AI.zip_richheo_卷积神经网络_数字识别_神经网络

#coding:utf-8 #0导入模块 ，生成模拟数据集 import tensorflow as tf import numpy as np import matplotlib.pyplot as plt BATCH_SIZE = 30 seed = 2 #基于seed产生随机数 rdm = np.random.RandomState(seed) #随机数返回300行2列的矩阵，表示300组坐标点（x0,x1）作为输入数据集 X = rdm.randn(300,2) #从X这个300行2列的矩阵中取出一行,判断如果两个坐标的平方和小于2，给Y赋值1，其余赋值0 #作为输入数据集的标签（正确答案） Y_ = [int(x0*x0 + x1*x1 <2) for (x0,x1) in X] #遍历Y中的每个元素，1赋值'red'其余赋值'blue'，这样可视化显示时人可以直观区分 Y_c = [['red' if y else 'blue'] for y in Y_] #对数据集X和标签Y进行shape整理，第一个元素为-1表示，随第二个参数计算得到，第二个元素表示多少列，把X整理为n行2列，把Y整理为n行1列 X = np.vstack(X).reshape(-1,2) Y_ = np.vstack(Y_).reshape(-1,1) print X print Y_ print Y_c #用plt.scatter画出数据集X各行中第0列元素和第1列元素的点即各行的（x0，x1），用各行Y_c对应的值表示颜色（c是color的缩写） plt.scatter(X[:,0], X[:,1], c=np.squeeze(Y_c)) plt.show() #定义神经网络的输入、参数和输出，定义前向传播过程 def get_weight(shape, regularizer): w = tf.Variable(tf.random_normal(shape), dtype=tf.float32) tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w)) return w def get_bias(shape): b = tf.Variable(tf.constant(0.01, shape=shape)) return b x = tf.placeholder(tf.float32, shape=(None, 2)) y_ = tf.placeholder(tf.float32, shape=(None, 1)) w1 = get_weight([2,11], 0.01) b1 = get_bias([11]) y1 = tf.nn.relu(tf.matmul(x, w1)+b1) w2 = get_weight([11,1], 0.01) b2 = get_bias([1]) y = tf.matmul(y1, w2)+b2 #定义损失函数 loss_mse = tf.reduce_mean(tf.square(y-y_)) loss_total = loss_mse + tf.add_n(tf.get_collection('losses')) #定义反向传播方法：不含正则化 train_step = tf.train.AdamOptimizer(0.0001).minimize(loss_mse) with tf.Session() as sess: init_op = tf.global_variables_initializer() sess.run(init_op) STEPS = 40000 for i in range(STEPS): start = (i*BATCH_SIZE) % 300 end = start + BATCH_SIZE sess.run(train_step, feed_dict={x:X[start:end], y_:Y_[start:end]}) if i % 2000 == 0: loss_mse_v = sess.run(loss_mse, feed_dict={x:X, y_:Y_}) print("After %d steps, loss is: %f" %(i, loss_mse_v)) #xx在-3到3之间以步长为0.01，yy在-3到3之间以步长0.01,生成二维网格坐标点 xx, yy = np.mgrid[-3:3:.01, -3:3:.01] #将xx , yy拉直，并合并成一个2列的矩阵，得到一个网格坐标点的集合 grid = np.c_[xx.ravel(), yy.ravel()] #将网格坐标点喂入神经网络 ，probs为输出 probs = sess.run(y, feed_dict={x:grid}) #probs的shape调整成xx的样子 probs = probs.reshape(xx.shape) print "w1:\n",sess.run(w1) print "b1:\n",sess.run(b1) print "w2:\n",sess.run(w2) print "b2:\n",sess.run(b2) plt.scatter(X[:,0], X[:,1], c=np.squeeze(Y_c)) plt.contour(xx, yy, probs, levels=[.5]) plt.show() #定义反向传播方法：包含正则化 train_step = tf.train.AdamOptimizer(0.0001).minimize(loss_total) with tf.Session() as sess: init_op = tf.global_variables_initializer() sess.run(init_op) STEPS = 40000 for i in range(STEPS): start = (i*BATCH_SIZE) % 300 end = start + BATCH_SIZE sess.run(train_step, feed_dict={x: X[start:end], y_:Y_[start:end]}) if i % 2000 == 0: loss_v = sess.run(loss_total, feed_dict={x:X,y_:Y_}) print("After %d steps, loss is: %f" %(i, loss_v)) xx, yy = np.mgrid[-3:3:.01, -3:3:.01] grid = np.c_[xx.ravel(), yy.ravel()] probs = sess.run(y, feed_dict={x:grid}) probs = probs.reshape(xx.shape) print "w1:\n",sess.run(w1) print "b1:\n",sess.run(b1) print "w2:\n",sess.run(w2) print "b2:\n",sess.run(b2) plt.scatter(X[:,0], X[:,1], c=np.squeeze(Y_c)) plt.contour(xx, yy, probs, levels=[.5]) plt.show()