吴恩达深度学习2-Week3课后作业-Tensorflow
一、deeplearning-assignment
到目前为止,我们一直使用numpy来建立神经网络。这次作业将深入学习框架,可以更容易地建立神经网络。
TensorFlow,PaddlePaddle,Torch,Caffe,Keras等机器学习框架可以显著地加速机器学习开发。这些框架有很多文档,可以随意阅读。在本次任务中,将学习如何在TensorFlow中执行以下操作:
- 初始化变量
- 开始你自己的会话
- 训练算法
- 实现一个神经网络
编程框架不仅可以缩短编码时间,但有时也可以执行优化来加速代码,关于tensorflow相关算法在文章后面已给出。
现在有这样一个问题:对于0-5的手势图片,利用tensorflow框架实现softmax分类器。
训练数据:1080张手势图片代表0-5的数字,每张图片是64 * 64像素大小,每种数字180张图片。
测试数据:120张手势图片代表0-5的数字,每张图片是64 * 64像素大小,每种数字20张图片。
目标是建立一个模型,使得能够以高准确度识别一张图片对应的数字。模型的结构为: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX。
实验结果:
二、相关算法代码
import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
np.random.seed(1)
# y_hat = tf.constant(36, name='y_hat')
# y = tf.constant(39, name='y')
#
# loss = tf.Variable((y - y_hat) ** 2, name='loss')
# init = tf.global_variables_initializer()
# with tf.Session() as session:
# session.run(init)
# print(session.run(loss))
# a = tf.constant(2)
# b = tf.constant(10)
# c = tf.multiply(a, b)
# sess = tf.Session()
# print(sess.run(c))
# x = tf.placeholder(tf.int64, name='x')
# print(sess.run(2 * x, feed_dict={x: 3}))
# sess.close()
def linear_function():
np.random.seed(1)
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)
sess = tf.Session()
result = sess.run(Y)
sess.close()
return result
# print("result = " + str(linear_function()))
def sigmoid(z):
x = tf.placeholder(tf.float32, name='x')
sigmoid = tf.sigmoid(x)
with tf.Session() as session:
result = session.run(sigmoid, feed_dict={x: z})
return result
# print("sigmoid(0) = " + str(sigmoid(0)))
# print("sigmoid(12) = " + str(sigmoid(12)))
def cost(logits, labels):
z = tf.placeholder(tf.float32, name='logits')
y = tf.placeholder(tf.float32, name='labels')
cost = tf.nn.sigmoid_cross_entropy_with_logits(logits=z, labels=y)
session = tf.Session()
cost = session.run(cost, feed_dict={z: logits, y: labels})
session.close()
return cost
# logits = sigmoid(np.array([0.2, 0.4, 0.7, 0.9]))
# cost = cost(logits, np.array([0, 0, 1, 1]))
# print(logits)
def one_hot_matrix(labels, C):
C = tf.constant(C, name='C')
one_hot_matrix = tf.one_hot(labels, C, axis=0)
sess = tf.Session()
one_hot = sess.run(one_hot_matrix)
sess.close()
return one_hot
# labels = np.array([1, 2, 3, 0, 2, 1])
# one_hot = one_hot_matrix(labels, C=4)
# print("one_hot = " + str(one_hot))
def ones(shape):
ones = tf.ones(shape)
sess = tf.Session()
ones = sess.run(ones)
sess.close()
return ones
# print("ones = " + str(ones([3])))
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
# print(X_train_orig.shape)
# print(Y_train_orig.shape)
# print(X_test_orig.shape)
# print(Y_test_orig.shape)
# index = 0
# plt.imshow(X_train_orig[index])
# plt.show()
# print("y = " + str(np.squeeze(Y_train_orig[:, index])))
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
X_train = X_train_flatten / 255.
X_test = X_test_flatten / 255.
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))
def create_placeholders(n_x, n_y):
X = tf.placeholder(tf.float32, shape=(n_x, None), name='X')
Y = tf.placeholder(tf.float32, shape=(n_y, None), name='Y')
return X, Y
# X, Y = create_placeholders(12288, 6)
# print("X = " + str(X))
# print("Y = " + str(Y))
def initialize_parameters():
tf.set_random_seed(1)
W1 = tf.get_variable("W1", [25, 12288], initializer=tf.contrib.layers.xavier_initializer(seed=1))
b1 = tf.get_variable("b1", [25, 1], initializer=tf.zeros_initializer())
W2 = tf.get_variable("W2", [12, 25], initializer=tf.contrib.layers.xavier_initializer(seed=1))
b2 = tf.get_variable("b2", [12, 1], initializer=tf.zeros_initializer())
W3 = tf.get_variable("W3", [6, 12], initializer=tf.contrib.layers.xavier_initializer(seed=1))
b3 = tf.get_variable("b3", [6, 1], initializer=tf.zeros_initializer())
parameters = {"W1": W1,
"b1": b1,
"W2": W2,
"b2": b2,
"W3": W3,
"b3": b3}
return parameters
# tf.reset_default_graph()
# with tf.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"]))
def forward_propagation(X, parameters):
W1 = parameters['W1']
b1 = parameters['b1']
W2 = parameters['W2']
b2 = parameters['b2']
W3 = parameters['W3']
b3 = parameters['b3']
Z1 = tf.matmul(W1, X) + b1
A1 = tf.nn.relu(Z1)
Z2 = tf.matmul(W2, A1) + b2
A2 = tf.nn.relu(Z2)
Z3 = tf.matmul(W3, A2) + b3
return Z3
# tf.reset_default_graph()
# with tf.Session() as sess:
# X, Y = create_placeholders(12288, 6)
# parameters = initialize_parameters()
# Z3 = forward_propagation(X, parameters)
# print("Z3 = " + str(Z3))
def compute_cost(Z3, Y):
logits = tf.transpose(Z3)
labels = tf.transpose(Y)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=labels))
return cost
# tf.reset_default_graph()
# with tf.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))
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.
ops.reset_default_graph()
tf.set_random_seed(1)
seed = 3
(n_x, m) = X_train.shape
n_y = Y_train.shape[0]
costs = []
X, Y = create_placeholders(n_x, n_y)
parameters = initialize_parameters()
Z3 = forward_propagation(X, parameters)
cost = compute_cost(Z3, Y)
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for epoch in range(num_epochs):
epoch_cost = 0.
num_minibatches = int(m / minibatch_size)
seed = seed + 1
minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)
for minibatch in minibatches:
(minibatch_X, minibatch_Y) = minibatch
_, minibatch_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y})
epoch_cost += minibatch_cost / num_minibatches
if 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)
plt.plot(np.squeeze(costs))
plt.ylabel('cost')
plt.xlabel('iterations (per tens)')
plt.title("Learning rate =" + str(learning_rate))
plt.show()
parameters = sess.run(parameters)
print("Parameters have been trained!")
correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))
accuracy = 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 parameters
parameters = model(X_train, Y_train, X_test, Y_test)
三、总结
tensorflow是深度学习里的一种框架,可以帮助我们更快地建立模型。
当利用tensorflow进行编码时,主要可分为如下步骤:
- 创建一个graph,包括张量(Variables,Placeholders...)和操作(tf.matmul,tf.add...)。
- 创建一个session。
- 将session初始化。
- 通过run上述的session来执行graph。
当在optimizer对象中run初始化的session时,反向传播和优化是自动完成的。