import tensorflow as tf a = tf.range(10) a # a中小于2的元素值为2 tf.maximum(a, 2) # a中大于8的元素值为8 tf.minimum(a, 8) # a中的元素值限制在[2,8]区间内 tf.clip_by_value(a, 2, 8) a = a - 5 a tf.nn.relu(a) tf.maximum(a, 0) 缩放时不改变梯度方向 a = tf.random.normal([2, 2], mean=10) a tf.no…

import tensorflow as tf a = tf.random.shuffle(tf.range(5)) a tf.sort(a, direction='DESCENDING') # 返回索引 tf.argsort(a, direction='DESCENDING') idx = tf.argsort(a, direction='DESCENDING') tf.gather(a, idx) idx = tf.argsort(a, direction='DESCENDING') tf.…

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], [10,28,28] # y: [60k], [10k] (x, y), (x_test, y_te…

手写数字识别流程 MNIST手写数字集7000*10张图片 60k张图片训练,10k张图片测试 每张图片是28*28,如果是彩色图片是28*28*3-255表示图片的灰度值,0表示纯白,255表示纯黑 打平28*28的矩阵,得到28*28=784的向量 对于b张图片得到[b,784];然后对于b张图片可以给定编码 把上述的普通编码给定成独热编码,但是独热编码都是概率值,并且概率值相加为1,类似于softmax回归 套用线性回归公式 X[b,784] W[784,10] b[10] 得到 [b,1…

import tensorflow as tf from tensorflow import keras from keras import Sequential,datasets, layers, optimizers, metrics def preprocess(x, y): """数据处理函数""" x = tf.cast(x, dtype=tf.float32) / 255. y = tf.cast(y, dtype=tf.int32)…

import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D def himmeblau(x): return (x[0]**2 + x[1] - 11)**2 + (x[0] + x[1]**2 - 7)**2 x = np.arange(-6, 6, 0.1) y = np.arange(-6, 6, 0.1) print(f'x_shape: {x.shape},y_sh…

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import tensorflow as tf x = tf.constant(1.) w1 = tf.constant(2.) b1 = tf.constant(1.) w2 = tf.constant(2.) b2 = tf.constant(1.) with tf.GradientTape(persistent=True) as tape: tape.watch([w1, b1, w2, b2]) y1 = x * w1 + b1 y2 = y1 * w2 + b2 dy2_dy1 = t…

import tensorflow as tf x = tf.random.normal([2, 4]) w = tf.random.normal([4, 3]) b = tf.zeros([3]) y = tf.constant([2, 0]) with tf.GradientTape() as tape: tape.watch([w, b]) # axis=1,表示结果[b,3]中的3这个维度为概率 prob = tf.nn.softmax(x @ w + b, axis=1) # 2 --…

import tensorflow as tf x = tf.random.normal([1, 3]) w = tf.ones([3, 1]) b = tf.ones([1]) y = tf.constant([1]) with tf.GradientTape() as tape: tape.watch([w, b]) prob = tf.sigmoid(x @ w + b) loss = tf.reduce_mean(tf.losses.MSE(y, prob)) grads = tape.…