本博客與手寫數字識別大同小異。

1.導入所需庫

import tensorflow as tf from tensorflow.keras import datasets, layers, models from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D from tensorflow.keras.preprocessing.image import ImageDataGenerator import os import numpy as np import matplotlib.pyplot as plt

2.數據準備

本階段需要做的工作：
①下載好我們所需要的服裝圖像庫。
②將圖片標準化。
③調整圖像的大小。
（類比與手寫數字識別的數據處理階段）
訓練集和測試集分別為60000和10000

def DataPre():# 導入數據(train_x, train_y), (test_x, test_y) = datasets.fashion_mnist.load_data()# 標準化train_x, test_x = train_x / 255.0, test_x / 255.0# 調整數據？？？train_x = train_x.reshape((60000, 28, 28, 1))test_x = test_x.reshape((10000, 28, 28, 1))return train_x,train_y,test_x,test_y

3.搭建網絡

網絡結構為：3層卷積池化層+Flatten+兩層全連接層。
（可以嘗試一下更深的網絡結構，硬件條件允許的情況下

def ModelBuild():# 搭建模型model = models.Sequential([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(64, (3, 3), activation='relu'),layers.Flatten(),layers.Dense(64, activation=tf.nn.softmax),layers.Dense(10)])model.summary() # 打印網絡結構model.compile(optimizer = 'adam',loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics = ['accuracy'])return model

網絡模型為：

Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d (Conv2D) (None, 26, 26, 32) 320 _________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 13, 13, 32) 0 _________________________________________________________________ conv2d_1 (Conv2D) (None, 11, 11, 64) 18496 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 3, 3, 64) 36928 _________________________________________________________________ flatten (Flatten) (None, 576) 0 _________________________________________________________________ dense (Dense) (None, 0) 0 _________________________________________________________________ dense_1 (Dense) (None, 64) 64 _________________________________________________________________ dense_2 (Dense) (None, 10) 650 ================================================================= Total params: 56,458 Trainable params: 56,458 Non-trainable params: 0 _________________________________________________________________ Train on 60000 samples, validate on 10000 samples

4.模型訓練

由于硬件原因，epochs設置的是10，經過實驗證明，epochs在50的時候，效果是明顯好于10的情況。

def Modeltrain(model,train_x,train_y,test_x,test_y):# 訓練模型history = model.fit(train_x, train_y, epochs=10,validation_data=(test_x, test_y))return history

5.結果可視化

accuracy = history.history["accuracy"]test_accuracy = history.history["val_accuracy"]loss = history.history["loss"]test_loss = history.history["val_loss"]epochs_range = range(10)plt.figure(figsize=(50, 5))plt.subplot(1, 2, 1)plt.plot(epochs_range, accuracy, label="Training Acc")plt.plot(epochs_range, test_accuracy, label="Test Acc")plt.legend()plt.title("Training and Test Acc")plt.subplot(1, 2, 2)plt.plot(epochs_range, loss, label="Training loss")plt.plot(epochs_range, test_loss, label="Test loss")plt.legend()plt.title("Training and Test loss")plt.show()

6.結果

10000/1 - 1s - loss: 0.2716 - accuracy: 0.8840

并沒有出現過擬合的情況，但是準確率并不是特別高，在增加epochs的情況下是可以提高準確率的。但是訓練速度會明顯變慢，而且提升效果并不大。可以嘗試一下利用遷移學習，利用別人搭建好的網絡，準確率可能會上升。
努力加油a啊

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