[深度之眼]TensorFlow2.0項目班-三種思路構建fashion_mnist圖像識別模型

三種思路構建fashion_mnist圖像識別模型

數據是對衣服、褲子、鞋子等圖片的十分類
模型很簡單，主要讓大家區分三種建模思路的區別，直接上代碼：

import tensorflow as tf

fashion = tf.keras.datasets.fashion_mnist
(x_train, y_train),(x_test, y_test) = fashion.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# 順序模型
model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')])

#函數模型
inputs = tf.keras.Input(shape=(28,28))
x = tf.keras.layers.Flatten()(inputs)
x = tf.keras.layers.Dense(128,activation='relu')(x)
predictions = tf.keras.layers.Dense(10,activation='softmax')(x)
model = tf.keras.Model(inputs,predictions)

#子類化模型
class MyModel(tf.keras.Model):
    def __init__(self):
        super(MyModel,self).__init__()
        self.D1 = tf.keras.layers.Flatten()
        self.D2 = tf.keras.layers.Dense(128,activation='relu')
        self.D3 = tf.keras.layers.Dense(10,activation='softmax')

    def call(self,inputs):
        x = self.D1(inputs)
        x = self.D2(x)
        x = self.D3(x)
        return x
model = MyModel()


model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['sparse_categorical_accuracy'])

model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
model.summary()

三種模型的網絡結構上是一樣的，輸出結果如下：

識別準確度有待提高，畢竟模型很簡單嘛，大家可以把dense層換成CNN，提升很高！