……

Tensorflow从0到1(4)之神经网络

一维数据集上的神经网络

代码实现：

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior() # 使用静态图模式运行以下代码
assert tf.__version__.startswith('2.')
sess = tf.Session()

# 2 初始化数据
data_size = 25
data_1d = np.random.normal(size=data_size)
x_input_1d = tf.placeholder(dtype=tf.float32, shape=[data_size])


# 3 定义卷积层
def conv_layer_1d(input_1d, my_filter):
    # Make 1d input 4d
    input_2d = tf.expand_dims(input_1d, 0)
    input_3d = tf.expand_dims(input_2d, 0)
    input_4d = tf.expand_dims(input_3d, 3)
    # Perform convolution
    convolution_output = tf.nn.conv2d(input_4d, filter=my_filter,
                                      strides=[1, 1, 1, 1], padding='VALID')
    conv_output_1d = tf.squeeze(convolution_output)
    return conv_output_1d
    # Now drop extra dimensions


my_filter = tf.Variable(tf.random_normal(shape=[1, 5, 1, 1]))
my_convolution_output = conv_layer_1d(x_input_1d, my_filter)


# 4 激励函数
def activation(input_1d):
    return tf.nn.relu(input_1d)


my_activation_output = activation(my_convolution_output)


# 池化
def max_pool(input_1d, width):
    # First we make the 1d input into 4d.
    input_2d = tf.expand_dims(input_1d, 0)
    input_3d = tf.expand_dims(input_2d, 0)
    input_4d = tf.expand_dims(input_3d, 3)
    # Perform the max pool operation
    pool_output = tf.nn.max_pool(input_4d, ksize=[1, 1, width, 1], strides=[1, 1, 1, 1],
                                 padding='VALID')
    pool_output_1d = tf.squeeze(pool_output)
    return pool_output_1d


my_maxpool_output = max_pool(my_activation_output, width=5)


# 全连接层
def fully_connected(input_layer, num_outputs):
    # Create weights
    weight_shape = tf.squeeze(tf.stack([tf.shape(input_layer), [num_outputs]]))
    weight = tf.random_normal(weight_shape, stddev=0.1)
    bias = tf.random_normal(shape=[num_outputs])
    # make input into 2d
    input_layer_2d = tf.expand_dims(input_layer, 0)
    # perform fully connected operations
    full_output = tf.add(tf.matmul(input_layer_2d, weight), bias)
    # Drop extra dimmensions
    full_output_1d = tf.squeeze(full_output)
    return full_output_1d


my_full_output = fully_connected(my_maxpool_output, 5)

# 初始化变量，运行计算图大阴每层输出结果
init = tf.global_variables_initializer()
sess.run(init)
feed_dict = {x_input_1d: data_1d}
# Convolution Output
print("Input = array of length 25")
print("Convolution w/filter length = 5, stride size = 1, results in an array of legth 21:")
print(sess.run(my_convolution_output, feed_dict=feed_dict))
# Activation Output
print('\nInput = the above array of length 21')
print('Relu element wise returns the array of length 21:')
print(sess.run(my_activation_output, feed_dict=feed_dict))
# Maxpool output
print('\nInput = the above array of length 21')
print('MaxPool, window length = 5, stride size = 1, results in the array of length 17')
print(sess.run(my_maxpool_output, feed_dict=feed_dict))
# Fully Connected Output
print('Input = the above array of length 17')
print('Fully connected layer on all four rows with five outputs')
print(sess.run(my_full_output, feed_dict=feed_dict))
# 关闭会话
sess.close()

输出结果如下：

C:\Anaconda3\python.exe "C:\Program Files\JetBrains\PyCharm 2019.1.1\helpers\pydev\pydevconsole.py" --mode=client --port=65184
import sys; print('Python %s on %s' % (sys.version, sys.platform))
sys.path.extend(['C:\\app\\PycharmProjects', 'C:/app/PycharmProjects'])
Python 3.7.6 (default, Jan  8 2020, 20:23:39) [MSC v.1916 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information
IPython 7.12.0 -- An enhanced Interactive Python. Type '?' for help.
PyDev console: using IPython 7.12.0
Python 3.7.6 (default, Jan  8 2020, 20:23:39) [MSC v.1916 64 bit (AMD64)] on win32
runfile('C:/app/PycharmProjects/ArtificialIntelligence/classification.py', wdir='C:/app/PycharmProjects/ArtificialIntelligence')
WARNING:tensorflow:From C:\Anaconda3\lib\site-packages\tensorflow\python\compat\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
2020-06-16 17:49:59.355213: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2020-06-16 17:49:59.371808: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x225498bdc50 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-06-16 17:49:59.373816: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
Input = array of length 25
Convolution w/filter length = 5, stride size = 1, results in an array of legth 21:
[ 0.57410306  1.2466588  -5.2342267   1.5412238   1.1326292   0.9449366
  4.176463   -0.66215414  0.5528939  -0.09213515 -0.5939804   1.1933011
  2.2207081   3.337352   -1.3791142  -2.5168674   2.025437   -5.8652186
 -3.144189   -0.01129406 -0.23239633]
Input = the above array of length 21
Relu element wise returns the array of length 21:
[0.57410306 1.2466588  0.         1.5412238  1.1326292  0.9449366
 4.176463   0.         0.5528939  0.         0.         1.1933011
 2.2207081  3.337352   0.         0.         2.025437   0.
 0.         0.         0.        ]
Input = the above array of length 21
MaxPool, window length = 5, stride size = 1, results in the array of length 17
[1.5412238 1.5412238 4.176463  4.176463  4.176463  4.176463  4.176463
 1.1933011 2.2207081 3.337352  3.337352  3.337352  3.337352  3.337352
 2.025437  2.025437  2.025437 ]
Input = the above array of length 17
Fully connected layer on all four rows with five outputs
[ 1.1319295  -0.16019821  1.164511   -2.4321733  -3.9491425 ]

卷积层

首先，卷积层输入序列是25个元素的一维数组。卷积层的功能是相邻5个元素与过滤器（长度为5的向量）内积。因为移动步长为1，所以25个元素的序列中一共有21个相邻为5的序列，最终输出也是5。

激励函数

将卷积成的输出，21个元素的向量通过relu函数逐元素转化。输出仍是21个元素的向量。

池化层，最大值池化

取相邻5个元素的最大值。输入21个元素的序列，输出17个元素的序列。

全连接层

上述17个元素通过全连接层，有5个输出。
注意上述过程的输出都做了维度的裁剪。但在每一步的过程中都是扩充成4维张量操作的。

二维数据上的神经网络

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior() # 使用静态图模式运行以下代码
assert tf.__version__.startswith('2.')
sess = tf.Session()
# 2 创建数据和占位符
data_size = [10, 10]
data_2d = np.random.normal(size=data_size)
x_input_2d = tf.placeholder(dtype=tf.float32, shape=data_size)

# 3 卷积层：2x2过滤器
def conv_layer_2d(input_2d, my_filter):
    # First, change 2d input to 4d
    input_3d = tf.expand_dims(input_2d, 0)
    input_4d = tf.expand_dims(input_3d, 3)
    # Perform convolution
    convolution_output = tf.nn.conv2d(input_4d, filter=my_filter, strides=[1,2,2,1], padding='VALID')
    # Drop extra dimensions
    conv_output_2d = tf.squeeze(convolution_output)
    return conv_output_2d
my_filter = tf.Variable(tf.random_normal(shape=[2,2,1,1]))
my_convolution_output = conv_layer_2d(x_input_2d, my_filter)
# 4 激励函数
def activation(input_2d):
    return tf.nn.relu(input_2d)
my_activation_output = activation(my_convolution_output)
# 5 池化层
def max_pool(input_2d, width, height):
    # Make 2d input into 4d
    input_3d = tf.expand_dims(input_2d, 0)
    input_4d = tf.expand_dims(input_3d, 3)
    # Perform max pool
    pool_output = tf.nn.max_pool(input_4d, ksize=[1, height, width, 1], strides=[1,1,1,1], padding='VALID')
    # Drop extra dimensions
    pool_output_2d = tf.squeeze(pool_output)
    return pool_output_2d
my_maxpool_output = max_pool(my_activation_output, width=2, height=2)
# 6 全连接层
def fully_connected(input_layer, num_outputs):
    # Flatten into 1d
    flat_input = tf.reshape(input_layer, [-1])
    # Create weights
    weight_shape = tf.squeeze(tf.stack([tf.shape(flat_input), [num_outputs]]))
    weight = tf.random_normal(weight_shape, stddev=0.1)
    bias = tf.random_normal(shape=[num_outputs])
    # Change into 2d
    input_2d = tf.expand_dims(flat_input, 0)
    # Perform fully connected operations
    full_output = tf.add(tf.matmul(input_2d, weight), bias)
    # Drop extra dimensions
    full_output_2d = tf.squeeze(full_output)
    return full_output_2d
my_full_output = fully_connected(my_maxpool_output, 5)
# 7 初始化变量
init = tf.initialize_all_variables()
sess.run(init)

feed_dict = {x_input_2d: data_2d}
# 8 打印每层输出结果
# Convolution Output
print('Input = [10 x 10] array')
print('2x2 Convolution, stride size = [2x2], results in the [5x5] array:')
print(sess.run(my_convolution_output, feed_dict=feed_dict))
# Activation Output
print('\nInput = the above [5x5] array')
print('Relu element wise returns the [5x5] array:')
print(sess.run(my_activation_output, feed_dict=feed_dict))
# Max Pool Output
print('\nInput = the above [5x5] array')
print('[2x2] MaxPool, stride size = [1x1] results in the [4x4] array:')
print(sess.run(my_maxpool_output, feed_dict = feed_dict))
# Fully connected output
print('\nInput = the above [4x4] array')
print('Fully connected layer on all four rows with five outputs:')
print(sess.run(my_full_output, feed_dict=feed_dict))

输出结果如下：

C:\Anaconda3\python.exe "C:\Program Files\JetBrains\PyCharm 2019.1.1\helpers\pydev\pydevconsole.py" --mode=client --port=49317
import sys; print('Python %s on %s' % (sys.version, sys.platform))
sys.path.extend(['C:\\app\\PycharmProjects', 'C:/app/PycharmProjects'])
Python 3.7.6 (default, Jan  8 2020, 20:23:39) [MSC v.1916 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information
IPython 7.12.0 -- An enhanced Interactive Python. Type '?' for help.
PyDev console: using IPython 7.12.0
Python 3.7.6 (default, Jan  8 2020, 20:23:39) [MSC v.1916 64 bit (AMD64)] on win32
runfile('C:/app/PycharmProjects/ArtificialIntelligence/classification.py', wdir='C:/app/PycharmProjects/ArtificialIntelligence')
WARNING:tensorflow:From C:\Anaconda3\lib\site-packages\tensorflow\python\compat\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
2020-06-16 17:54:43.270370: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2020-06-16 17:54:43.291541: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1a303a82660 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-06-16 17:54:43.293406: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
WARNING:tensorflow:From C:\Anaconda3\lib\site-packages\tensorflow\python\util\tf_should_use.py:235: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
Input = [10 x 10] array
2x2 Convolution, stride size = [2x2], results in the [5x5] array:
[[ 0.9541372  -0.5357596  -1.9833868   0.9627971   0.34088665]
 [-1.480279    1.0545661   0.27794296  1.0536083   0.9530723 ]
 [ 1.6841828   1.1668804  -0.1371274  -3.2182534   1.9898502 ]
 [-1.077354    3.28457     3.120058   -1.7702417  -0.75471795]
 [-0.08349466  1.4444114   1.7416577   0.10787213 -3.7250257 ]]
Input = the above [5x5] array
Relu element wise returns the [5x5] array:
[[0.9541372  0.         0.         0.9627971  0.34088665]
 [0.         1.0545661  0.27794296 1.0536083  0.9530723 ]
 [1.6841828  1.1668804  0.         0.         1.9898502 ]
 [0.         3.28457    3.120058   0.         0.        ]
 [0.         1.4444114  1.7416577  0.10787213 0.        ]]
Input = the above [5x5] array
[2x2] MaxPool, stride size = [1x1] results in the [4x4] array:
[[1.0545661  1.0545661  1.0536083  1.0536083 ]
 [1.6841828  1.1668804  1.0536083  1.9898502 ]
 [3.28457    3.28457    3.120058   1.9898502 ]
 [3.28457    3.28457    3.120058   0.10787213]]
Input = the above [4x4] array
Fully connected layer on all four rows with five outputs:
[ 2.3414695   0.36326647  1.9187844   1.7268257  -0.7776375 ]

TensorFlow 实现多层神经网络

import csv
import os
import matplotlib.pyplot as plt
import numpy as np
import requests
import tensorflow.compat.v1 as tf

tf.disable_v2_behavior()  # 使用静态图模式运行以下代码
assert tf.__version__.startswith('2.')
sess = tf.Session()

# 2 导入数据
# name of data file
birth_weight_file = 'birth_weight.csv'
birthdata_url = 'https://github.com/nfmcclure/tensorflow_cookbook/raw/master' \
                '/01_Introduction/07_Working_with_Data_Sources/birthweight_data/birthweight.dat'

# Download data and create data file if file does not exist in current directory
if not os.path.exists(birth_weight_file):
    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)

# 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:
        if len(row) > 0:
            birth_data.append(row)

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

# Extract y-target (birth weight)
y_vals = np.array([x[8] 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])

# 3 设置种子
seed = 4
tf.set_random_seed(seed)
np.random.seed(seed)
batch_size = 100

# 4 划分训练集和测试集
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]


def normalize_cols(m):
    col_max = m.max(axis=0)
    col_min = m.min(axis=0)
    return (m - col_min) / (col_max - col_min)


x_vals_train = np.nan_to_num(normalize_cols(x_vals_train))
x_vals_test = np.nan_to_num(normalize_cols(x_vals_test))


# 5 定义一个设置变量和bias的函数
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


# 6 初始化占位符
x_data = tf.placeholder(shape=[None, 7], dtype=tf.float32)
y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)


# 7 创建全连接层函数，方便重复使用
def fully_connected(input_layer, weights, biases):
    layer = tf.add(tf.matmul(input_layer, weights), biases)
    return tf.nn.relu(layer)


# 8 创建算法模型
# Create second layer (25 hidden nodes)
weight_1 = init_weight(shape=[7, 25], st_dev=10.0)
bias_1 = init_weight(shape=[25], st_dev=10.0)
layer_1 = fully_connected(x_data, weight_1, bias_1)

# Create second layer (10 hidden nodes)
weight_2 = init_weight(shape=[25, 10], st_dev=10.0)
bias_2 = init_weight(shape=[10], st_dev=10.0)
layer_2 = fully_connected(layer_1, weight_2, bias_2)

# Create third layer (3 hidden nodes)
weight_3 = init_weight(shape=[10, 3], st_dev=10.0)
bias_3 = init_weight(shape=[3], st_dev=10.0)
layer_3 = fully_connected(layer_2, weight_3, bias_3)

# Create output layer (1 output value)
weight_4 = init_weight(shape=[3, 1], st_dev=10.0)
bias_4 = init_bias(shape=[1], st_dev=10.0)
final_output = fully_connected(layer_3, weight_4, bias_4)

# 9 L1损失函数
loss = tf.reduce_mean(tf.abs(y_target - final_output))
my_opt = tf.train.AdamOptimizer(0.05)
train_step = my_opt.minimize(loss)
init = tf.global_variables_initializer()
sess.run(init)

# 10 迭代200
# Initialize the loss vectors
loss_vec = []
test_loss = []
for i in range(200):
    # Choose random indices for batch selection
    rand_index = np.random.choice(len(x_vals_train), size=batch_size)
    # Get random batch
    rand_x = x_vals_train[rand_index]
    rand_y = np.transpose([y_vals_train[rand_index]])
    # Run the training step
    sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})
    # Get and store the train loss
    temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})
    loss_vec.append(temp_loss)
    # get and store the test loss
    test_temp_loss = sess.run(loss, feed_dict={x_data: x_vals_test, y_target: np.transpose([y_vals_test])})
    test_loss.append(test_temp_loss)
    if (i + 1) % 25 == 0:
        print('Generation: ' + str(i + 1) + '.Loss = ' + str(temp_loss))

# 12 绘图
plt.plot(loss_vec, 'k-', label='Train Loss')
plt.plot(test_loss, 'r--', label='Test Loss')
plt.title('Loss per Generation')
plt.xlabel('Generation')
plt.ylabel('Loss')
plt.legend(loc='upper right')
plt.show()

# Model Accuracy
actuals = np.array([x[0] for x in birth_data])
test_actuals = actuals[test_indices]
train_actuals = actuals[train_indices]
test_preds = [x[0] for x in sess.run(final_output, feed_dict={x_data: x_vals_test})]
train_preds = [x[0] for x in sess.run(final_output, feed_dict={x_data: x_vals_train})]
test_preds = np.array([0.0 if x < 2500.0 else 1.0 for x in test_preds])
train_preds = np.array([0.0 if x < 2500.0 else 1.0 for x in train_preds])
# Print out accuracies
test_acc = np.mean([x == y for x, y in zip(test_preds, test_actuals)])
train_acc = np.mean([x == y for x, y in zip(train_preds, train_actuals)])
print('On predicting the category of low birthweight from regression output (<2500g):')
print('Test Accuracy: {}'.format(test_acc))
print('Train Accuracy: {}'.format(train_acc))

执行结果：

C:\Anaconda3\python.exe "C:\Program Files\JetBrains\PyCharm 2019.1.1\helpers\pydev\pydevconsole.py" --mode=client --port=49628
import sys; print('Python %s on %s' % (sys.version, sys.platform))
sys.path.extend(['C:\\app\\PycharmProjects', 'C:/app/PycharmProjects'])
Python 3.7.6 (default, Jan  8 2020, 20:23:39) [MSC v.1916 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information
IPython 7.12.0 -- An enhanced Interactive Python. Type '?' for help.
PyDev console: using IPython 7.12.0
Python 3.7.6 (default, Jan  8 2020, 20:23:39) [MSC v.1916 64 bit (AMD64)] on win32
runfile('C:/app/PycharmProjects/ArtificialIntelligence/classification.py', wdir='C:/app/PycharmProjects/ArtificialIntelligence')
WARNING:tensorflow:From C:\Anaconda3\lib\site-packages\tensorflow\python\compat\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
2020-06-16 17:57:44.532006: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2020-06-16 17:57:44.552087: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x12781280550 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-06-16 17:57:44.554884: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
Generation: 25.Loss = 2876.5713
Generation: 50.Loss = 2733.6035
Generation: 75.Loss = 2716.0818
Generation: 100.Loss = 2562.6213
Generation: 125.Loss = 2748.026
Generation: 150.Loss = 2664.8457
Generation: 175.Loss = 2535.071
Generation: 200.Loss = 2723.3906
On predicting the category of low birthweight from regression output (<2500g):
Test Accuracy: 0.5789473684210527
Train Accuracy: 0.6556291390728477

 

 

实现了一个含有三层隐藏层的全连接神经网络。

线性预测模型的优化

代码实现：

import csv
import os

import matplotlib.pyplot as plt
import numpy as np
import requests
import tensorflow.compat.v1 as tf

tf.disable_v2_behavior()  # 使用静态图模式运行以下代码
assert tf.__version__.startswith('2.')
sess = tf.Session()

# 加载数据集，进行数据抽取和归一化
# name of data file
birth_weight_file = 'birth_weight.csv'
birthdata_url = 'https://github.com/nfmcclure/tensorflow_cookbook/raw/master' \
                '/01_Introduction/07_Working_with_Data_Sources/birthweight_data/birthweight.dat'

# Download data and create data file if file does not exist in current directory
if not os.path.exists(birth_weight_file):
    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)

# 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:
        if len(row) > 0:
            birth_data.append(row)

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

# Extract y-target (birth weight)
y_vals = np.array([x[0] 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])

# 4 划分训练集和测试集
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]


def normalize_cols(m):
    col_max = m.max(axis=0)
    col_min = m.min(axis=0)
    return (m - col_min) / (col_max - col_min)


x_vals_train = np.nan_to_num(normalize_cols(x_vals_train))
x_vals_test = np.nan_to_num(normalize_cols(x_vals_test))

# 3 声明批量大小和占位符
batch_size = 90
x_data = tf.placeholder(shape=[None, 7], dtype=tf.float32)
y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)


# 4 声明函数来初始化变量和层
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)

    if activation:
        return tf.nn.sigmoid(linear_layer)
    else:
        return linear_layer


# 5 声明神经网络的两个隐藏层和输出层
# First logistic layer (7 inputs to 14 hidden nodes)
A1 = init_variable(shape=[7, 14])
b1 = init_variable(shape=[14])
logistic_layer1 = logistic(x_data, A1, b1)
# Second logistic layer (14 inputs to 5 hidden nodes)
A2 = init_variable(shape=[14, 5])
b2 = init_variable(shape=[5])
logistic_layer2 = logistic(logistic_layer1, A2, b2)
# Final output layer (5 hidden nodes to 1 output)
A3 = init_variable(shape=[5, 1])
b3 = init_variable(shape=[1])
final_output = logistic(logistic_layer2, A3, b3, activation=False)

# 6 声明损失函数和优化方法
# Create loss function
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
    labels=y_target, logits=final_output))
# Declare optimizer
my_opt = tf.train.AdamOptimizer(learning_rate=0.002)
train_step = my_opt.minimize(loss)
# Initialize variables
init = tf.global_variables_initializer()
sess.run(init)

# 7 评估精度
prediction = tf.round(tf.nn.sigmoid(final_output))
predictions_correct = tf.cast(tf.equal(prediction, y_target), tf.float32)
accuracy = tf.reduce_mean(predictions_correct)

# 8 迭代训练模型
# Initialize loss and accuracy vectors
loss_vec = []
train_acc = []
test_acc = []
for i in range(1500):
    # Select random indicies for batch selection
    rand_index = np.random.choice(len(x_vals_train), size=batch_size)
    # Select batch
    rand_x = x_vals_train[rand_index]
    rand_y = np.transpose([y_vals_train[rand_index]])
    # Run training step
    sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})
    # Get training loss
    temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})
    loss_vec.append(temp_loss)
    # Get training accuracy
    temp_acc_train = sess.run(accuracy, feed_dict={x_data: x_vals_train, y_target: np.transpose([y_vals_train])})
    train_acc.append(temp_acc_train)
    # Get test accuracy
    temp_acc_test = sess.run(accuracy, feed_dict={x_data: x_vals_test, y_target: np.transpose([y_vals_test])})
    test_acc.append(temp_acc_test)
    if (i + 1) % 150 == 0:
        print('Loss = ' + str(temp_loss))

# 9 绘图
# 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()
# Plot train and test accuracy
plt.plot(train_acc, 'k-', label='Train Set Accuracy')
plt.plot(test_acc, 'r--', label='Test Set Accuracy')
plt.title('Train and Test Accuracy')
plt.xlabel('Generation')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')
plt.show()

执行结果：

C:\Anaconda3\python.exe "C:\Program Files\JetBrains\PyCharm 2019.1.1\helpers\pydev\pydevconsole.py" --mode=client --port=50184
import sys; print('Python %s on %s' % (sys.version, sys.platform))
sys.path.extend(['C:\\app\\PycharmProjects', 'C:/app/PycharmProjects'])
Python 3.7.6 (default, Jan  8 2020, 20:23:39) [MSC v.1916 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information
IPython 7.12.0 -- An enhanced Interactive Python. Type '?' for help.
PyDev console: using IPython 7.12.0
Python 3.7.6 (default, Jan  8 2020, 20:23:39) [MSC v.1916 64 bit (AMD64)] on win32
runfile('C:/app/PycharmProjects/ArtificialIntelligence/classification.py', wdir='C:/app/PycharmProjects/ArtificialIntelligence')
WARNING:tensorflow:From C:\Anaconda3\lib\site-packages\tensorflow\python\compat\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
2020-06-16 18:03:42.213313: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2020-06-16 18:03:42.230418: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x24a2a2387e0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-06-16 18:03:42.232488: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
Loss = 0.5170202
Loss = 0.53559077
Loss = 0.51184845
Loss = 0.5487095
Loss = 0.48175952
Loss = 0.5629245
Loss = 0.469246
Loss = 0.586653
Loss = 0.5067203
Loss = 0.51572895

 

 

这一个仍然是全连接，只是只有两层隐藏层，节点数也减少了。




 posted on 2020-05-20 15:25  大码王  阅读(1958)  评论(0)  编辑  收藏  举报
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