使用多层神经网络

我们展示如何用TensorFlow构建多层神经网络

###低出生率数据 Low Birthrate data:

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#Columns    Variable                                      Abbreviation

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#---------------------------------------------------------------------

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# Low Birth Weight (0 = Birth Weight >= 2500g,            LOW

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#                          1 = Birth Weight < 2500g)

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# Age of the Mother in Years                              AGE

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# Weight in Pounds at the Last Menstrual Period           LWT

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# Race (1 = White, 2 = Black, 3 = Other)                  RACE

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# Smoking Status During Pregnancy (1 = Yes, 0 = No)       SMOKE

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# History of Premature Labor (0 = None  1 = One, etc.)    PTL

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# History of Hypertension (1 = Yes, 0 = No)               HT

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# Presence of Uterine Irritability (1 = Yes, 0 = No)      UI

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# Birth Weight in Grams                                   BWT

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#---------------------------------------------------------------------

我们要创建的多层神经网络由三个全链接隐藏层组成, 节点数分别为 50, 25, 和 5

import tensorflow as tf

import matplotlib.pyplot as plt

import csv

import os

import os.path

import random

import numpy as np

import random

import requests

from tensorflow.python.framework import ops

name of data file

birth_weight_file = 'birth_weight.csv'

download data and create data file if file does not exist in current directory

if not os.path.exists(birth_weight_file):

birthdata_url = 'https://github.com/nfmcclure/tensorflow_cookbook/raw/master/01_Introduction/07_Working_with_Data_Sources/birthweight_data/birthweight.dat'

birth_file = requests.get(birthdata_url)

birth_data = birth_file.text.split('\r\n')

birth_header = birth_data $0$ .split('\t')

birth_data = $\[float(x) for x in y.split('\\t') if len(x)\>=1$ for y in birth_data $1:$ if len(y)>=1]

with open(birth_weight_file, "w") as f:

writer = csv.writer(f)

writer.writerows( $birth_header$ )

writer.writerows(birth_data)

f.close()

read birth weight data into memory

birth_data = \[\]

with open(birth_weight_file, newline='') as csvfile:

csv_reader = csv.reader(csvfile)

birth_header = next(csv_reader)

for row in csv_reader:

birth_data.append(row)

birth_data = $\[float(x) for x in row$ for row in birth_data]

birth_data

Extract y-target (birth weight)

y_vals = np.array( $x\[8:9$ for x in birth_data])

Filter for features of interest

cols_of_interest = $'AGE', 'LWT', 'RACE', 'SMOKE', 'PTL', 'HT', 'UI'$

x_vals = np.array( $\[x\[ix$ for ix, feature in enumerate(birth_header) if feature in cols_of_interest] for x in birth_data])

set batch size for training

batch_size = 10

make results reproducible

seed = 3

np.random.seed(seed)

#tf.set_random_seed(seed)

Split data into train/test = 80%/20%

train_indices = np.random.choice(len(x_vals), round(len(x_vals)*0.8), replace=False)

test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices)))

x_vals_train = x_vals $train_indices$

x_vals_test = x_vals $test_indices$

y_vals_train = y_vals $train_indices$

y_vals_test = y_vals $test_indices$

Record training column max and min for scaling of non-training data

train_max = np.max(x_vals_train, axis=0)

train_min = np.min(x_vals_train, axis=0)

Normalize by column (min-max norm to be between 0 and 1)

def normalize_cols(mat, max_vals, min_vals):

return (mat - min_vals) / (max_vals - min_vals)

x_vals_train = np.nan_to_num(normalize_cols(x_vals_train, train_max, train_min))

x_vals_test = np.nan_to_num(normalize_cols(x_vals_test, train_max, train_min))

#定义权重和偏置。

Define Variable Functions (weights and bias)

def init_weight(shape, st_dev):

weight = tf.Variable(tf.random.normal(shape, stddev=st_dev))

return(weight)

def init_bias(shape, st_dev):

bias = tf.Variable(tf.random.normal(shape, stddev=st_dev))

return(bias)

#定义模型!我们先创建一个根据变量生成全链接层的函数。

x_data = tf.Variable(np.random.randn(1,7),dtype=tf.float32)

y_target = tf.Variable(np.random.randn(1,1),dtype=tf.float32)

Create a fully connected layer:

def fully_connected(input_layer, weights, biases):

layer = tf.add(tf.matmul(input_layer, weights), biases)

return(tf.nn.relu(layer))

#我们初始化变量并开始训练循环。

learning_rate=0.001

Training loop

loss_vec = \[\]

test_loss = \[\]

weight_1 = init_weight(shape= $7, 25$ , st_dev=1.0)

bias_1 = init_bias(shape= $25$ , st_dev=10.0)

weight_2 = init_weight(shape= $25, 10$ , st_dev=1.0)

bias_2 = init_bias(shape= $10$ , st_dev=10.0)

weight_3 = init_weight(shape= $10, 3$ , st_dev=1.0)

bias_3 = init_bias(shape= $3$ , st_dev=10.0)

weight_4 = init_weight(shape= $3, 1$ , st_dev=1.0)

bias_4 = init_bias(shape= $1$ , st_dev=1.0)

for i in range(3000):

rand_index = np.random.choice(len(x_vals_train), size=batch_size)

rand_x = x_vals_train $rand_index$

rand_y = np.transpose( $y_vals_train\[rand_index$ ])

with tf.GradientTape() as tape:

#--------Create the first layer (50 hidden nodes)--------

layer_1 = fully_connected(x_data, weight_1, bias_1)

#--------Create second layer (25 hidden nodes)--------

layer_2 = fully_connected(layer_1, weight_2, bias_2)

#--------Create third layer (5 hidden nodes)--------

layer_3 = fully_connected(layer_2, weight_3, bias_3)

#--------Create output layer (1 output value)--------

final_output = fully_connected(layer_3, weight_4, bias_4)

Declare loss function (L1)

loss = tf.reduce_mean(tf.abs(y_target - final_output))

grads=tape.gradient(loss, $weight_1,bias_1,weight_2,bias_2,weight_3,bias_3,weight_4,bias_4$ )

loss_vec.append(loss)

weight_1.assign_sub(learning_rate*grads $0$ )

bias_1.assign_sub(learning_rate*grads $1$ )

weight_2.assign_sub(learning_rate*grads $2$ )

bias_2.assign_sub(learning_rate*grads $3$ )

weight_3.assign_sub(learning_rate*grads $4$ )

bias_3.assign_sub(learning_rate*grads $5$ )

weight_4.assign_sub(learning_rate*grads $6$ )

bias_4.assign_sub(learning_rate*grads $7$ )

#test_loss.append(test_temp_loss)

if (i+1) % 25 == 0:

print('Generation: ' + str(i+1) + '. Loss = ' + str(loss.numpy()))

#绘制损失函数。

%matplotlib inline

Plot loss (MSE) over time

plt.plot(loss_vec, 'k-', label='Train Loss')

#plt.plot(test_loss, 'r--', label='Test Loss')

plt.title('Loss (MSE) per Generation')

plt.legend(loc='upper right')

plt.xlabel('Generation')

plt.ylabel('Loss')

plt.show()

Create variable definition

def init_variable(shape):

return(tf.Variable(tf.random.normal(shape=shape)))

Create a logistic layer definition

def logistic(input_layer, multiplication_weight, bias_weight, activation = True):

linear_layer = tf.add(tf.matmul(input_layer, multiplication_weight), bias_weight)

We separate the activation at the end because the loss function will

implement the last sigmoid necessary

if activation:

return(tf.nn.sigmoid(linear_layer))

else:

return(linear_layer)

Declare optimizer

#my_opt = tf.train.AdamOptimizer(learning_rate = 0.002)

#train_step = my_opt.minimize(loss)

Actual Prediction

A1 = init_variable(shape= $7,14$ )

b1 = init_variable(shape= $14$ )

A2 = init_variable(shape= $14,5$ )

b2 = init_variable(shape= $5$ )

A3 = init_variable(shape= $5,1$ )

b3 = init_variable(shape= $1$ )

optimizer = tf.optimizers.SGD(learning_rate)

Training loop

loss_vec = \[\]

train_acc = \[\]

test_acc = \[\]

for i in range(3000):

rand_index = np.random.choice(len(x_vals_train), size=batch_size)

rand_x = x_vals_train $rand_index$

rand_y = np.transpose( $y_vals_train\[rand_index$ ])

with tf.GradientTape() as tape:

First logistic layer (7 inputs to 14 hidden nodes)

logistic_layer1 = logistic(x_data, A1, b1)

Second logistic layer (14 hidden inputs to 5 hidden nodes)

logistic_layer2 = logistic(logistic_layer1, A2, b2)

Final output layer (5 hidden nodes to 1 output)

final_output = logistic(logistic_layer2, A3, b3, activation=False)

Declare loss function (Cross Entropy loss)

loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=final_output, labels=y_target))

grads=tape.gradient(loss, $A1,b1,A2,b2,A3,b3$ )

optimizer.apply_gradients(zip(grads, $A1,b1,A2,b2,A3,b3$ ))

loss_vec.append(loss)

#A1.assign_sub(learning_rate*grads $0$ )

#b1.assign_sub(learning_rate*grads $1$ )

#A2.assign_sub(learning_rate*grads $2$ )

#b2.assign_sub(learning_rate*grads $3$ )

#A3.assign_sub(learning_rate*grads $4$ )

#b3.assign_sub(learning_rate*grads $5$ )

if (i+1)%150==0:

print('Loss = ' + str(loss.numpy()))

%matplotlib inline

Plot loss over time

plt.plot(loss_vec, 'k-')

plt.title('Cross Entropy Loss per Generation')

plt.xlabel('Generation')

plt.ylabel('Cross Entropy Loss')

plt.show()

用Python实现神经网络(四)

使用多层神经网络

name of data file

download data and create data file if file does not exist in current directory

read birth weight data into memory

Extract y-target (birth weight)

Filter for features of interest

set batch size for training

make results reproducible

Split data into train/test = 80%/20%

Record training column max and min for scaling of non-training data

Normalize by column (min-max norm to be between 0 and 1)

Define Variable Functions (weights and bias)

Create a fully connected layer:

Training loop

Declare loss function (L1)

Plot loss (MSE) over time

Create variable definition

Create a logistic layer definition

We separate the activation at the end because the loss function will

implement the last sigmoid necessary

Declare optimizer

Actual Prediction

Training loop

First logistic layer (7 inputs to 14 hidden nodes)

Second logistic layer (14 hidden inputs to 5 hidden nodes)

Final output layer (5 hidden nodes to 1 output)

Declare loss function (Cross Entropy loss)

Plot loss over time