch 24/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4492 - accuracy: 0.8501 Epoch 25/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4497 - accuracy: 0.8525 Epoch 26/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4489 - accuracy: 0.8495 Epoch 27/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4464 - accuracy: 0.8515 Epoch 28/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4529 - accuracy: 0.8507 Epoch 29/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4495 - accuracy: 0.8526 Epoch 30/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4454 - accuracy: 0.8508 Epoch 31/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4578 - accuracy: 0.8513 Epoch 32/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4434 - accuracy: 0.8534 Epoch 33/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4387 - accuracy: 0.8541 Epoch 34/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4481 - accuracy: 0.8536 Epoch 35/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4448 - accuracy: 0.8536: Epoch 36/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4459 - accuracy: 0.8540 Epoch 37/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4397 - accuracy: 0.8537 Epoch 38/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4399 - accuracy: 0.8559 Epoch 39/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4528 - accuracy: 0.8530 Epoch 40/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4404 - accuracy: 0.8561 Epoch 41/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4433 - accuracy: 0.8540 Epoch 42/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4354 - accuracy: 0.8555 Epoch 43/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4319 - accuracy: 0.8572 Epoch 44/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4408 - accuracy: 0.8557 Epoch 45/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4708 - accuracy: 0.8440 Epoch 46/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4461 - accuracy: 0.8542 Epoch 47/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4337 - accuracy: 0.8584 Epoch 48/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4346 - accuracy: 0.8577 Epoch 49/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4386 - accuracy: 0.8569 Epoch 50/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4505 - accuracy: 0.8560

可以看到，经过50次的训练，正确率在%85左右徘徊 ，中间经过几次周旋倒退，最终总体上还是在正确率上升的轨道上进行，稳定副在%85.6
加入normalization，提升正确率
说白了，正规化就是这样一个过程：

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将数据正规化，转换为[0,1]区间的数据

train_images = train_images/255 model.compile(optimizer=tf.optimizers.Adam(),loss=tf.losses.sparse_categorical_crossentropy,metrics=['accuracy']) model.fit(train_images,train_labels,epochs=50)

Epoch 1/50 1875/1875 [==============================] - 2s 1ms/step - loss: 1.0806 - accuracy: 0.6693 Epoch 2/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.6354 - accuracy: 0.7707 Epoch 3/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.5593 - accuracy: 0.8002 Epoch 4/50 1875/1875 [==============================] - 3s 1ms/step - loss: 0.5169 - accuracy: 0.8167 Epoch 5/50 1875/1875 [==============================] - 3s 1ms/step - loss: 0.4904 - accuracy: 0.8270 Epoch 6/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4709 - accuracy: 0.8342 Epoch 7/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4553 - accuracy: 0.8400 Epoch 8/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4430 - accuracy: 0.8442 Epoch 9/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4333 - accuracy: 0.8471 Epoch 10/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4246 - accuracy: 0.8502 Epoch 11/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4172 - accuracy: 0.8526 Epoch 12/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4102 - accuracy: 0.8553 Epoch 13/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.4045 - accuracy: 0.8586 Epoch 14/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3987 - accuracy: 0.8605 Epoch 15/50 1875/1875 [==============================] - 3s 1ms/step - loss: 0.3939 - accuracy: 0.8608 Epoch 16/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3904 - accuracy: 0.8626 Epoch 17/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3858 - accuracy: 0.8647 Epoch 18/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3818 - accuracy: 0.8654 Epoch 19/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3778 - accuracy: 0.8677 Epoch 20/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3745 - accuracy: 0.8683 Epoch 21/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3718 - accuracy: 0.8690 Epoch 22/50 1875/1875 [==============================] - 3s 1ms/step - loss: 0.3680 - accuracy: 0.8701 Epoch 23/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3653 - accuracy: 0.8717 Epoch 24/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3620 - accuracy: 0.8724 Epoch 25/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3594 - accuracy: 0.8736 Epoch 26/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3569 - accuracy: 0.8743 Epoch 27/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3543 - accuracy: 0.8750 Epoch 28/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3521 - accuracy: 0.8763 Epoch 29/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3492 - accuracy: 0.8766 Epoch 30/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3467 - accuracy: 0.8773 Epoch 31/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3445 - accuracy: 0.8788 Epoch 32/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3424 - accuracy: 0.8783 Epoch 33/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3403 - accuracy: 0.8801 Epoch 34/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3379 - accuracy: 0.8795 Epoch 35/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3360 - accuracy: 0.8816 Epoch 36/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3338 - accuracy: 0.8817 Epoch 37/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3317 - accuracy: 0.8824 Epoch 38/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3296 - accuracy: 0.8832 Epoch 39/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3271 - accuracy: 0.8837 Epoch 40/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3257 - accuracy: 0.8842 Epoch 41/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3236 - accuracy: 0.8846 Epoch 42/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3211 - accuracy: 0.8864 Epoch 43/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3196 - accuracy: 0.8859 Epoch 44/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3174 - accuracy: 0.8868 Epoch 45/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3162 - accuracy: 0.8874 Epoch 46/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3144 - accuracy: 0.8877 Epoch 47/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3124 - accuracy: 0.8884 Epoch 48/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3108 - accuracy: 0.8894 Epoch 49/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3086 - accuracy: 0.8895 Epoch 50/50 1875/1875 [==============================] - 2s 1ms/step - loss: 0.3074 - accuracy: 0.8897

可以看到，最终的正确率比正规化之前有所提升。
利用模型进行推理

test_images_scaled=test_images/255 model.evaluate(test_images_scaled,test_labels)

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验证非正规化的参数推理：

import numpy as np demo = tf.reshape(test_images[0],(1,28,28)) print(np.argmax(model.predict(demo))) print(test_labels[0])

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如果训练的时候，输入经过了正规化，则推理也需要正规化：

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通过上面几次的训练过程输出的准确率差异我们可以总结出，同一个网络模型，不同的时间进行训练，可以得到完全不同的正确率，这也说明模型本身的复杂性和时变性，至于为何会发生， 模型的大量参数组成一个高维的数据空间，我们用梯度下降法寻找最优解的时候，依赖于初始参数权重和BIAS在高维空间中的位置，不同次的训练过程，初始化参数在空间中的位置不同，优化的方向则不同，最终的最优解也不相同，所以可以看出，模型训练是一个非常复杂的数学计算过程，并且很可能寻找到的最优解是局部最优解，而并非全局最优解。
调试
得到每层的名称：

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得到权重和BIAS

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【嵌入式系统|基于keras的fashion_mnist模型训练过程】
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可以看到，权重的维度和上文中的计算是相符合的。
参考排错：
ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape_善良995的博客-CSDN博客
结束！

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