\u9996\u5148\u5bfc\u5305\uff1a<\/p>\n\n\n
import tensorflow as tf\nprint(tf.__version__)\nfrom matplotlib import pyplot as plt\nimport random\n<\/pre>\n\n\n\u8f93\u51fa\uff1a<\/p>\n\n\n
2.1.0\n<\/pre>\n\n\n\u7136\u540e\u6211\u4eec\u751f\u6210\u6570\u636e\uff0c\u9996\u5148\u6a21\u62df\u4e00\u4e2a\u771f\u5b9e\u53c2\u6570w\u548cb\uff0c\u7136\u540e\u968f\u673a\u751f\u6210\u4e00\u4e9b\u6570\u636e\u901a\u8fc7\u53c2\u6570\u751f\u6210\u6570\u636e\uff0c\u7136\u540e\u52a0\u4e00\u70b9\u6df7\u6dc6\u3002<\/p>\n\n\n
\u8fd9\u91cc\u6211\u4eec\u9009\u75282\u4e2a\u7279\u5f81\uff0c\u751f\u62101000\u4e2a\u6570\u636e\uff0cw\u5206\u522b\u4e3a2\u548c-3.4\uff0cb\u4e3a4.2\u3002<\/p>\n\n\n
num_inputs = 2\nnum_examples = 1000\ntrue_w = [2,-3.4]\ntrue_b = 4.2\n# \u751f\u6210\u6807\u51c6\u5dee\u4e3a1\u7684\u8f93\u5165\u6570\u636e\nfeatures = tf.random.normal((num_examples,num_inputs),stddev=1)\nlabels = true_w[0]*features[:,0]+true_w[1]*features[:,1]+true_b\n# \u968f\u673a\u52a0\u4e0a\u6807\u51c6\u5dee\u4e3a0.01\u7684\u566a\u97f3\nlabels += tf.random.normal(labels.shape,stddev=0.01)<\/pre>\n\n\n\u7136\u540e\u6211\u4eec\u8f93\u51fa\u4e00\u4e0b\u7279\u5f81\u548c\u6807\u7b7e\uff1a<\/p>\n\n\n
print(features[0],labels[0])\n<\/pre>\n\n\n\u8f93\u51fa\uff1a<\/p>\n\n\n
tf.Tensor([ 0.60977983 -0.21984003], shape=(2,), dtype=float32) tf.Tensor(6.182882, shape=(), dtype=float32)\n<\/pre>\n\n\n\u753b\u51fa\u6563\u5217\u70b9\u56fe\uff1a<\/p>\n\n\n
def set_figsize(figsize=(3.5,2.5)):\n plt.rcParams['figure.figsize'] = figsize\nset_figsize()\nplt.scatter(features[:,1],labels,c='r',s=1)\n<\/pre>\n\n\n<\/figure><\/div>\n\n\n
\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u6279\uff0c\u6bcf\u6b21\u8fd4\u56de\u4e00\u4e2a\u6279\u5927\u5c0f\u7684\u6570\u636e\uff0c\u5728\u5206\u5272\u4e4b\u524d\u8981\u8fdb\u884c\u6253\u4e71\uff0c\u5176\u4e2dtf.gather\u662f\u6839\u636eindices\u6765\u5bf9\u539f\u59cb\u6570\u636e\u8fdb\u884c\u5207\u7247\uff1a<\/p>\n\n\n
def data_iter(batch_size,features,labels):\n num_examples = len(features)\n indices = list(range(num_examples))\n random.shuffle(indices)\n for i in range(0,num_examples,batch_size):\n j = indices[i:min(i+batch_size,num_examples)]\n # gather \u6839\u636eindices\u5207\u7247\n yield tf.gather(features,axis=0,indices=j),tf.gather(labels,axis=0,indices=j)\n<\/pre>\n\n\n\u6211\u4eec\u8f93\u51fa\u8bd5\u9a8c\u4e00\u4e0b\uff1a<\/p>\n\n\n
batch_size = 64
for X,y in data_iter(batch_size,features,labels):
print(X,y)
break<\/pre>\n\n\n\u8f93\u51fa64\u4e2a\u6570\u636e\uff1a<\/p>\n\n\n
tf.Tensor(\n[[-1.568652 1.1892316 ]\n [ 0.02704381 -1.2114034 ]\n [ 0.54140687 0.6874146 ]\n [-0.3238443 -0.7069099 ]\n [ 0.670013 1.0555037 ]\n [ 1.4785416 -0.02862001]\n [-1.5540386 -0.90648144]\n [-2.657925 -0.34048915]\n [-0.13981636 0.45385957]\n [-0.0776669 -0.02562523]\n [ 0.15774341 1.3849018 ]\n [ 0.9904404 0.49320254]\n [-0.5353007 1.9172873 ]\n [-0.7673046 1.2029607 ]\n [ 1.9484698 -0.25718805]\n [-1.5775664 0.23291822]\n [ 1.2565771 -0.39332908]\n [ 0.5235608 -0.60813963]\n [ 0.2633382 -1.6159766 ]\n [ 1.8978524 1.3576044 ]\n [-0.40452728 0.02824024]\n [-0.6108405 0.1997418 ]\n [ 0.28169724 -1.2480029 ]\n [ 1.1958692 2.5761182 ]\n [-0.01832403 0.2854444 ]\n [ 0.02692759 -0.79011744]\n [ 1.9638565 0.14134032]\n [-0.53969157 -1.1126633 ]\n [ 1.3907293 -1.3411927 ]\n [-1.2987695 1.61959 ]\n [-2.0938413 -0.20309015]\n [-0.9750384 0.8229153 ]\n [-1.7626753 0.4005635 ]\n [ 0.22338685 -0.72602 ]\n [-0.21097755 -0.3210915 ]\n [-1.3122041 2.3579533 ]\n [-0.3418174 -1.2656993 ]\n [-1.5983564 -0.02388086]\n [-1.0887327 -0.6083594 ]\n [ 0.16343872 -1.0361065 ]\n [ 0.4365194 1.3847867 ]\n [ 0.2617833 0.9032589 ]\n [ 0.5480683 1.3676795 ]\n [-0.73368275 2.49532 ]\n [-1.5379615 0.891124 ]\n [ 0.20122519 -0.00788717]\n [ 0.5612905 -0.9636846 ]\n [-0.08743163 0.31147835]\n [ 1.6043682 0.913555 ]\n [-1.4654535 -0.648391 ]\n [-0.45734385 -2.094514 ]\n [-0.05061309 0.68695724]\n [-0.05295819 0.1176275 ]\n [ 0.5119573 0.04384377]\n [-0.88304853 -0.26165497]\n [-0.39841247 0.13567868]\n [ 1.6472635 1.2322218 ]\n [-0.1735344 0.5267474 ]\n [-0.8581472 0.30082825]\n [ 0.45566842 -0.07357331]\n [-0.747671 -0.27977103]\n [-0.40926522 -1.5160431 ]\n [ 1.0966959 1.6514026 ]\n [-0.4333734 0.10984586]], shape=(64, 2), dtype=float32) tf.Tensor(\n[-2.9873667 8.377133 2.946247 5.9545803 1.9532868 7.249343\n 4.1806116 0.03477701 2.3633275 4.1365757 -0.18981844 4.5226593\n -3.3917785 -1.4252613 8.998332 0.23443112 8.046925 7.302883\n 10.212007 3.4053535 3.2838557 2.3075964 8.993652 -2.153344\n 3.205419 6.938238 7.631661 6.9013247 11.54775 -3.912147\n 0.70706934 -0.5570358 -0.70196784 7.121228 4.8730345 -6.4330792\n 7.812588 1.0836437 4.1145034 8.063896 0.35680652 1.6736523\n 0.64509416 -5.7431006 -1.9141186 4.6364574 8.593288 2.9628947\n 4.298334 3.4748955 10.410857 1.7619439 3.6892602 5.0799007\n 3.3337908 2.9197962 3.3119173 2.0573657 1.463488 5.371661\n 3.6591358 8.535518 0.78479844 2.942409 ], shape=(64,), dtype=float32)\n<\/pre>\n\n\n\u7136\u540e\u6211\u4eec\u521d\u59cb\u5316\u6211\u4eec\u7684\u53c2\u6570\uff0c\u5c31\u662fw\u548cb<\/p>\n\n\n
# \u521d\u59cb\u5316\u53c2\u6570
w = tf.Variable(tf.random.normal((num_inputs,1),stddev=0.01))
b = tf.Variable(tf.zeros((1,)))<\/pre>\n\n\n\u5b9a\u4e49\u7ebf\u6027\u4f20\u64ad\u65b9\u6cd5\uff1a<\/p>\n\n\n
def linreg(X,w,b):
return tf.matmul(X,w)+b<\/pre>\n\n\n\u4ee3\u4ef7\u8ba1\u7b97\u65b9\u6cd5\uff1a<\/p>\n\n\n
def squared_loss(y_hat,y):
return (y_hat-tf.reshape(y,y_hat.shape))**2\/2<\/pre>\n\n\n\u66f4\u65b0\u53c2\u6570\u65b9\u6cd5\uff1a<\/p>\n\n\n
def sgd(params,lr,batch_size,grads):
for i,param in enumerate(params):
param.assign_sub(lr*grads[i]\/batch_size)<\/pre>\n\n\n\u7136\u540e\u5bf9\u6240\u6709\u6570\u636e\u8fdb\u884c3\u6b21\u8ba1\u7b97\uff0c\u5373epochs=3\uff1a<\/p>\n\n\n
lr = 0.03
num_epochs = 3
net = linreg
loss = squared_loss
for epoch in range(num_epochs):
for X,y in data_iter(batch_size,features,labels):
with tf.GradientTape() as t:
t.watch([w,b])
# \u5b9a\u4e49\u635f\u5931\u51fd\u6570
l = loss(net(X,w,b),y)
grads = t.gradient(l,[w,b])
sgd([w,b],lr,batch_size,grads)
train_l = loss(net(features,w,b),labels)
print('epoch %d, loss %f'.format(epoch+1,tf.reduce_mean(train_l)))<\/pre>\n\n\n\u6700\u540e\u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n\n\n
print(true_w,w)
print(true_b,b)
<\/pre>\n\n\n\u8f93\u51fa\uff0c\u53ef\u4ee5\u770b\u52302\u548c-3.4\u5206\u522b\u62df\u5408\u5230\u4e861.6\u548c-2.6\uff0c4.2\u62df\u5408\u5230\u4e863.11\uff1a<\/p>\n\n\n
[2, -3.4] <tf.Variable 'Variable:0' shape=(2, 1) dtype=float32, numpy=\narray([[ 1.6446801],\n [-2.5876393]], dtype=float32)>\n4.2 <tf.Variable 'Variable:0' shape=(1,) dtype=float32, numpy=array([3.159864], dtype=float32)><\/pre>\n\n\n\u4e0b\u9762\u662f\u4e00\u4e2a\u66f4\u52a0\u7cbe\u7b80\u7684\u5b9e\u73b0\u8fc7\u7a0b\uff0c\u540c\u65f6\u6548\u679c\u4e5f\u66f4\u597d\uff0c\u4f7f\u7528\u4e86tensorflow\u7684\u9ad8\u7ea7\u5c01\u88c5\u2014\u2014keras\u3002<\/p>\n\n\n
\u548c\u4e4b\u524d\u4e00\u6837\uff0c\u5148\u5bfc\u5305\uff1a<\/p>\n\n\n
import tensorflow as tf\nprint(tf.__version__)\n<\/pre>\n\n\n\u8f93\u51fa\uff1a<\/p>\n\n\n
2.1.0\n<\/pre>\n\n\n\u7136\u540e\u751f\u6210\u6570\u636e\uff1a<\/p>\n\n\n
num_input = 2\nnum_example = 1000\ntrue_w = [2,-3.4]\ntrue_b = 4.2\nfeatures = tf.random.normal(shape=(num_example,num_input),stddev=1)\nlabels = true_w[0] * features[:,0]+true_w[1]*features[:,1]+true_b\nlabels += tf.random.normal(labels.shape,stddev=0.01)\n<\/pre>\n\n\n\u8fd9\u91cc\u6211\u4eec\u548c\u4e4b\u524d\u4e0d\u4e00\u6837\uff0c\u6211\u4eec\u4f7f\u7528\u5b83tensorflow\u7684dataset\u5bf9\u6570\u636e\u8fdb\u884c\u6253\u5305\uff1a<\/p>\n\n\n
from tensorflow import data as tddata\nbatch_size = 10\n# \u5c06\u8bad\u7ec3\u6570\u636e\u7684\u7279\u5f81\u548c\u6807\u7b7e\u7ec4\u5408\ndataset = tddata.Dataset.from_tensor_slices((features,labels))\n# \u968f\u673a\u8bfb\u53d6\u5c0f\u6279\u91cf\ndataset = dataset.shuffle(buffer_size=num_example)\ndataset = dataset.batch(batch_size)\ndata_iter = iter(dataset)<\/pre>\n\n\n\u8f93\u51fa\u4e00\u4e0b\u6570\u636e\uff1a<\/p>\n\n\n
for (batch,(X, y)) in enumerate(dataset):\n print(batch,X,y)\n break\n<\/pre>\n\n\n\u8f93\u51fa\u5982\u4e0b\uff1a<\/p>\n\n\n
0 tf.Tensor(\n[[-1.3183944 0.88571453]\n [ 0.5582473 0.6283028 ]\n [ 0.3318851 0.30217335]\n [ 0.5585688 1.625666 ]\n [ 1.3739443 -0.2193088 ]\n [-1.1692793 -1.3866609 ]\n [ 0.76365966 1.0310326 ]\n [-1.7447253 1.4246174 ]\n [-0.4479057 -0.52858967]\n [ 0.6691452 -0.44063178]], shape=(10, 2), dtype=float32) tf.Tensor(\n[-1.4440066 3.1915112 3.8415916 -0.20795591 7.6803966 6.574926\n 2.2208211 -4.1473265 5.0976176 7.0480514 ], shape=(10,), dty<\/pre>\n\n\n\u7136\u540e\u662f\u7f51\u7edc\u7ed3\u6784\u8bbe\u7f6e\u4e0e\u7f51\u7edc\u53c2\u6570\u521d\u59cb\u5316\uff1a<\/p>\n\n\n
from tensorflow import keras\nfrom tensorflow import initializers as init\nmodel = keras.Sequential()\nmodel.add(keras.layers.Dense(1,kernel_initializer=init.RandomNormal(stddev=0.01)))\n<\/pre>\n\n\n\u635f\u5931\u51fd\u6570\u5b9a\u4e49\uff1a<\/p>\n\n\n
from tensorflow import losses\nloss = losses.MeanSquaredError()\n<\/pre>\n\n\n\u53c2\u6570\u66f4\u65b0\u65b9\u6cd5\u5b9a\u4e49\uff1a<\/p>\n\n\n
from tensorflow import optimizers\ntrainer = optimizers.SGD(learning_rate=0.03)\n<\/pre>\n\n\n\u8bad\u7ec3\u8fc7\u7a0b\uff1a<\/p>\n\n\n
9<\/pre>\n\n\nnum_epochs = 3\nfor epoch in range(1,num_epochs+1):\n for (batch,(X,y)) in enumerate(dataset):\n with tf.GradientTape() as t:\n l = loss(model(X,training=True),y)\n grads = t.gradient(l, model.trainable_variables)\n trainer.apply_gradients(zip(grads,model.trainable_variables))\n l = loss(model(features),labels)\n print('epoch %d, loss: %f' % (epoch, l))<\/pre>\n\n\n\u6253\u5370\u4e00\u4e0b\u7ed3\u679c\uff0c\u53d1\u73b0\u76f8\u6bd4\u4e0a\u4e00\u4e2a\u7248\u672c\uff0c\u5177\u6709\u66f4\u597d\u7684\u62df\u5408\uff08\u56e0\u4e3a\u4f7f\u7528\u7684\u662f\u5185\u7f6e\u65b9\u6cd5\uff09\uff1a<\/p>\n\n\n
true_w,model.get_weights()[0]\n<\/pre>\n\n\n([2, -3.4],\n array([[ 2.0009027],\n [-3.4000764]], dtype=float32))<\/pre>\n\n\ntrue_b,model.get_weights()[1]\n<\/pre>\n\n\n(4.2, array([4.2001143], dtype=float32))<\/pre>\n","protected":false},"excerpt":{"rendered":"\u672c\u8282\u8bb2\u8ff0\u7528tensorflow2\u5b9e\u73b0\u7ebf\u6027\u56de\u5f52\u3002 \u7b97\u6cd5\u8bb2\u89e3\u53ca\u57fa\u7840\u5b9e\u73b0\u89c1\u3010\u5434\u6069\u8fbe\u673a\u5668\u5b66\u4e60\u2014\u2014\u7ebf\u6027\u56de\u5f52\u3011 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_mi_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[28],"tags":[],"views":5001,"_links":{"self":[{"href":"http:\/\/www.sniper97.cn\/index.php\/wp-json\/wp\/v2\/posts\/3522"}],"collection":[{"href":"http:\/\/www.sniper97.cn\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.sniper97.cn\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.sniper97.cn\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.sniper97.cn\/index.php\/wp-json\/wp\/v2\/comments?post=3522"}],"version-history":[{"count":0,"href":"http:\/\/www.sniper97.cn\/index.php\/wp-json\/wp\/v2\/posts\/3522\/revisions"}],"wp:attachment":[{"href":"http:\/\/www.sniper97.cn\/index.php\/wp-json\/wp\/v2\/media?parent=3522"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.sniper97.cn\/index.php\/wp-json\/wp\/v2\/categories?post=3522"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.sniper97.cn\/index.php\/wp-json\/wp\/v2\/tags?post=3522"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}