tensorflow基本操作小白快速构建线性回归和分类模型
目录
- tensorflow是非常强的工具,生态庞大
- tensorflow提供了Keras的分支
- Define tensor constants.
- Linear Regression
- 分类模型
- 本例使用MNIST手写数字
- Model prediction: 7
- Model prediction: 2
- Model prediction: 1
- Model prediction: 0
- Model prediction: 4
TF 目前发布2.5 版本,之前阅读1.X官方文档,最近查看2.X的文档。
tensorflow是非常强的工具,生态庞大
tensorflow提供了Keras的分支
这里不再提供Keras相关顺序模型教程。
关于环境:ubuntu的 GPU,需要cuda和nvcc
不会安装:查看
完整的Ubuntu18.04深度学习GPU环境配置,英伟达显卡驱动安装、cuda9.0安装、cudnn的安装、anaconda安装
不安装,直接翻墙用colab
测试GPU
>>> from tensorflow.python.client import device_lib >>> device_lib.list_local_devices()
这是意思是挂了一个显卡
具体查看官方文档:https://www.tensorflow.org/install
服务器跑Jupyter
Define tensor constants.
import tensorflow as tf # Create a Tensor. hello = tf.constant("hello world") hello # Define tensor constants. a = tf.constant(1) b = tf.constant(6) c = tf.constant(9) # tensor变量的操作 # (+, *, ...) add = tf.add(a, b) sub = tf.subtract(a, b) mul = tf.multiply(a, b) div = tf.divide(a, b) # 通过numpy返回数值 和torch一样 print("add =", add.numpy()) print("sub =", sub.numpy()) print("mul =", mul.numpy()) print("div =", div.numpy()) add = 7 sub = -5 mul = 6 div = 0.16666666666666666 mean = tf.reduce_mean([a, b, c]) sum_ = tf.reduce_sum([a, b, c]) # Access tensors value. print("mean =", mean.numpy()) print("sum =", sum_ .numpy()) mean = 5 sum = 16 # Matrix multiplications. matrix1 = tf.constant([[1., 2.], [3., 4.]]) matrix2 = tf.constant([[5., 6.], [7., 8.]]) product = tf.matmul(matrix1, matrix2) product <tf.Tensor: shape=(2, 2), dtype=float32, numpy= array([[19., 22.], [43., 50.]], dtype=float32)> # Tensor to Numpy. np_product = product.numpy() print(type(np_product), np_product) (numpy.ndarray, array([[19., 22.], [43., 50.]], dtype=float32))
Linear Regression
下面使用tensorflow快速构建线性回归模型,这里不使用kears的顺序模型,而是采用torch的模型定义的写法。
import numpy as np import tensorflow as tf # Parameters: learning_rate = 0.01 training_steps = 1000 display_step = 50 # Training Data. X = np.array([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1]) Y = np.array([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3]) random = np.random # 权重和偏差,随机初始化。 W = tf.Variable(random.randn(), name="weight") b = tf.Variable(random.randn(), name="bias") # Linear regression (Wx + b). def linear_regression(x): return W * x + b # Mean square error. def mean_square(y_pred, y_true): return tf.reduce_mean(tf.square(y_pred - y_true)) # 随机梯度下降优化器。 optimizer = tf.optimizers.SGD(learning_rate) # 优化过程。 def run_optimization(): # 将计算包在GradientTape中,以便自动区分。 with tf.GradientTape() as g: pred = linear_regression(X) loss = mean_square(pred, Y) # 计算梯度。 gradients = g.gradient(loss, [W, b]) # 按照梯度更新W和b。 optimizer.apply_gradients(zip(gradients, [W, b])) #按给定的步数进行训练。 for step in range(1, training_steps + 1): # 运行优化以更新W和b值。 run_optimization() if step % display_step == 0: pred = linear_regression(X) loss = mean_square(pred, Y) print("Step: %i, loss: %f, W: %f, b: %f" % (step, loss, W.numpy(), b.numpy()))
import matplotlib.pyplot as plt plt.plot(X, Y, 'ro', label='Original data') plt.plot(X, np.array(W * X + b), label='Fitted line') plt.legend() plt.show()
分类模型
本例使用MNIST手写数字
数据集包含60000个训练示例和10000个测试示例。
这些数字已经过大小标准化,并在一个固定大小的图像(28x28像素)中居中,值从0到255。
在本例中,每个图像将转换为float32,标准化为[0,1],并展平为784个特征(28×28)的一维数组。
import numpy as np import tensorflow as tf # MNIST data num_classes = 10 # 0->9 digits num_features = 784 # 28 * 28 # Parameters lr = 0.01 batch_size = 256 display_step = 100 training_steps = 1000 # Prepare MNIST data from tensorflow.keras.datasets import mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() # Convert to Float32 x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32) # Flatten images into 1-D vector of 784 dimensions (28 * 28) x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features]) # [0, 255] to [0, 1] x_train, x_test = x_train / 255, x_test / 255 # 打乱顺序: tf.data API to shuffle and batch data train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)) train_dataset = train_dataset.repeat().shuffle(5000).batch(batch_size=batch_size).prefetch(1) # Weight of shape [784, 10] ~= [number_features, number_classes] W = tf.Variable(tf.ones([num_features, num_classes]), name='weight') # Bias of shape [10] ~= [number_classes] b = tf.Variable(tf.zeros([num_classes]), name='bias') # Logistic regression: W*x + b def logistic_regression(x): # 应用softmax函数将logit标准化为概率分布 out = tf.nn.softmax(tf.matmul(x, W) + b) return out # 交叉熵损失函数 def cross_entropy(y_pred, y_true): # 将标签编码为一个one_hot向量 y_true = tf.one_hot(y_true, depth=num_classes) # 剪裁预测值避免错误 y_pred = tf.clip_by_value(y_pred, 1e-9, 1) # 计算交叉熵 cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred), 1)) return cross_entropy # Accuracy def accuracy(y_pred, y_true): correct = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64)) return tf.reduce_mean(tf.cast(correct, tf.float32)) # 随机梯度下降优化器 optimizer = tf.optimizers.SGD(lr) # Optimization def run_optimization(x, y): with tf.GradientTape() as g: pred = logistic_regression(x) loss = cross_entropy(y_pred=pred, y_true=y) gradients = g.gradient(loss, [W, b]) optimizer.apply_gradients(zip(gradients, [W, b])) # Training for step, (batch_x, batch_y) in enumerate(train_dataset.take(training_steps), 1): # Run the optimization to update W and b run_optimization(x=batch_x, y=batch_y) if step % display_step == 0: pred = logistic_regression(batch_x) loss = cross_entropy(y_pred=pred, y_true=batch_y) acc = accuracy(y_pred=pred, y_true=batch_y) print("Step: %i, loss: %f, accuracy: %f" % (step, loss, acc))
pred = logistic_regression(x_test) print(f"Test Accuracy: {accuracy(pred, y_test)}")
Test Accuracy: 0.892300009727478
import matplotlib.pyplot as plt n_images = 5 test_images = x_test[:n_images] predictions = logistic_regression(test_images) # 预测前5张 for i in range(n_images): plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray') plt.show() print("Model prediction: %i" % np.argmax(predictions.numpy()[i]))
Model prediction: 7
Model prediction: 2
Model prediction: 1
Model prediction: 0
Model prediction: 4
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