Openpose连踩6坑记录
记得上次配置openpose是一次难忘的记忆，但由于一直才试错所以没有记录，这一次在一台全新的电脑上配置ope…
KITTI标定数据解读
KITTI的标定数据是这样的 一个相机有三个标定文件组成： calib_cam_to_cam [crayon-…
机器视觉-测量和拟合
今天测量一个摄像头的宽度： 测量的原理是： 计算一条直线上的灰度值，做成一条曲线， 对这个曲线的每一点都计算该…
TensorFlow Faster-Rcnn运行配置记录
第一步 下载模型 链接：https://pan.baidu.com/s/1r9L76SOoZOJxoKya5b…
深度学习-15-循环神经网络理论+实战情感分类
情感分类实战 比较高层一点的实现 [crayon-5f2fe10137198706…
深度学习-14-卷积理论+实战CIFAR100与VGG13
VGG13实战CIFAR100： 网络长成这个样子 [crayon-5f2fe10…
深度学习-提升训练质量的技巧合集
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深度学习-12-Keras训练CIFAR 10
一般来说在TF中使用keras的五个功能： datasets layers losses metrics op…
深度学习-11-fashion_mnist层方法实现
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import tensorflow as tf from tensorflow import keras from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' def preprocess(x, y): x = tf.cast(x, dtype=tf.float32) / 255. # cast(x, dtype, name=None) 张量数据类型转换 将数据转换成对应的tensor类型 y = tf.cast(y, dtype=tf.int32) return x,y (x, y), (x_test, y_test) = datasets.fashion_mnist.load_data() # 从线上加载数据集 print(x.shape, y.shape) # (60000, 28, 28) (60000,) x 是图片的数据 有60000张28*28的数据 y 是分类数据 batchsz = 128 # 单个batch的size ## 创建训练数据集 db = tf.data.Dataset.from_tensor_slices((x, y)) # 对数据进行切片 datasets中的函数 tf.data.Dataset.from_tensor_slices((features, labels)) db = db.map(preprocess).shuffle(10000).batch(batchsz) # 把数据打乱然后打包成batch ## 创建验证数据集 db_test = tf.data.Dataset.from_tensor_slices((x_test,y_test)) db_test = db_test.map(preprocess).batch(batchsz) ## 创建一个迭代器 db_iter = iter(db) sample = next(db_iter) print('batch:', sample[0].shape, sample[1].shape) # batch: (128, 28, 28) (128,) ## 创建一个神经网络 每层进行降维 model = Sequential([ layers.Dense(256, activation=tf.nn.relu), # [b, 784] => [b, 256] layers.Dense(128, activation=tf.nn.relu), # [b, 256] => [b, 128] layers.Dense(64, activation=tf.nn.relu), # [b, 128] => [b, 64] layers.Dense(32, activation=tf.nn.relu), # [b, 64] => [b, 32] layers.Dense(10) # [b, 32] => [b, 10], 330 = 32*10 + 10 最后一个不需要激活函数 ]) model.build(input_shape=[None, 28*28]) # 输入的维度 model.summary() # 打印网络结构 # w = w - lr*grad optimizer = optimizers.Adam(lr=1e-3) def main(): for epoch in range(30): for step, (x,y) in enumerate(db): # x: [b, 28, 28] => [b, 784] # y: [b] x = tf.reshape(x, [-1, 28*28]) with tf.GradientTape() as tape: # 求梯度的信息 # [b, 784] => [b, 10] logits = model(x) # 完成一次前向传播 [b, 784] => [b, 10] y_onehot = tf.one_hot(y, depth=10) # [b] # 求出loss loss_mse = tf.reduce_mean(tf.losses.MSE(y_onehot, logits)) loss_ce = tf.losses.categorical_crossentropy(y_onehot, logits, from_logits=True) loss_ce = tf.reduce_mean(loss_ce) # loss 要求均值因此是针对每一个来做的 # 完成梯度的计算 原地更新 grads = tape.gradient(loss_ce, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables)) if step % 100 == 0: print(epoch, step, 'loss:', float(loss_ce), float(loss_mse)) # test total_correct = 0 total_num = 0 for x,y in db_test: # x: [b, 28, 28] => [b, 784] # y: [b] x = tf.reshape(x, [-1, 28*28]) # [b, 10] logits = model(x) # logits => prob, [b, 10] prob = tf.nn.softmax(logits, axis=1) # [b, 10] => [b], int64 pred = tf.argmax(prob, axis=1) pred = tf.cast(pred, dtype=tf.int32) # pred:[b] # y: [b] # correct: [b], True: equal, False: not equal correct = tf.equal(pred, y) correct = tf.reduce_sum(tf.cast(correct, dtype=tf.int32)) total_correct += int(correct) total_num += x.shape[0] acc = total_correct / total_num print(epoch, 'test acc:', acc) if __name__ == '__main__': main() |
深度学习-10-前向传播(张量)
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import tensorflow as tf from tensorflow import keras from tensorflow.keras import datasets import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # x: [60k, 28, 28], # y: [60k] (x, y), _ = datasets.mnist.load_data() # 读取数据 # x: [0~255] => [0~1.] x = tf.convert_to_tensor(x, dtype=tf.float32) / 255. y = tf.convert_to_tensor(y, dtype=tf.int32) print(x.shape, y.shape, x.dtype, y.dtype) print(tf.reduce_min(x), tf.reduce_max(x)) print(tf.reduce_min(y), tf.reduce_max(y)) train_db = tf.data.Dataset.from_tensor_slices((x, y)).batch(128) # 切分成batch 相当于每128张图片是一个batch train_iter = iter(train_db) sample = next(train_iter) print('batch:', sample[0].shape, sample[1].shape) # [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])) lr = 1e-3 for epoch in range(10): # iterate db for 10 对整个数据集迭代10次 for step, (x, y) in enumerate(train_db): # for every batch # 对每个batch迭代 # x:[128, 28, 28] # y: [128] # [b, 28, 28] => [b, 28*28] x = tf.reshape(x, [-1, 28*28]) with tf.GradientTape() as tape: # tf.Variable # 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 = 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]) # print(grads) # w1 = w1 - lr * w1_grad 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(epoch, step, 'loss:', float(loss)) |