Jupyter Notebook, TensorFlow, Keras 笔记

import time
for i in range(10):
    time.sleep(0.3)
    print('\r任务进度: {0}%'.format(i*10+10), end='')
    sys.stdout.flush()



# >>> 任务进度: 70%  (会不断输出更新到这一行)

%load_ext autoreload
%autoreload 2

import webbrowser
webbrowser.register('firefox', None, webbrowser.GenericBrowser('C:/Program Files/Mozilla Firefox/firefox.exe'))
c.NotebookApp.browser = 'firefox'

%%html
<style>
.output_text {font-size: 12px; }
.output_text pre {line-height: 12px; font-family: "Monaco";}

.text_cell_render pre {line-height: 11px;}
.rendered_html pre {line-height: 11px; }
.text_cell_render pre code {font-size: 12px;}
.rendered_html pre code {font-size: 12px;}

.output_text pre, .text_cell_render pre {padding: 0.5ex;}
.container {width:100% !important;}
.prompt {min-width: 10ex;}
</style>

import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'

# multiple plot 方法一 
ax = plt.subplot(2, 1, 2)
plt.plot(hist.history['acc'], '-o')
plt.plot(hist.history['val_acc'], '-o')
plt.legend(['train', 'val'], loc='upper left')
plt.xlabel('epoch')
plt.ylabel('accuracy')
ax.set_ylim([0, 1])  # 这里是 ax, not plt


# multiple plot 方法二 to grid m*n
#　适合输出大量的小图片
f, plots = plt.subplots(21, 10, sharex='all', sharey='all', figsize=(10, 21))
for i in range(203):
    plots[i // 10, i % 10].axis('off')
    plots[i // 10, i % 10].imshow(pat[i], cmap=plt.cm.bone)


# multiple plot 方法三 span plot
# 将框架视为表格, subplot 可以跨行跨列, 并且 index 从 0 开始计数, 更符合直觉...
ax4 = plt.subplot2grid((2, 3), (1, 0), colspan=2)

import numpy as np
import pandas as pd

pd.set_option('display.width', 200)   # 每行最大字符
pd.set_option('precision', 3)         # 显示数字精度


import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline

plt.rcParams['axes.unicode_minus'] = False     # 显示数字负号
plt.rcParams['font.sans-serif'] = ['SimHei']   # 显示中文字体

mpl.rcParams['figure.figsize'] = 10, 6
mpl.rcParams['figure.dpi'] = 80
mpl.rcParams['savefig.dpi'] = 100
mpl.rcParams['font.size'] = 12
mpl.rcParams['legend.fontsize'] = 'large'
mpl.rcParams['figure.titlesize'] = 'medium'

plt.style.use('seaborn-whitegrid')

import seaborn as sns
sns.set(style="white")

def get_subplot_ax(position):
    ''' 注意 grid, index, span 都是先 行 后 列'''
    position = position.split(',')
    if len(position) == 3:
        grid, position, span = position
    elif len(position) == 2:
        grid, position = position
        span = '1x1'
    elif len(position) == 1:
        grid = position[0]
        position = span = '1x1'
    to_tuple = lambda s: tuple(int(n) for n in s.split('x'))
    rowspan, colspan = to_tuple(span)
    ax = plt.subplot2grid(to_tuple(grid), to_tuple(position), colspan=colspan, rowspan=rowspan)
    return ax

f, (ax1, ax2) = plt.subplots(2, 1, sharey='col')
# sharey='col' 当 subplots 处在同一列时, 共享 y 轴
# sharey='row' 当 subplots 处在同一行时, 共享 y 轴

import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
plt.style.use('ggplot')

data = np.random.randn(50)

print(plt.style.available)

# => ['seaborn-paper', 'seaborn-poster', 'seaborn-darkgrid', 'fast', 'ggplot', 'seaborn-pastel', 'bmh', 'seaborn-deep', 'fivethirtyeight', 'grayscale', 'seaborn', 'seaborn-talk', 'seaborn-bright', 'seaborn-white', 'seaborn-ticks', 'seaborn-dark-palette', 'seaborn-dark', '_classic_test', 'seaborn-colorblind', 'dark_background', 'seaborn-whitegrid', 'seaborn-notebook', 'Solarize_Light2', 'seaborn-muted', 'classic']

sns.palplot(sns.color_palette("Set2", 9))
sns.palplot(sns.cubehelix_palette(8))
sns.palplot(sns.color_palette("coolwarm", 12))
sns.palplot(sns.color_palette("hls", 6))
sns.palplot(sns.color_palette("husl", 6))

import seaborn as sns
import itertools

palette = sns.color_palette(sns.color_palette("Set2", 12))
palette = itertools.cycle(palette)

# 之后在需要使用颜色的地方:
ax.plot(seq, '.-', color=next(palette))

import matplotlib
for name, hex in matplotlib.colors.cnames.items():
    print(name, hex)

pylab.rcParams['figure.figsize'] = 8, 24

import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.colors as colors
import math

fig = plt.figure()
ax = fig.add_subplot(111)

ratio = 1.0 / 3.0
count = math.ceil(math.sqrt(len(colors.cnames)))
x_count = count * ratio
y_count = count / ratio
x = 0
y = 0
w = 1 / x_count
h = 1 / y_count

for c in colors.cnames:
    pos = (x / x_count, y / y_count)
    ax.add_patch(patches.Rectangle(pos, w, h, color=c))
    ax.annotate(c, xy=pos)
    if y >= y_count-1:
        x += 1
        y = 0
    else:
        y += 1
plt.show()

import keras
keras.__version__

# >>> '2.0.8'

from keras.utils.visualize_util import plot
plot(model, show_shapes=True, to_file='model.png')

# 在 notebook 中显示 jpg / png
from IPython.display import display, Image
display(Image('model.png', width=500))

# 在 notebook 中显示 svg
from IPython.display import SVG
from keras.utils.visualize_util import model_to_dot
SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))