前向傳播(張量)- 實戰

目錄

手寫数字識別流程

  • MNIST手寫数字集7000*10張圖片
  • 60k張圖片訓練,10k張圖片測試
  • 每張圖片是28*28,如果是彩色圖片是28*28*3
  • 0-255表示圖片的灰度值,0表示純白,255表示純黑
  • 打平28*28的矩陣,得到28*28=784的向量
  • 對於b張圖片得到[b,784];然後對於b張圖片可以給定編碼
  • 把上述的普通編碼給定成獨熱編碼,但是獨熱編碼都是概率值,並且概率值相加為1,類似於softmax回歸
  • 套用線性回歸公式
  • X[b,784] W[784,10] b[10] 得到 [b,10]
  • 高維圖片實現非常複雜,一個線性模型無法完成,因此可以添加非線性因子
  • f(X@W+b),使用激活函數讓其非線性化,引出relu函數
  • 用了激活函數,模型還是太簡單
  • 使用工廠
    • H1 =relu(X@W1+b1)
    • H2 = relu(h1@W2+b2)
    • Out = relu(h2@W3+b3)
  • 第一步,把[1,784]變成[1,512]變成[1,256]變成[1,10]
  • 得到[1,10]后將結果進行獨熱編碼
  • 使用歐氏距離或者使用mse進行誤差度量
  • [1,784]通過三層網絡輸出一個[1,10]

前向傳播(張量)- 實戰

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets
import os
# do not print irrelevant information
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# x: [60k,28,28]
# y: [60k]
(x, y), _ = datasets.mnist.load_data()
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 1s 0us/step
# transform Tensor
# x: [0~255] ==》 [0~1.]
x = tf.convert_to_tensor(x, dtype=tf.float32) / 255.
y = tf.convert_to_tensor(y, dtype=tf.int32)
f'x.shape: {x.shape}, y.shape: {y.shape}, x.dtype: {x.dtype}, y.dtype: {y.dtype}'
"x.shape: (60000, 28, 28), y.shape: (60000,), x.dtype: <dtype: 'float32'>, y.dtype: <dtype: 'int32'>"
f'min_x: {tf.reduce_min(x)}, max_x: {tf.reduce_max(x)}'
'min_x: 0.0, max_x: 1.0'
f'min_y: {tf.reduce_min(y)}, max_y: {tf.reduce_max(y)}'
'min_y: 0, max_y: 9'
# batch of 128
train_db = tf.data.Dataset.from_tensor_slices((x, y)).batch(128)
train_iter = iter(train_db)
sample = next(train_iter)
f'batch: {sample[0].shape,sample[1].shape}'
'batch: (TensorShape([128, 28, 28]), TensorShape([128]))'
# [b,784] ==> [b,256] ==> [b,128] ==> [b,10]
# [dim_in,dim_out],[dim_out]
w1 = tf.Variable(tf.random.truncated_normal([784, 256], stddev=0.1))
b1 = tf.Variable(tf.zeros([256]))
w2 = tf.Variable(tf.random.truncated_normal([256, 128], stddev=0.1))
b2 = tf.Variable(tf.zeros([128]))
w3 = tf.Variable(tf.random.truncated_normal([128, 10], stddev=0.1))
b3 = tf.Variable(tf.zeros([10]))
# learning rate
lr = 1e-3
for epoch in range(10):  # iterate db for 10
    # tranin every train_db
    for step, (x, y) in enumerate(train_db):
        # x: [128,28,28]
        # y: [128]

        # [b,28,28] ==> [b,28*28]
        x = tf.reshape(x, [-1, 28*28])

        with tf.GradientTape() as tape:  # only data types of tf.variable are logged
            # x: [b,28*28]
            # h1 = x@w1 + b1
            # [b,784]@[784,256]+[256] ==> [b,256] + [256] ==> [b,256] + [b,256]
            h1 = x @ w1 + tf.broadcast_to(b1, [x.shape[0], 256])
            h1 = tf.nn.relu(h1)
            # [b,256] ==> [b,128]
            # h2 = x@w2 + b2  # b2 can broadcast automatic
            h2 = h1 @ w2 + b2
            h2 = tf.nn.relu(h2)
            # [b,128] ==> [b,10]
            out = h2 @ w3 + b3

            # compute loss
            # out: [b,10]
            # y:[b] ==> [b,10]
            y_onehot = tf.one_hot(y, depth=10)

            # mse = mean(sum(y-out)^2)
            # [b,10]
            loss = tf.square(y_onehot - out)
            # mean:scalar
            loss = tf.reduce_mean(loss)

        # compute gradients
        grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3])
        # w1 = w1 - lr * w1_grad
        # w1 = w1 - lr * grads[0]  # not in situ update
        # in situ update
        w1.assign_sub(lr * grads[0])
        b1.assign_sub(lr * grads[1])
        w2.assign_sub(lr * grads[2])
        b2.assign_sub(lr * grads[3])
        w3.assign_sub(lr * grads[4])
        b3.assign_sub(lr * grads[5])

        if step % 100 == 0:
            print(f'epoch:{epoch}, step: {step}, loss:{float(loss)}')
epoch:0, step: 0, loss:0.5366693735122681
epoch:0, step: 100, loss:0.23276552557945251
epoch:0, step: 200, loss:0.19647717475891113
epoch:0, step: 300, loss:0.17389704287052155
epoch:0, step: 400, loss:0.1731622964143753
epoch:1, step: 0, loss:0.16157487034797668
epoch:1, step: 100, loss:0.16654588282108307
epoch:1, step: 200, loss:0.15311869978904724
epoch:1, step: 300, loss:0.14135733246803284
epoch:1, step: 400, loss:0.14423415064811707
epoch:2, step: 0, loss:0.13703864812850952
epoch:2, step: 100, loss:0.14255204796791077
epoch:2, step: 200, loss:0.1302051544189453
epoch:2, step: 300, loss:0.12224273383617401
epoch:2, step: 400, loss:0.12742099165916443
epoch:3, step: 0, loss:0.1219201311469078
epoch:3, step: 100, loss:0.12757658958435059
epoch:3, step: 200, loss:0.11587800830602646
epoch:3, step: 300, loss:0.10984969139099121
epoch:3, step: 400, loss:0.11641304194927216
epoch:4, step: 0, loss:0.11171815544366837
epoch:4, step: 100, loss:0.11717887222766876
epoch:4, step: 200, loss:0.10604140907526016
epoch:4, step: 300, loss:0.10111508518457413
epoch:4, step: 400, loss:0.10865814983844757
epoch:5, step: 0, loss:0.10434548556804657
epoch:5, step: 100, loss:0.10952303558588028
epoch:5, step: 200, loss:0.09875871241092682
epoch:5, step: 300, loss:0.09467941522598267
epoch:5, step: 400, loss:0.10282392799854279
epoch:6, step: 0, loss:0.09874211996793747
epoch:6, step: 100, loss:0.10355912148952484
epoch:6, step: 200, loss:0.09315416216850281
epoch:6, step: 300, loss:0.08971598744392395
epoch:6, step: 400, loss:0.0982089415192604
epoch:7, step: 0, loss:0.09428335726261139
epoch:7, step: 100, loss:0.09877124428749084
epoch:7, step: 200, loss:0.08866965025663376
epoch:7, step: 300, loss:0.08573523908853531
epoch:7, step: 400, loss:0.09440126270055771
epoch:8, step: 0, loss:0.09056715667247772
epoch:8, step: 100, loss:0.09483197331428528
epoch:8, step: 200, loss:0.0849832147359848
epoch:8, step: 300, loss:0.08246967941522598
epoch:8, step: 400, loss:0.09117519855499268
epoch:9, step: 0, loss:0.08741479367017746
epoch:9, step: 100, loss:0.09150294959545135
epoch:9, step: 200, loss:0.08185736835002899
epoch:9, step: 300, loss:0.07972464710474014
epoch:9, step: 400, loss:0.08842341601848602
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