本文主要是介绍Andrew Ng 吴恩达的深度学习课程作业 TensorFlow Tutorial (TF2兼容)，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
使用TensorFlow 2.6.0版本改写TensorFlow 1的代码，使用TF2兼容TF1的API。
1 - Exploring the Tensorflow Library
1.1 导入相关库
import math
import h5py
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
from tf_utils import convert_to_one_hot, load_dataset, predict, random_mini_batches%matplotlib inline
np.random.seed(1)1.2 计算 Loss
# 禁用 eager execution 以使用 TensorFlow 1.x API 风格
tf.compat.v1.disable_eager_execution()
# 定义变量
y_hat = tf.constant(36, name="y_hat")
y = tf.constant(39, name="y")# 计算损失
loss = tf.Variable((y - y_hat) ** 2, name='loss')# 初始化变量
init = tf.compat.v1.global_variables_initializer()# 创建会话
with tf.compat.v1.Session() as session:# 运行初始化操作session.run(init)# 计算损失值loss_value = session.run(loss)# 打印损失值print(loss_value)# Output输出
# 91.3 初始化变量
a = tf.constant(2)
b = tf.constant(10)
c = tf.multiply(a, b)
print(c)# Output 输出
# Tensor("Mul:0", shape=(), dtype=int32)1.4 运行Session
# 使用tf.compat.v1.Session()来运行静态图
with tf.compat.v1.Session() as sess:# 由于tf.function已经将操作包装成了一个图，我们可以直接运行它result = sess.run(c)print(result)# Output 输出
# 201.5 变量占位并赋值
# Change the value of x in the feed_dict
x = tf.compat.v1.placeholder(tf.int64, name='x')with tf.compat.v1.Session() as sess:# 运行图并获取结果print(sess.run(2 * x, feed_dict={x: 3}))# Output 输出
# 61.6 Linear function 线性函数
# GRADED FUNCTION: linear_functiondef linear_function():"""Implements a linear function:Initializes W to be a random tensor of shape (4,3)Initializes X to be a random tensor of shape (3,1)Initializes b to be a random tensor of shape (4,1)Returns:result -- runs the session for Y = WX + b"""np.random.seed(1)### START CODE HERE ### (4 lines of code)X = tf.constant(np.random.randn(3, 1), name="X")W = tf.constant(np.random.randn(4, 3), name="W")b = tf.constant(np.random.randn(4, 1), name="b")Y = tf.add(tf.matmul(W, X), b)### END CODE HERE #### Create the session using tf.Session() and run it with sess.run(...) on the variable you want to calculate### START CODE HERE ###sess = tf.compat.v1.Session()result = sess.run(Y)### END CODE HERE #### close the sessionsess.close()return resultprint("result = " + str(linear_function()))# Output 输出
'''
result = [[-2.15657382][ 2.95891446][-1.08926781][-0.84538042]]
'''1.7 计算 Sigmoid
# GRADED FUNCTION: sigmoiddef sigmoid(z):"""Computes the sigmoid of zArguments:z -- input value, scalar or vectorReturns:results -- the sigmoid of z"""x = tf.compat.v1.placeholder(tf.float32, name="x")sigmoid = tf.sigmoid(x)with tf.compat.v1.Session() as sess:# Initialize global variablesresult = sess.run(sigmoid, feed_dict={x: z})return resultprint("sigmoid(0) = " + str(sigmoid(0)))
print("sigmoid(12) = " + str(sigmoid(12)))# Output 输出
'''
sigmoid(0) = 0.5
sigmoid(12) = 0.9999939
'''1.8 计算成本Cost
# GRADED FUNCTION: costdef cost(logits, labels):"""Computes the cost using the sigmoid cross entropyArguments:logits -- vector containing z, output of the last linear unit (before the final sigmoid activation)labels -- vector of labels y (1 or 0)Note: What we've been calling "z" and "y" in this class are respectively called "logits" and "labels"in the TensorFlow documentation. So logits will feed into z, and labels into y.Returns:cost -- runs the session of the cost (formula (2))"""# Create the placeholders for "logits" (z) and "labels" (y) (approx. 2 lines)z = tf.compat.v1.placeholder(tf.float32, name="z")y = tf.compat.v1.placeholder(tf.float32, name="y")# Use the loss function (approx. 1 line)cost = tf.nn.sigmoid_cross_entropy_with_logits(logits=z, labels=y)# Create a session (approx. 1 line). See method 1 above.with tf.compat.v1.Session() as sess:# Initialize variablessess.run(tf.compat.v1.global_variables_initializer())# Run the session (approx. 1 line).cost_value = sess.run(cost, feed_dict={z: logits, y: labels})### END CODE HERE ###return cost_valuecost = cost(logits, labels)
print("cost = " + str(cost))# Output 输出
# cost = [1.0053872 1.0366409 0.41385433 0.39956614]
1.9 独热编码 One Hot Encoding
# GRADED FUNCTION: one_hot_matrixdef one_hot_matrix(labels, C):"""Creates a matrix where the i-th row corresponds to the ith class number and the jth columncorresponds to the jth training example. So if example j had a label i. Then entry (i,j)will be 1.Arguments:labels -- vector containing the labelsC -- number of classes, the depth of the one hot dimensionReturns:one_hot -- one hot matrix"""### START CODE HERE #### Create a tf.constant equal to C (depth), name it 'C'. (approx. 1 line)C = tf.constant(value=C, name="C")# Use tf.one_hot, be careful with the axis (approx. 1 line)one_hot_matrix = tf.one_hot(labels, C, axis=0)with tf.compat.v1.Session() as sess:# Initialize variablesone_hot = sess.run(one_hot_matrix)### END CODE HERE ###return one_hotlabels = np.array([1, 2, 3, 0, 2, 1])
one_hot = one_hot_matrix(labels, C=4)
print("one_hot = " + str(one_hot))# Output 输出
'''
one_hot = [[0. 0. 0. 1. 0. 0.][1. 0. 0. 0. 0. 1.][0. 1. 0. 0. 1. 0.][0. 0. 1. 0. 0. 0.]]
'''1.10 初始化向量
# GRADED FUNCTION: onesdef ones(shape):"""Creates an array of ones of dimension shapeArguments:shape -- shape of the array you want to createReturns:ones -- array containing only ones"""### START CODE HERE #### Create "ones" tensor using tf.ones(...). (approx. 1 line)ones = tf.ones(shape)with tf.compat.v1.Session() as sess:# Run the session (approx. 1 line).ones = sess.run(ones)### END CODE HERE ###return onesprint("ones = " + str(ones([3])))# Output 输出
# ones = [1. 1. 1.]2 - Building your first neural network in tensorflow
2.1 读取数据
# Loading the dataset
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()# Example of a picture
index = 11
plt.imshow(X_train_orig[index])
print("y = " + str(np.squeeze(Y_train_orig[:, index])))# Output 输出
# y = 12.2 数据预处理
# Flatten the training and test images
X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T
X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T
# Normalize image vectors
X_train = X_train_flatten / 255.0
X_test = X_test_flatten / 255.0
# Convert training and test labels to one hot matrices
Y_train = convert_to_one_hot(Y_train_orig, 6)
Y_test = convert_to_one_hot(Y_test_orig, 6)print("number of training examples = " + str(X_train.shape[1]))
print("number of test examples = " + str(X_test.shape[1]))
print("X_train shape: " + str(X_train.shape))
print("Y_train shape: " + str(Y_train.shape))
print("X_test shape: " + str(X_test.shape))
print("Y_test shape: " + str(Y_test.shape))# Output 输出
'''
number of training examples = 1080
number of test examples = 120
X_train shape: (12288, 1080)
Y_train shape: (6, 1080)
X_test shape: (12288, 120)
Y_test shape: (6, 120)
'''2.3 创建占位变量 Placeholders
# GRADED FUNCTION: create_placeholdersdef create_placeholders(n_x, n_y):"""Creates the placeholders for the tensorflow session.Arguments:n_x -- scalar, size of an image vector (num_px * num_px = 64 * 64 * 3 = 12288)n_y -- scalar, number of classes (from 0 to 5, so -> 6)Returns:X -- placeholder for the data input, of shape [n_x, None] and dtype "float"Y -- placeholder for the input labels, of shape [n_y, None] and dtype "float"Tips:- You will use None because it let's us be flexible on the number of examples you will for the placeholders.In fact, the number of examples during test/train is different."""### START CODE HERE ### (approx. 2 lines)X = tf.compat.v1.placeholder(tf.float32, shape=[n_x, None], name='X')Y = tf.compat.v1.placeholder(tf.float32, shape=[n_y, None], name='Y')### END CODE HERE ###return X, YX, Y = create_placeholders(12288, 6)
print("X = " + str(X))
print("Y = " + str(Y))# Output 输出
'''
X = Tensor("X_5:0", shape=(12288, None), dtype=float32)
Y = Tensor("Y_2:0", shape=(6, None), dtype=float32)
'''2.4 初始化参数
# GRADED FUNCTION: initialize_parametersdef initialize_parameters():"""Initializes parameters to build a neural network with tensorflow. The shapes are:W1 : [25, 12288]b1 : [25, 1]W2 : [12, 25]b2 : [12, 1]W3 : [6, 12]b3 : [6, 1]Returns:parameters -- a dictionary of tensors containing W1, b1, W2, b2, W3, b3"""tf.random.set_seed(1) # so that your "random" numbers match ours### START CODE HERE ### (approx. 6 lines of code)W1 = tf.compat.v1.get_variable("W1",[25,12288],initializer = tf.initializers.GlorotUniform(seed=1))b1 = tf.compat.v1.get_variable("b1",[25,1],initializer=tf.zeros_initializer())W2 = tf.compat.v1.get_variable("W2", [12, 25], initializer = tf.initializers.GlorotUniform(seed=1))b2 = tf.compat.v1.get_variable("b2", [12, 1], initializer = tf.zeros_initializer())W3 = tf.compat.v1.get_variable("W3", [6, 12], initializer = tf.initializers.GlorotUniform(seed=1))b3 = tf.compat.v1.get_variable("b3", [6, 1], initializer = tf.zeros_initializer())### END CODE HERE ###parameters = {"W1": W1, "b1": b1, "W2": W2, "b2": b2, "W3": W3, "b3": b3}return parametersops.reset_default_graph()
with tf.compat.v1.Session() as sess:parameters = initialize_parameters()print("W1 = " + str(parameters["W1"]))print("b1 = " + str(parameters["b1"]))print("W2 = " + str(parameters["W2"]))print("b2 = " + str(parameters["b2"]))# Output 输出
'''
W1 = <tf.Variable 'W1:0' shape=(25, 12288) dtype=float32>
b1 = <tf.Variable 'b1:0' shape=(25, 1) dtype=float32>
W2 = <tf.Variable 'W2:0' shape=(12, 25) dtype=float32>
b2 = <tf.Variable 'b2:0' shape=(12, 1) dtype=float32>
'''2.5 前向传播
# GRADED FUNCTION: forward_propagationdef forward_propagation(X, parameters):"""Implements the forward propagation for the model: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAXArguments:X -- input dataset placeholder, of shape (input size, number of examples)parameters -- python dictionary containing your parameters "W1", "b1", "W2", "b2", "W3", "b3"the shapes are given in initialize_parametersReturns:Z3 -- the output of the last LINEAR unit"""# Retrieve the parameters from the dictionary "parameters"W1 = parameters["W1"]b1 = parameters["b1"]W2 = parameters["W2"]b2 = parameters["b2"]W3 = parameters["W3"]b3 = parameters["b3"]### START CODE HERE ### (approx. 5 lines) # Numpy Equivalents:Z1 = tf.add(tf.matmul(W1, X), b1) # Z1 = np.dot(W1, X) + b1A1 = tf.nn.relu(Z1) # A1 = relu(Z1)Z2 = tf.add(tf.matmul(W2, A1), b2) # Z2 = np.dot(W2, a1) + b2A2 = tf.nn.relu(Z2) # A2 = relu(Z2)Z3 = tf.add(tf.matmul(W3, A2), b3) # Z3 = np.dot(W3,Z2) + b3### END CODE HERE ###return Z3ops.reset_default_graph()
with tf.compat.v1.Session() as sess:X, Y = create_placeholders(12288, 6)parameters = initialize_parameters()Z3 = forward_propagation(X, parameters)print("Z3 = " + str(Z3))# Output 输出
# Z3 = Tensor("Add_2:0", shape=(6, None), dtype=float32)2.6 计算成本 Cost
# GRADED FUNCTION: compute_costdef compute_cost(Z3, Y):"""Computes the costArguments:Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (6, number of examples)Y -- "true" labels vector placeholder, same shape as Z3Returns:cost - Tensor of the cost function"""# to fit the tensorflow requirement for tf.nn.softmax_cross_entropy_with_logits(...,...)logits = tf.transpose(Z3)labels = tf.transpose(Y)### START CODE HERE ### (1 line of code)cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))### END CODE HERE ###return costops.reset_default_graph()
with tf.compat.v1.Session() as sess:X, Y = create_placeholders(12288, 6)parameters = initialize_parameters()Z3 = forward_propagation(X, parameters)cost = compute_cost(Z3, Y)print("cost = " + str(cost))# Output 输出
# cost = Tensor("Mean:0", shape=(), dtype=float32)2.7 构建模型
def model(X_train,Y_train,X_test,Y_test,learning_rate=0.0001,num_epochs=1500,minibatch_size=32,print_cost=True,
):"""Implements a three-layer tensorflow neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SOFTMAX.Arguments:X_train -- training set, of shape (input size = 12288, number of training examples = 1080)Y_train -- test set, of shape (output size = 6, number of training examples = 1080)X_test -- training set, of shape (input size = 12288, number of training examples = 120)Y_test -- test set, of shape (output size = 6, number of test examples = 120)learning_rate -- learning rate of the optimizationnum_epochs -- number of epochs of the optimization loopminibatch_size -- size of a minibatchprint_cost -- True to print the cost every 100 epochsReturns:parameters -- parameters learnt by the model. They can then be used to predict."""#ops.reset_default_graph() # to be able to rerun the model without overwriting tf variablesops.reset_default_graph()tf.random.set_seed(1) # to keep consistent resultsseed = 3 # to keep consistent results(n_x, m) = X_train.shape # (n_x: input size, m : number of examples in the train set)n_y = Y_train.shape[0] # n_y : output sizecosts = [] # To keep track of the cost# Create Placeholders of shape (n_x, n_y)### START CODE HERE ### (1 line)X, Y = create_placeholders(n_x, n_y)### END CODE HERE #### Initialize parameters### START CODE HERE ### (1 line)parameters = initialize_parameters()### END CODE HERE ####print(parameters)# Forward propagation: Build the forward propagation in the tensorflow graph### START CODE HERE ### (1 line)Z3 = forward_propagation(X, parameters)### END CODE HERE #### Cost function: Add cost function to tensorflow graph### START CODE HERE ### (1 line)cost = compute_cost(Z3, Y)### END CODE HERE #### Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer.### START CODE HERE ### (1 line)optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)### END CODE HERE #### Initialize all the variablesinit = tf.compat.v1.global_variables_initializer()#print(init)# Start the session to compute the tensorflow graphwith tf.compat.v1.Session() as sess:# Run the initializationsess.run(init)# Do the training loopfor epoch in range(num_epochs):epoch_cost = 0.0 # Defines a cost related to an epochnum_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train setseed = seed + 1minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)for minibatch in minibatches:# Select a minibatch(minibatch_X, minibatch_Y) = minibatch# IMPORTANT: The line that runs the graph on a minibatch.# Run the session to execute the "optimizer" and the "cost", the feedict should contain a minibatch for (X,Y).### START CODE HERE ### (1 line)_, minibatch_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y})### END CODE HERE ###epoch_cost += minibatch_cost / num_minibatches# Print the cost every epochif print_cost == True and epoch % 100 == 0:print("Cost after epoch %i: %f" % (epoch, epoch_cost))if print_cost == True and epoch % 5 == 0:costs.append(epoch_cost)# plot the costplt.plot(np.squeeze(costs))plt.ylabel("cost")plt.xlabel("iterations (per tens)")plt.title("Learning rate =" + str(learning_rate))plt.show()# lets save the parameters in a variableparameters = sess.run(parameters)print("Parameters have been trained!")# Calculate the correct predictionscorrect_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))# Calculate accuracy on the test setaccuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))print("Train Accuracy:", accuracy.eval({X: X_train, Y: Y_train}))print("Test Accuracy:", accuracy.eval({X: X_test, Y: Y_test}))return parametersparameters = model(X_train, Y_train, X_test, Y_test, minibatch_size=64)# Output 输出
'''
Cost after epoch 0: 1.969848
Cost after epoch 100: 0.945937
Cost after epoch 200: 0.701537
Cost after epoch 300: 0.575044
Cost after epoch 400: 0.486772
Cost after epoch 500: 0.422395
Cost after epoch 600: 0.355608
Cost after epoch 700: 0.304337
Cost after epoch 800: 0.253242
Cost after epoch 900: 0.204498
Cost after epoch 1000: 0.166818
Cost after epoch 1100: 0.132297
Cost after epoch 1200: 0.098340
Cost after epoch 1300: 0.076905
Cost after epoch 1400: 0.057851
Parameters have been trained!
Train Accuracy: 0.99722224
Test Accuracy: 0.80833334
'''2.8 测试自己的图片
import scipy
from PIL import Imageimport imageio.v2 as im## START CODE HERE ## (PUT YOUR IMAGE NAME)
my_image = "thumbs_up.jpg"
## END CODE HERE ### We preprocess your image to fit your algorithm.
fname = "images/" + my_image
image = np.array(np.array(im.imread(fname)))
#my_image = scipy.misc.imresize(image, size=(64, 64)).reshape((1, 64 * 64 * 3)).T
my_image = np.array(Image.fromarray(image).resize((64, 64))).reshape((1, 64 * 64 * 3)).T
my_image_prediction = predict(my_image, parameters)plt.imshow(image)
print("Your algorithm predicts: y = " + str(np.squeeze(my_image_prediction)))# Output 输出
# Your algorithm predicts: y = 3这篇关于Andrew Ng 吴恩达的深度学习课程作业 TensorFlow Tutorial (TF2兼容)的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
