Raspberry Pi和Python OpenCV人工神经网络和卷积神经网络演示及其机器学习微型框架

首先，主要讨论和演示机器学习中使用的基本数据模型及其演示，其次开始的深度学习讨论，然后，探讨 ANN 和 CNN 如何预测结果，例如，当呈现未知图像时，CNN 将尝试将其识别为属于它已被训练识别的类别之一。

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn import decomposition
from sklearn.preprocessing import scale
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
df = pd.read_csv('iris.csv', header=None, sep=',')
df.columns=['sepal_length', 'sepal_width', 'petal_length',
'petal_width', 'class']
df.dropna(how="all", inplace=True) # Drops empty line at EOF
# Show the first 5 records
print(df.head())
f, ax = plt.subplots(1, 4, figsize=(10,5))
vis1 = sns.distplot(df['sepal_length'],bins=10, ax= ax[0])
vis2 = sns.distplot(df['sepal_width'],bins=10, ax=ax[1])
vis3 = sns.distplot(df['petal_length'],bins=10, ax= ax[2])
vis4 = sns.distplot(df['petal_width'],bins=10, ax=ax[3])
plt.show()
# split data table into data X and class labels y
X = df.ix[:,0:4].values
y = df.ix[:,4].values
# Standardize the data
X_std = StandardScaler().fit_transform(X)
# Compute the covariance matrix
mean_vec = np.mean(X_std, axis=0)
cov_mat = (X_std -mean_vec).T.dot(X_std - mean_vec) /
(X_std.shape[0] - 1)
print('Covariance matrix n%s' %cov_mat)
# Compute the Eigenvectors and Eigenvalues
cov_mat = np.cov(X_std.T)
eig_vals, eig_vecs = np.linalg.eig(cov_mat)
print('Eigenvectors n%s' %eig_vecs)
print('Eigenvalues n%s' %eig_vals)
eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in
range(len(eig_vals))]
eig_pairs.sort()
eig_pairs.reverse()
print('Eigenvalues in descending order:')
for i in eig_pairs:
 print(i[0])
# Compute the Eigenvalue ratios
tot = sum(eig_vals)
var_exp = [(i / tot)*100 for i in sorted(eig_vals,
reverse=True)]
cum_var_exp = np.cumsum(var_exp)
print('Eigenvalue ratios:%s' %cum_var_exp)
#Create the W matrix
matrix_w = np.hstack((eig_pairs[0][1].reshape(4,1),
 eig_pairs[1][1].reshape(4,1)))
print('Matrix W:n', matrix_w)
# Transform the X_std dataset to the sub-space Y
Y = X_std.dot(matrix_w)
features = ['sepal_length', 'sepal_width', 'petal_length',
'petal_width']
# Create a scatter plot for PC1 vs PC2
x = df.loc[:,features].values
x = StandardScaler().fit_transform(x)
pca = PCA(n_components=2)
principalComponents = pca.fit_transform(x)
principalDf = pd.DataFrame(data=principalComponents,
columns=['principal component 1','principal component 2'])
finalDf = pd.concat([principalDf, df[['class']]], axis=1)
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(1,1,1)
ax.set_xlabel('Principal Component 1', fontsize=15)
ax.set_ylabel('Principal Component 2', fontsize=15)
ax.set_title('2 Component PCA', fontsize=20)
targets = ['setosa', 'versicolor', 'virginica']
colors = ['r', 'g', 'b']
for target, color in zip(targets, colors):
 indicesToKeep = finalDf['class'] == target
 ax.scatter(finalDf.loc[indicesToKeep, 'principal component
1'], finalDf.loc[indicesToKeep, 'principal component 2'],
c=color, s=50)
ax.legend(targets)
ax.grid
plt.show()