导入相关类库
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
%matplotlib inline
np.random.seed(1)
TensorFlow计算损失函数样例
代码实现：
y_hat = tf.constant(36, name='y_hat') # Define y_hat constant. Set to 36.
y = tf.constant(39, name='y') # Define y. Set to 39
loss = tf.Variable((y - y_hat)**2, name='loss') # Create a variable for the loss
init = tf.global_variables_initializer() # When init is run later (session.run(init)),
# the loss variable will be initialized and ready to be computed
with tf.Session() as session: # Create a session and print the output
session.run(init) # Initializes the variables
print(session.run(loss)) # Prints the loss
注：只是定义了损失函数loss，这并不能计算损失函数；需要执行init=tf.global_variables_initializer()，才能计算
placeholder代码样例
创建placeholder时，不需要传入值，当执行这个变量时才需要传入具体值
x = tf.placeholder(tf.int64, name = 'x')
print(sess.run(2 * x, feed_dict = {x: 3}))
sess.close()
linear function代码实现
代码实现公式：Y=WX+b
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)
执行：
sess = tf.Session()
result = sess.run(Y)
sess.close()
sigmoid函数的代码实现
定义占位符：
x = tf.placeholder(tf.float32, name="x")
代码定义sigmoid：
sigmoid = tf.sigmoid(x)
执行：
with tf.Session() as session:
result = session.run(sigmoid, feed_dict = {x: z})
代码实现损失函数
定义占位符：
z = tf.placeholder(tf.float32, name="z")
y = tf.placeholder(tf.float32, name="y")
定义计算损失函数：
cost = tf.nn.sigmoid_cross_entropy_with_logits(logits = z, labels = y)
执行：
sess = tf.Session()
cost = sess.run(cost,feed_dict={z:logits,y:labels})
sess.close()
矩阵的转换
代码实现：tf.one_hot(labels, depth, axis)
初始化0、1矩阵
- tf.zeros(shape)
- tf.ones(shape)