tensorflow,keras手寫數據集分類

tensorflow手寫數據集分類

所使用的環境及數據集;
tensorflow 1.15.0
本地MNIST數據包
以下爲代碼

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# 載入數據集
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)

# 批次大小
batch_size = 64
# 計算一個週期一共有多少個批次
n_batch = mnist.train.num_examples // batch_size

# 定義兩個placeholder
x = tf.placeholder(tf.float32,[None,784])
y = tf.placeholder(tf.float32,[None,10])

# 創建一個簡單的神經網絡:784-10
W = tf.Variable(tf.truncated_normal([784,10], stddev=0.1))
b = tf.Variable(tf.zeros([10]) + 0.1)
prediction = tf.nn.softmax(tf.matmul(x,W)+b)

# 二次代價函數
loss = tf.losses.mean_squared_error(y, prediction)
# 使用梯度下降法
train = tf.train.GradientDescentOptimizer(0.3).minimize(loss)

# 結果存放在一個布爾型列表中
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))
# 求準確率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

with tf.Session() as sess:
    # 變量初始化
    sess.run(tf.global_variables_initializer())
    # 週期epoch：所有數據訓練一次，就是一個週期
    for epoch in range(21):
        for batch in range(n_batch):
            # 獲取一個批次的數據和標籤
            batch_xs,batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train,feed_dict={x:batch_xs,y:batch_ys})
        # 每訓練一個週期做一次測試
        acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))
        

keras手寫數據集分類

import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD

# 載入數據
(x_train,y_train),(x_test,y_test) = mnist.load_data()
# (60000,28,28)
print('x_shape:',x_train.shape)
# (60000)
print('y_shape:',y_train.shape)
# (60000,28,28)->(60000,784)
x_train = x_train.reshape(x_train.shape[0],-1)/255.0
x_test = x_test.reshape(x_test.shape[0],-1)/255.0
# 講標籤轉換爲one hot格式(即獨熱編碼格式)
y_train = np_utils.to_categorical(y_train,num_classes=10)
y_test = np_utils.to_categorical(y_test,num_classes=10)

# 創建模型，輸入784個神經元，輸出10個神經元
model = Sequential([
        Dense(units=10,input_dim=784,bias_initializer='one',activation='softmax')
    ])

# 定義優化器
sgd = SGD(lr=0.2)

# 定義優化器，loss function，訓練過程中計算準確率
model.compile(
    optimizer = sgd,
    loss = 'mse',
    metrics=['accuracy'],
)

# 訓練模型
model.fit(x_train,y_train,batch_size=32,epochs=10)

# 評估模型
loss,accuracy = model.evaluate(x_test,y_test)

print('\ntest loss',loss)
print('accuracy',accuracy)