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Terson类型:
.int(整型) float(浮点型) double(双浮点型)
.bool(布尔型)
.string(浮点型)
创建Tensor的几种方法：
1、将numpy, list类型进行转化成tensor用convert_to_tensor
2、zeros, ones，, zeros_like, ones_like
3、fill ——> 填充
4、random -->normal创建一个正态分布的数据,truncated_normal截断一部分数据, uniform创建一个均匀分布的数据, shuffle随机打散
5、constant--> 创建一个常量
Terson常量的方法:
numpy(): 查看常量的数据， dtype: 查看常量的的类型， shape查看常量的形状， ndim, rank: 查看常量的维度
device: 当前tensor所在的设备的名字
判断是否是Tensor类型的方法: isinstance, is_tensor
把其他类型转化为tensor类型: convert_to_tensor
数据类型的转化: cast
将Tensor常量转化为能求导的Tensor : Variable --> trainable查看是否可求导， name
import numpy as np import tensorflow as tf with tf.device("cpu"): a = tf.constant([1]) #在cpu环境下创建一个Tensor a.device with tf.device("gpu"): b = tf.constant([1]) b.device aa = a.gpu bb = b.cpu# numpy tf.convert_to_tensor(np.ones(shape = [3, 3])) tf.convert_to_tensor(np.eye(3)) tf.convert_to_tensor(np.linspace(1, 10, 10)) tf.convert_to_tensor(np.random.randint(1, 10, size = (2, 4))) # list tf.convert_to_tensor([1, 2, 3, 4, 5, 6, 7]) # tf.ones(shape, dtype) tf.ones(shape = (3, 4), dtype = tf.float32) a = tf.constant([1, 2, 3, 4, 5, 6, 6]) tf.ones_like(a) # 计算a的shape然后按照这个shape创建一个ones # tf.zeros(shape, dtype) tf.zeros(shape = (3, 4), dtype = tf.float32) a = tf.constant([1, 2, 3, 4, 5, 6, 6]) tf.ones_like(a) # 计算a的shape然后按照这个shape创建一个zeros #tf.fill(dims, value) tf.fill(dims = (3, 4), value = 3) # tf.random.normal(shape, mean, stddev, dtype) tf.random.normal(shape = (3, 4), mean = 0, stddev = 0.4, dtype = tf.float32) # tf.random.uniform(shape, minval, maxval, dtype) tf.random.uniform(shape = (3, 4), minval = 0, maxval = 10, dtype = tf.float32) # tf.random.shuffle(value) a = np.arange(10) tf.random.shuffle(a) # constant tf.constant(1.) # <tf.Tensor: shape=(), dtype=float32, numpy=1.0> tf.constant([True, False]) # <tf.Tensor: shape=(2,), dtype=bool, numpy=array([ True, False])> tf.constant(2) # <tf.Tensor: shape=(2,), dtype=bool, numpy=array([ True, False])> tf.constant("hello world") # <tf.Tensor: shape=(2,), dtype=bool, numpy=array([ True, False])>a = tf.random.normal(shape = (4, 5), mean = 0, stddev = 0.3, dtype = tf.float32) a.numpy(), a.dtype, a.shape, a.ndim, tf.rank(a) isinstance(a, tf.Tensor), tf.is_tensor(a) tf.cast(a, dtype = tf.float16) b = tf.Variable(a, name = "input_data") b.trainable, b.name a.dtype == tf.float32 # 判断两个类型相同# loss的例子 out = tf.random.uniform([4, 10]) out = tf.nn.softmax(out) # softmax = tf.exp(logits) / tf.reduce_sum(tf.exp(logits), axis, keepdims=True) y = tf.range(4) y = tf.one_hot(y, depth = 10) loss = tf.keras.losses.MSE(y, out) # loss = mean(square(y_true - y_pred), axis=-1) loss = tf.reduce_mean(loss) loss""" .Bias .[out_dim] """ net = tf.keras.layers.Dense(10) # 全连接神经网络 net.build(input_shape = (4, 10)) # 建立网络输入数据 net.kernel # 网络中参数w net.bias # 网络中偏置b""" matrix(矩阵) .input x: [b, vec_dim] b： 多少张照片, vec_dim: 照片的长宽 .weight:[input_dim, out_dim] # w权值的 """ x = tf.random.normal([4, 784]) net = tf.layers.Dense(10) net.build(input_shape = [4, 784]) net(x).shape net.kernel.shape, net.bias.shape""" Dim = 3 Tensor x: [b, seq_dim, word_dim] # b:多少句子, seq_dim: 单词数量, word_dim: 编码长度 """ (x_train, y_train), (x_test, y_test) = tf.keras.datasets.imdb.load_data(num_words = 10000) # num_words 最多考虑的单词的数量 设置为10000表示只关注最常用的前10000个词汇，其他较少使用的忽略 x_train = tf.keras.preprocessing.sequence.pad_sequences(x_train, maxlen = 80) #maxlen每一个句子最长有多少单词，然后其他单词截断 emb = tf.keras.layers.Embedding(input_dim=10000, output_dim=100, input_length=80) """ input_dim=10000 表示输入数据中词汇表的大小，即总共有 10000 个不同的元素（例如 10000 个不同的单词）。 output_dim=100 表示每个输入元素被映射到的嵌入向量的维度大小，即生成的嵌入向量长度为 100。 input_length=80 表示输入序列的长度，即每个输入样本中包含的元素个数为 80。 """ out = tf.keras.layers.SimpleRNNCell(units, dropout) # units 表示输出空间的维度，即隐藏层神经元的数量； """ Dim = 4 Tensor .image [b, h, w, 3] .feature maps(特征图): [b, h, w, c] """ x = tf.random.normal([4, 32, 32, 3]) net = tf.keras.layers.Conv2D(filters, kernel_size, padding, activation) #filters: 卷积核, kernel_size: 滤波矩阵， padding: 填充， activation 激活函数 net(x) #调用这个网络 """ Dim = 5 Tensor .single task [b, h, w, 3] .[task_b, b, h, w, 3] """# 索引 a = tf.random.truncated_normal(shape = [4, 32, 32, 3], mean = 0, stddev = 0.8) a[0][0].shape, a[0][0][0].shape, a[0][0][0][2] a[1, 2, 3, 2], a[1, 2].shape, a[1, 3, 4].shape a[1, ..., 2].shape, # 切片 start: end: step a[-1:].shape, a[1:3, 0:14, 1:28:2, 0:2].shape, a[0, 1, :, :].shape a[0:2, :, :, 2].shape a[:, ::2, ::2, :].shape a[::-1].shape""" tf.gather(params, indices, axis) 根据提供的indices(索引)从axis(维度)收集元素 tf.gather_nd(params, indices) 利用indices 收集元素 tf.boolean_mask(tensor, mask, axis=None) 利用mask(布尔值)从axis(维度)收集元素 """ tf.gather(a, axis = 0, indices = [2, 3]).shape tf.gather(a, axis = 0, indices = [2, 1, 3, 0]).shape tf.gather(a, axis = 1, indices = [2, 3, 4, 5, 7, 9, 12]).shape tf.gather_nd(a, indices = [0, 1]).shape tf.gather_nd(a, indices = [0, 1, 2]) tf.gather_nd(a, indices = [[0, 0], [1, 1]]).shape tf.gather_nd(a, indices = [[0, 0], [1, 1], [2, 2]]).shape tf.gather_nd(a, indices = [[0, 0, 0], [1, 1, 1], [2, 2, 2]]).shape tf.boolean_mask(a, mask = [True, True, False, False], axis = 0).shape tf.boolean_mask(a, mask = [True, False, False], axis = 3).shape""" 维度变换: .shape 形状 .ndim 维度 .reshape(tensor, shape) 改变形状 .expand_dims(input, axis)/squeeze(input, axis) 扩展/压缩维度 .transpose(a, perm) Broadcasting(广播机制) .broadcast_to .expand .expand_dims .without copying data .tf.tile """ # shape, ndim a1 = tf.random.truncated_normal(shape = (4, 28, 28, 3), mean = 0, stddev = 1) a1.shape, a1.ndim # reshape tf.reshape(a1, shape = (4, 28 * 28, 3)).shape #expand_dims/squeeze tf.expand_dims(a1, axis=0).shape, a2 = tf.expand_dims(a1, axis = 4) tf.squeeze(a2).shape #transpose tf.transpose(a1, perm = [0, 2, 3, 1]) #Broadcast b = tf.ones(shape = (3, 3), dtype = tf.float32) b = tf.expand_dims(b, axis = 2) b = tf.tile(b, [1, 1, 3]) b.shape tf.broadcast_to(tf.random.normal([4, 1, 1, 1]), [4, 32, 32, 3]).shape x = tf.random.uniform([4, 32, 32, 3]) (x + tf.random.uniform([3])).shape (x + tf.random.uniform([32, 32, 1])).shape (x + tf.random.uniform([4, 1, 1, 1])).shape""" 数学运算 . +, -, *, / --> element_wise . **, pow, square .sqrt .//, % . exp, math.log . @, matmul .layers-->Dense, Conv2D, MaxPooling2D, Embedding, SimpleRNNCell, LSTM .losses--> MSE, categorical_crassentropy dim_wise .reduce_mean/max/min/sum/variance """ a = tf.random.normal(shape = (4, 3)) b = tf.random.normal(shape = (4, 3)) b - a, b + a, b * a, b / a, b ** a, tf.pow(a, 3), a ** 3, tf.square(a), tf.sqrt(a) tf.exp(a), tf.math.log(a) # 以e为底 tf.math.log(8.) / tf.math.log(2.) #loge^8/loge^2 tf.math.log(100.) / tf.math.log(10.) # loge^100/loge^10 tf.transpose(a) @ b""" 数学运算 . +, -, *, / --> element_wise . **, pow, square .sqrt .//, % . exp, math.log . @, matmul .layers-->Dense, Conv2D, MaxPooling2D, Embedding, SimpleRNNCell, LSTM .losses--> MSE, categorical_crassentropy dim_wise .reduce_mean/max/min/sum/variance """ a = tf.random.normal(shape = (4, 3)) b = tf.random.normal(shape = (4, 3)) b - a, b + a, b * a, b / a, b ** a, tf.pow(a, 3), a ** 3, tf.square(a), tf.sqrt(a) tf.exp(a), tf.math.log(a) # 以e为底 tf.math.log(8.) / tf.math.log(2.) #loge^8/loge^2 tf.math.log(100.) / tf.math.log(10.) # loge^100/loge^10 tf.transpose(a) @ b""" .pad(tensor, paddings, constant_values) paddings对左右上下进行填充[[上行, 下行], [左列, 右列]] constant_values填充的数字 .expand_dims, tile .broadcast_to .where(condition, x, y) .scatter_nd(indices, updates, shape) .meshgrid 张量限幅： tf.clip_by_value(t, clip_value_min, clip_value_max) tf.nn.relu max(0, a) clip_by_norm(t, clip_norm) (clip_norm * t) / ||t|| maximum minimum Gradient clipping : clip_by_global_norm(t_list, clip_norm) t_list[i] * clip_norm / max(global_norm, clip_norm) tf.one_hot """ a = tf.reshape(tf.range(9), shape = (3, 3)) a = tf.cast(tf.convert_to_tensor(a), dtype = tf.float32) tf.pad(a, [[1, 0], [1, 1]], constant_values = 1) aa = tf.expand_dims(a, axis = 0) tf.tile(aa, [4, 1, 1]) tf.broadcast_to(a, [4, 3, 3]) tf.where(a > 4, 0, 1) tf.maximum(a, 4) # 小于四的全部用四代替 tf.minimum(a, 4) # 大于四的全部用四代替 tf.clip_by_value(a, 2, 6) # 小于2用2代替大于6用6代替 tf.nn.relu(a) # max(0, a) tf.clip_by_norm(t = a, clip_norm = 5).numpy() clipped_tensors, global_norm = tf.clip_by_global_norm(t_list = [tf.cast(tf.convert_to_tensor(tf.range(9)), dtype=tf.float32)], clip_norm=4) clipped_tensors, global_norm updates = tf.constant([9, 10, 11, 12]) indices = tf.constant([[4], [2], [1], [6]]) shape = tf.constant([8]) tf.scatter_nd(indices, updates, shape) indices = tf.constant([[0], [2]]) updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]]) shape = tf.constant([4, 4, 4]) tf.scatter_nd(indices, updates, shape) x = tf.range(5) y = tf.range(6) points_x, points_y = tf.meshgrid(x, y) points = tf.stack([points_x, points_y], axis = 2) points""" 数据加载 outline: .keras.datasets .boston_housing.load_data() 波斯顿房价 .mnist/fashion_mnist.load_data() 手写体/流行照片 .cifar10/100.load_data() 模糊图像 .imdb.load_data() 情感分类 .tf.data.Dataset.from_tensor_slices 将输入的张量按照指定的维度进行切片，并将每个切片作为数据集中的一个元素。 .shuffle .map .batch .repeat 1. shuffle(10000) : 对数据集进行随机打乱，缓冲区大小为 10000。这有助于在训练过程中引入随机性，减少数据的顺序相关性。 2. map(proprecession) : 对数据集中的每个元素应用自定义的预处理函数 proprecession ，以进行数据的预处理操作，例如数据增强、归一化等。 3. batch(128) : 将数据集分成大小为 128 的批次。这意味着模型在训练时每次处理 128 个样本。 4. repeat(2) : 重复整个数据集 2 次。这在训练时可以让数据集被多次遍历。 """ # 数据预处理的方法 import tensorflow as tf (x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data() def proprecession(x, y): x = tf.cast((x - tf.reduce_min(x)) / (tf.reduce_max(x) - tf.reduce_min(x)), dtype = tf.float32) y = tf.cast(y, dtype = tf.int32) return x, y train_db = tf.data.Dataset.from_tensor_slices((x_train, y_train)) train_db = train_db.shuffle(10000).map(proprecession).batch(128).repeat(2) test_db = tf.data.Dataset.from_tensor_slices((x_test, y_test)) test_db = test_db.shuffle(10000).map(proprecession).batch(128)反向传播算法
import tensorflow as tf from tensorflow import keras from tensorflow.keras import datasets (x_train, y_train), (x_test, y_test) = datasets.mnist.load_data() tf.reduce_max(x_train), tf.reduce_min(x_train), x_train.shape, x_test.shape, tf.reduce_max(x_test), tf.reduce_min(x_test) # 预处理 x_train = tf.convert_to_tensor(x_train, dtype = tf.float32) y_train = tf.convert_to_tensor(y_train, dtype = tf.int32) x_test = tf.convert_to_tensor(x_test, dtype = tf.float32) y_test = tf.convert_to_tensor(y_test, dtype = tf.int32) x_train = (x_train - tf.reduce_min(x_train)) / (tf.reduce_max(x_train) - tf.reduce_min(x_train)) # 最大最小值归一化 x_test = (x_test - tf.reduce_mean(x_test)) / tf.sqrt(tf.math.reduce_variance(x_test)) #零均值归一化 # y_test = tf.one_hot(y_test, depth = 10) train_db = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(128) test_db = tf.data.Dataset.from_tensor_slices((x_test, y_test)).shuffle(10000).batch(128) sample = next(iter(train_db)) print("batch:", sample[0].shape, sample[1].shape) lr = 0.003 w1 = tf.Variable(tf.random.truncated_normal([784, 256], mean = 0, stddev = 0.1)) b1 = tf.Variable(tf.zeros([256])) w2 = tf.Variable(tf.random.truncated_normal([256, 128], mean = 0, stddev = 0.1)) b2 = tf.Variable(tf.zeros([128])) w3 = tf.Variable(tf.random.truncated_normal([128, 10], mean = 0, stddev = 0.1)) b3 = tf.Variable(tf.zeros([10])) total_correct = 0 total_num = 0 for epoch in range(10): for step, (x, y) in enumerate(train_db): x = tf.reshape(x, [-1, 28*28]) with tf.GradientTape() as tape: h1 = x @ w1 + tf.broadcast_to(b1, [x.shape[0], 256]) # [128, 256] + [128, 256] h1 = tf.nn.relu(h1) h2 = h1 @ w2 + b2 h2 = tf.nn.relu(h2) out = h2 @ w3 + b3 y_onehot = tf.one_hot(y, depth = 10) # mean(sum((y - out) ** 2)) loss = tf.square(y_onehot - out) loss = tf.reduce_mean(loss) grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3]) w1.assign_sub(lr * grads[0]) # w1 - lr * grads[n] 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 = ", epoch, "step = ", step, "loss:", float(loss)) for step, (x, y) in enumerate(test_db): x = tf.reshape(x, [-1, 28 * 28]) h1 = tf.nn.relu(x @ w1 + b1) h2 = tf.nn.relu(h1 @ w2 + b2) out = h2 @ w3 + b3 prob = tf.nn.softmax(out, axis = 1) prob = tf.argmax(prob, axis = 1) prob = tf.cast(prob, dtype = tf.int32) correct = tf.cast(tf.equal(prob, y), dtype = tf.int32) correct = tf.reduce_sum(correct) total_correct += int(correct) total_num += x.shape[0] acc = total_correct / total_num print("test acc:", acc)