最簡單用TensorFlow實現(xiàn)CNN（cifar10數(shù)據(jù)集）

用TensorFlow實現(xiàn)CNN識別cifar10數(shù)據(jù)集。

知識回顧：

CNN的本質(zhì)是先把數(shù)據(jù)進(jìn)行特征提取，再送進(jìn)DNN。前面特征提取的部分可以概括為CBAPD，C表示卷積（convolution），B表示批標(biāo)準(zhǔn)化（batch normalization），A表示激活（activation），P表示池化（pool），D表示（dropout）

全代碼：

import tensorflow as tf
import numpy as np
from matplotlib import pyplot as plt
from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Dropout, Flatten, Dense
from tensorflow.keras import Model

cifar10 = tf.keras.datasets.cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0


class Baseline(Model):
    def __init__(self):
        super(Baseline, self).__init__()
        self.c1 = Conv2D(filters=6, kernel_size=(5, 5), padding='same')  # 卷積層
        self.b1 = BatchNormalization()  # BN層
        self.a1 = Activation('relu')  # 激活層
        self.p1 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')  # 池化層
        self.d1 = Dropout(0.2)  # dropout層

        self.flatten = Flatten()
        self.f1 = Dense(128, activation='relu')
        self.d2 = Dropout(0.2)
        self.f2 = Dense(10, activation='softmax')

    def call(self, x):
        x = self.c1(x)
        x = self.b1(x)
        x = self.a1(x)
        x = self.p1(x)
        x = self.d1(x)

        x = self.flatten(x)
        x = self.f1(x)
        x = self.d2(x)
        y = self.f2(x)
        return y


model = Baseline()

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

history = model.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_test, y_test), validation_freq=1)
model.summary()



# 搞定，畫圖，顯示訓(xùn)練集和驗證集的acc和loss曲線
acc = history.history['sparse_categorical_accuracy']
val_acc = history.history['val_sparse_categorical_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

plt.subplot(1, 2, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()
plt.show()

為了快速出結(jié)果，epoch只設(shè)了10，所以準(zhǔn)確率不高，batch_size設(shè)高點，epoch設(shè)高點，結(jié)果應(yīng)該會變很理想。

感興趣可以看看這篇！《一文極速理解深度學(xué)習(xí)》