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 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.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.…

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]) prob = tf.nn.softmax(x @ w + b, axis=1) loss = tf.reduce_mean(tf.losses.MSE…

import tensorflow as tf a = tf.linspace(-10., 10., 10) a with tf.GradientTape() as tape: tape.watch(a) y = tf.sigmoid(a) grads = tape.gradient(y, [a]) grads a = tf.linspace(-5.,5.,10) a tf.tanh(a) a = tf.linspace(-1.,1.,10) a tf.nn.relu(a) tf.nn.leak…

import tensorflow as tf w = tf.constant(1.) x = tf.constant(2.) y = x * w with tf.GradientTape() as tape: tape.watch([w]) y2 = x * w grad1 = tape.gradient(y, [w]) grad1 with tf.GradientTape() as tape: tape.watch([w]) y2 = x * w grad2 = tape.gradient(…

import tensorflow as tf y = tf.constant([1, 2, 3, 0, 2]) y = tf.one_hot(y, depth=4) # max_label=3种 y = tf.cast(y, dtype=tf.float32) out = tf.random.normal([5, 4]) out loss1 = tf.reduce_mean(tf.square(y - out)) loss1 loss2 = tf.square(tf.norm(y - out)…