@Author：Runsen

TF 目前發布2.5 版本，之前閱讀1.X官方文檔，最近查看2.X的文檔。tensorflow是非常強的工具，生態龐大。

tensorflow提供了Keras的分支，這里不再提供Keras相關順序模型教程。

關于環境：ubuntu的 GPU，需要cuda和nvcc

不會安裝：查看

• 完整的Ubuntu18.04深度學習GPU環境配置，英偉達顯卡驅動安裝、cuda9.0安裝、cudnn的安裝、anaconda安裝

不安裝，直接翻墻用colab

測試GPU

>>> from tensorflow.python.client import device_lib >>> device_lib.list_local_devices()

這是意思是掛了一個顯卡

具體查看官方文檔：https://www.tensorflow.org/install

服務器跑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變量的操作 # (+, *, ...) add = tf.add(a, b) sub = tf.subtract(a, b) mul = tf.multiply(a, b) div = tf.divide(a, b)# 通過numpy返回數值 和torch一樣 print("add =", add.numpy()) print("sub =", sub.numpy()) print("mul =", mul.numpy()) print("div =", div.numpy())add = 7 sub = -5 mul = 6 div = 0.16666666666666666mean = 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快速構建線性回歸模型，這里不使用kears的順序模型，而是采用torch的模型定義的寫法。

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# 權重和偏差，隨機初始化。 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))# 隨機梯度下降優化器。 optimizer = tf.optimizers.SGD(learning_rate)# 優化過程。 def run_optimization():# 將計算包在GradientTape中，以便自動區分。with tf.GradientTape() as g:pred = linear_regression(X)loss = mean_square(pred, Y)# 計算梯度。gradients = g.gradient(loss, [W, b])# 按照梯度更新W和b。optimizer.apply_gradients(zip(gradients, [W, b]))#按給定的步數進行訓練。 for step in range(1, training_steps + 1):# 運行優化以更新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()

分類模型

本例使用MNIST手寫數字。數據集包含60000個訓練示例和10000個測試示例。這些數字已經過大小標準化，并在一個固定大小的圖像（28x28像素）中居中，值從0到255。
在本例中，每個圖像將轉換為float32，標準化為[0，1]，并展平為784個特征（28×28）的一維數組。

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# 打亂順序： 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):# 應用softmax函數將logit標準化為概率分布out = tf.nn.softmax(tf.matmul(x, W) + b)return out# 交叉熵損失函數 def cross_entropy(y_pred, y_true):# 將標簽編碼為一個one_hot向量y_true = tf.one_hot(y_true, depth=num_classes)# 剪裁預測值避免錯誤y_pred = tf.clip_by_value(y_pred, 1e-9, 1)# 計算交叉熵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))# 隨機梯度下降優化器 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 brun_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) # 預測前5張 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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