在我們使用tensorflow時，如果不能使用函數式api進行編程，那么一些復雜的神經網絡結構就不會實現出來，只能使用簡單的單向模型進行一層一層地堆疊。如果稍微復雜一點，遇到了Resnet這種帶有殘差模塊的神經網絡，那么用簡單的神經網絡堆疊的方式則不可能把這種網絡堆疊出來。下面我們來使用函數式API來編寫一個簡單的全連接神經網絡：
首先導包：
from tensorflow import keras import tensorflow as tf import pandas as pd import numpy as np import matplotlib.pyplot as plt
導入圖片數據集：mnist
(train_image,train_label),(test_image,test_label)=tf.keras.datasets.fashion_mnist.load_data()
歸一化：
train_image=train_image/255 test_image=test_image/255#進行數據的歸一化，加快計算的進程
搭建全連接神經網絡：
input=keras.Input(shape=(28,28)) x=keras.layers.Flatten()(input)#調用input x=keras.layers.Dense(32,activation="relu")(x) x=keras.layers.Dropout(0.5)(x)#一層一層的進行調用上一層的結果 output=keras.layers.Dense(10,activation="softmax")(x) model=keras.Model(inputs=input,outputs=output) model.summary()
輸出：
Model: "model" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) [(None, 28, 28)] 0 _________________________________________________________________ flatten (Flatten) (None, 784) 0 _________________________________________________________________ dense (Dense) (None, 32) 25120 _________________________________________________________________ dropout (Dropout) (None, 32) 0 _________________________________________________________________ dense_1 (Dense) (None, 10) 330 ================================================================= Total params: 25,450 Trainable params: 25,450 Non-trainable params: 0
擬合模型：
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss="sparse_categorical_crossentropy", metrics=['acc'] ) history=model.fit(train_image, train_label, epochs=15, validation_data=(test_image,test_label))
輸出：
Train on 60000 samples, validate on 10000 samples Epoch 1/15 60000/60000 [==============================] - 4s 64us/sample - loss: 0.8931 - acc: 0.6737 - val_loss: 0.5185 - val_acc: 0.8160 Epoch 2/15 60000/60000 [==============================] - 3s 57us/sample - loss: 0.6757 - acc: 0.7508 - val_loss: 0.4805 - val_acc: 0.8230 Epoch 3/15 60000/60000 [==============================] - 3s 50us/sample - loss: 0.6336 - acc: 0.7647 - val_loss: 0.4587 - val_acc: 0.8369 Epoch 4/15 60000/60000 [==============================] - 3s 49us/sample - loss: 0.6174 - acc: 0.7689 - val_loss: 0.4712 - val_acc: 0.8294 Epoch 5/15 60000/60000 [==============================] - 3s 48us/sample - loss: 0.6080 - acc: 0.7732 - val_loss: 0.4511 - val_acc: 0.8404 Epoch 6/15 60000/60000 [==============================] - 3s 48us/sample - loss: 0.5932 - acc: 0.7773 - val_loss: 0.4545 - val_acc: 0.8407 Epoch 7/15 60000/60000 [==============================] - 3s 47us/sample - loss: 0.5886 - acc: 0.7772 - val_loss: 0.4394 - val_acc: 0.8428 Epoch 8/15 60000/60000 [==============================] - 3s 52us/sample - loss: 0.5820 - acc: 0.7788 - val_loss: 0.4338 - val_acc: 0.8506 Epoch 9/15 60000/60000 [==============================] - 3s 48us/sample - loss: 0.5742 - acc: 0.7839 - val_loss: 0.4393 - val_acc: 0.8454 Epoch 10/15 60000/60000 [==============================] - 3s 49us/sample - loss: 0.5713 - acc: 0.7847 - val_loss: 0.4422 - val_acc: 0.8477 Epoch 11/15 60000/60000 [==============================] - 3s 47us/sample - loss: 0.5642 - acc: 0.7858 - val_loss: 0.4325 - val_acc: 0.8488 Epoch 12/15 60000/60000 [==============================] - 3s 48us/sample - loss: 0.5582 - acc: 0.7873 - val_loss: 0.4294 - val_acc: 0.8492 Epoch 13/15 60000/60000 [==============================] - 3s 48us/sample - loss: 0.5574 - acc: 0.7882 - val_loss: 0.4263 - val_acc: 0.8523 Epoch 14/15 60000/60000 [==============================] - 3s 48us/sample - loss: 0.5524 - acc: 0.7888 - val_loss: 0.4350 - val_acc: 0.8448 Epoch 15/15 60000/60000 [==============================] - 3s 47us/sample - loss: 0.5486 - acc: 0.7901 - val_loss: 0.4297 - val_acc: 0.8493
最后驗證集的精度達到了84%，這是一個僅僅使用全連接神經網絡和softmax就能夠得到的一個很不錯的結果了！