markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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Let's run a tournament, playing each plan against every other, and returning a list of `[(plan, mean_game_points),...]`. I will also define `show` to pretty-print these results and display a histogram: | def tournament(plans):
"Play each plan against each other; return a sorted list of [(plan: mean_points)]"
rankdict = {A: mean_points(A, plans) for A in plans}
return Counter(rankdict).most_common()
def mean_points(A, opponents):
"Mean points for A playing against all opponents (but not against itself)... | Top 10 of 1202 plans:
( 0, 3, 4, 7, 16, 24, 4, 34, 4, 4) 85.6%
( 5, 7, 9, 11, 15, 21, 25, 2, 2, 3) 84.1%
( 3, 5, 8, 10, 13, 1, 26, 30, 2, 2) 83.3%
( 2, 2, 6, 12, 2, 18, 24, 30, 2, 2) 83.3%
( 2, 8, 2, 2, 10, 18, 26, 26, 3, 3) 83.2%
( 3, 6, 7, 9, 11, 2, 27, 31, 2, 2) 83.2%
( 1, 1, ... | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
It looks like there are a few really bad plans in there. Let's just keep the top 1000 plans (out of 1202), and re-run the rankings: | plans = {A for (A, _) in rankings[:1000]}
rankings = tournament(plans)
show(rankings) | Top 10 of 1000 plans:
( 0, 3, 4, 7, 16, 24, 4, 34, 4, 4) 87.4%
( 5, 5, 5, 5, 5, 5, 27, 30, 6, 7) 84.8%
( 5, 5, 5, 5, 5, 5, 30, 30, 5, 5) 84.2%
( 3, 3, 5, 5, 7, 7, 30, 30, 5, 5) 84.1%
( 1, 2, 3, 4, 6, 16, 25, 33, 4, 6) 82.5%
( 2, 2, 2, 5, 5, 26, 26, 26, 3, 3) 82.4%
( 1, 1, ... | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
The top 10 plans are still winning over 80%, and the top plan remains `(0, 3, 4, 7, 16, 24, 4, 34, 4, 4)`. This is an interesting plan: it places most of the soldiers on castles 4+5+6+8, which totals only 23 points, so it needs to pick up 5 more points from the other castles (that have mostly 4 soldiers attacking each ... | def plotter(plans, X=range(41)):
X = list(X)
def mean_reward(c, s): return mean(reward(s, p[c], c+1) for p in plans)
for c in range(10):
plt.plot(X, [mean_reward(c, s) for s in X], '.-')
plt.xlabel('Number of soldiers (on each of the ten castles)')
plt.ylabel('Expected points won')
plt.g... | _____no_output_____ | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
For example, this says that for castle 10 (the orange line at top), there is a big gain in expected return as we increase from 0 to 4 soldiers, and after that the gains are relatively less steep. This plot is interesting, but I can't see how to directly read off a best plan from it. HillclimbingInstead I'll see if I ca... | def hillclimb(A, plans=plans, steps=1000):
"Try to improve Plan A, repeat `steps` times; return new plan and total."
m = mean_points(A, plans)
for _ in range(steps):
B = mutate(A)
m, A = max((m, A),
(mean_points(B, plans), B))
return A, m
def mutate(plan):
"Retur... | _____no_output_____ | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
Let's see how well this works. Remember, the best plan so far had a score of `87.4%`. Can we improve on that? | hillclimb((0, 3, 4, 7, 16, 24, 4, 34, 4, 4)) | _____no_output_____ | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
We got an improvement. Let's see what happens if we start with other plans: | hillclimb((10, 10, 10, 10, 10, 10, 10, 10, 10, 10))
hillclimb((0, 1, 2, 3, 4, 18, 18, 18, 18, 18))
hillclimb((2, 3, 5, 5, 5, 20, 20, 20, 10, 10))
hillclimb((0, 0, 5, 5, 25, 3, 25, 3, 31, 3)) | _____no_output_____ | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
What if we hillclimb 20 times longer? | hillclimb((0, 3, 4, 7, 16, 24, 4, 34, 4, 4), steps=20000) | _____no_output_____ | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
Opponent modellingTo have a chance of winning the second round of this contest, we have to predict what the other entries will be like. Nobody knows for sure, but I can hypothesize that the entries will be slightly better than the first round, and try to approximate that by hillclimbing from each of the first-round pl... | def hillclimbers(plans, steps=100):
"Return a sorted list of [(improved_plan, mean_points), ...]"
pairs = {hillclimb(plan, plans, steps) for plan in plans}
return sorted(pairs, key=lambda pair: pair[1], reverse=True)
# For example:
hillclimbers({(26, 5, 5, 5, 6, 7, 26, 0, 0, 0),
(25, 0, 0, 0,... | _____no_output_____ | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
I will define `plans2` (and `rankings2`) to be my estimate of the entries for round 2: | %time rankings2 = hillclimbers(plans)
plans2 = {A for (A, _) in rankings2}
show(rankings2) | CPU times: user 6min 11s, sys: 3.21 s, total: 6min 14s
Wall time: 6min 17s
Top 10 of 1000 plans:
( 1, 4, 5, 15, 6, 21, 3, 31, 3, 11) 90.8%
( 0, 3, 5, 14, 7, 21, 3, 30, 4, 13) 90.6%
( 0, 4, 6, 15, 9, 21, 4, 31, 5, 5) 90.2%
( 2, 4, 3, 13, 5, 22, 3, 32, 4, 12) 90.1%
( 0, 3, 5, 15, 8, 21, 4, 32... | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
Even though we only took 100 steps, the `plans2` plans are greatly improved: Almost all of them defeat 75% or more of the first-round `plans`. The top 10 plans are all very similar, targeting castles 4+6+8+10 (for 28 points), but reserving 20 or soldiers to spread among the other castles. Let's look more carefully at ... | for (p, m) in rankings2[::40] + [rankings2[-1]]:
print(pplan(p), pct(m)) | ( 1, 4, 5, 15, 6, 21, 3, 31, 3, 11) 90.8%
( 0, 6, 3, 13, 3, 22, 2, 32, 4, 15) 88.9%
( 1, 3, 6, 13, 9, 22, 1, 30, 4, 11) 88.3%
( 2, 2, 1, 13, 3, 21, 2, 32, 3, 21) 87.9%
( 0, 2, 5, 5, 15, 2, 28, 31, 5, 7) 87.6%
( 2, 2, 4, 14, 9, 1, 27, 30, 6, 5) 87.3%
( 3, 2, 3, 12, 3, 28, 3, 32,... | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
We see a wider variety in plans as we go farther down the rankings. Now for the plot: | plotter(plans2) | _____no_output_____ | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
We see that many castles (e.g. 9 (green), 8 (blue), 7 (black), 6 (yellowish)) have two plateaus. Castle 7 (black) has a plateau at 3.5 points for 6 to 20 soldiers (suggesting that 6 soldiers is a good investment and 20 soldiers a bad investment), and then another plateau at 7 points for everything above 30 soldiers.Now... | %time rankings3 = hillclimbers(plans2)
show(rankings3) | CPU times: user 5min 40s, sys: 1 s, total: 5min 41s
Wall time: 5min 42s
Top 10 of 1000 plans:
( 3, 8, 10, 18, 21, 3, 5, 6, 10, 16) 99.9%
( 1, 9, 10, 17, 21, 6, 4, 6, 9, 17) 99.9%
( 1, 8, 10, 18, 21, 4, 4, 6, 11, 17) 99.9%
( 0, 10, 10, 17, 20, 4, 5, 6, 7, 21) 99.9%
( 2, 11, 1, 16, 18, 7, 6, 6, ... | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
We can try even harder to improve the champ: | champ, _ = rankings3[0]
hillclimb(champ, plans2, 10000) | _____no_output_____ | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
Here are some champion plans from previous runs of this notebook: | champs = {
(0, 1, 3, 16, 20, 3, 4, 5, 32, 16),
(0, 1, 9, 16, 15, 24, 5, 5, 8, 17),
(0, 1, 9, 16, 16, 24, 5, 5, 7, 17),
(0, 2, 9, 16, 15, 24, 5, 5, 8, 16),
(0, 2, 9, 16, 15, 25, 5, 4, 7, 17),
(0, 3, 4, 7, 16, 24, 4, 34, 4, 4),
(0, 3, 5, 6, 20, 4, 4, 33, 8, 17),
(0, 4, 5, 7, 20, 4, 4, 33, 7, 16),
(0, 4, 6, 7, 19... | _____no_output_____ | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
We can evaluate each of them against the original `plans`, against the improved `plans2`, against their fellow champs, and against all of those put together: | def μ(plan, plans): return pct(mean_points(plan,plans))
all = plans | plans2 | champs
print('Plan plans plans2 champs all')
for p in sorted(champs, key=lambda p: -mean_points(p, all)):
print(pplan(p), μ(p, plans), μ(p, plans2), μ(p, champs), μ(p, all)) | Plan plans plans2 champs all
( 0, 5, 7, 3, 18, 4, 4, 34, 8, 17) 85.5% 96.0% 68.5% 90.2%
( 0, 4, 6, 7, 19, 4, 4, 31, 8, 17) 84.7% 95.0% 63.0% 89.2%
( 0, 1, 3, 16, 20, 3, 4, 5, 32, 16) 85.6% 95.2% 31.5% 89.0%
( 0, 3, 5, 6, 20, 4, 4, 33, 8, 17) 84.1... | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
Individual EDA- Separate the states into 4 regions: Western, southern, eastern and northern.- Filter data based on assigned regions and explore with support from visualization- North East and South is the main focus in this EDA.___ Data Filtering | import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
# Add scripts module's directory to sys.path
import sys, os
sys.path.append(os.path.join(os.getcwd(),".."))
from scripts import project_functions as pf
# Load 4 parts of raw data on State Names
state_df = pf.load_and_process_m... | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
___ Initial inspectionLet's have a general of the data set for each region. North East region | n_df.head(10)
n_df.shape | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
For the North East, we see that there are **more than 1 million collected record** and **5 variable for each observation**. | n_df.columns | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
Indeed, we have 5 variables for each observation. **The state column is not important since we care only about regions.** | n_df.info() | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 1077888 entries, 0 to 1077887
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Name 1077888 non-null object
1 Year 1077888 non-null int64
2 Gender 1077888 non-null object
3 State 10778... | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
We see Year and Count are 64-bit integers type while other columns are categorial types. | n_df.describe(include=[object]).T | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
For categorial data:- We see that there are 3 categorical variable in the dataframe with other 2 numerical variable (Year and Count)- Here, we can see that are 15817 unique names in this region- There are 11 states recorded that equal to total number of states in this region. This means all states participates in this ... | n_df.describe().T | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
Summary on numerical values do not give any useful information. | n_df["Year"].unique() | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
The data set span from 1910 to 2014 without any missing years. | len(n_df.loc[n_df["Count"]<=0]) | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
This shows that we do not have negative values for names'count. South region | s_df.head(10)
s_df.shape | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
For the South, we see that there are **more than 2 million collected record** and **5 variable for each observation**. | # We have 5 variables for each observation
s_df.columns | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
This is similar to that of North East region. | s_df.info() | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 2173021 entries, 0 to 2173020
Data columns (total 5 columns):
# Column Dtype
--- ------ -----
0 Name object
1 Year int64
2 Gender object
3 State object
4 Count int64
dtypes: int64(2), object(3)
memory usage: 82.9+ MB
| MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
The type of each column is also similar to that of North East dataset. | s_df.describe(include=[object]).T | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
For categorial data:- We see that there are 3 categorical variable in the dataframe with other 2 numerical variable (Year and Count)- Here, we can see that are 20860 unique names in this region- There are 17 states recorded that equal to total number of states in this region. This means all states participates in this ... | s_df.describe().T | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
Summary on numerical values do not give any useful information. | s_df.loc[s_df["Count"]<=0] | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
This shows that we do not have negative values for names'count. | s_df["Year"].unique() | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
The data set also spans from 1910 to 2014 without gaps! ___ Analysis Top 5 of all times in South and North We start by aggregating sum of counts of every name in each region for all years. | # Define processing function
def get_top5_all_time(data=None):
if data is None:
return data
return (data.groupby(by="Name")
.aggregate("sum")
.drop(columns=["Year"]) # We do not analyze with time
.reset_index()
.sort_values(by="Count", ascending=Fa... | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
The code works properly to return top 5 all times in these two regions. Now, we can build plots. In this case, for counting the number of occurence for each discrete entry, bar plots is ideal. | fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12,7), sharex=True)
# Check similarity between 2 regions
sns.set_theme(context="notebook", style="ticks", font_scale=1.3)
def get_top5_all_time(data, ax, region):
plot = sns.barplot(y="Name",
x="Count",
data=data,
or... | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
Observations- We can see that top 5 in these 2 regions are quite similar with the appearance of **James, William, Robert and John**. The difference is that **Michael** is in top 5 in the North East while **Mary** is in the top 5 in the South.- All names in top 5 list in both region pass the mark of **1 million** coun... | # Function for filter data based on gender
def get_top5_gender(data, region, gender):
return (data.loc[data["Gender"] == gender]
.groupby(by="Name")
.agg(np.sum)
.sort_values(by="Count", ascending=False)
.head()
.dro... | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
Now, we can plot. In this case, we will will bar plot to indicate counts and FacetGrid as way to categorize plot based on region and gender. | # Settings
sns.set_theme(style="ticks", font_scale=1.3)
fig,ax= plt.subplots(1,2, figsize=(12,7), sharex=True)
def draw_gender_plot(axes, data_list, result_axes=None):
if result_axes is None:
result_axes = list()
for i in range(len(ax)):
data = data_list[i]
ax_ij = sns.barplot(x="Count"... | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
Observation- Interestingly, two regions have the same names in the top 5 male names all times. This might result from the fact that these two regions are close to each other.- However, the pattern is different. In the **North East**, **John is most occuring name** with over 1.5 million counts. In the **South**, **Jame... | fig, ax = plt.subplots(1,2, figsize=(12,7), sharex=True)
draw_gender_plot(ax,[top5_female_n,top5_female_s])
# Configure figure object
sns.despine()
fig.tight_layout(pad=2.7)
fig.suptitle("Top 5 female names of all times in North East and South Region")
plt.show() | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
Observations- Even more interesting, the top 5 female names see Mary at top for both region. **Mary's count is almost double that of other names in the list.**- The two list seems similar with the appearance of Mary, Patricia, Elizabeth. Unlike male top 5, **this list differs by 2 names between two region**. In the N... | def get_proportion_df(data, region):
p_df = (data.pivot_table(index="Year",
columns="Name",
values="Count",
aggfunc="sum")
.fillna(0)
)
y = data.groupby(by="Year").sum()
for year in range(1910... | _____no_output_____ | MIT | analysis/Jamie/milestones2_EDA.ipynb | data301-2020-winter2/course-project-group_1039 |
PyTorch Training and Serving in SageMaker "Script Mode"Script mode is a training script format for PyTorch that lets you execute any PyTorch training script in SageMaker with minimal modification. The [SageMaker Python SDK](https://github.com/aws/sagemaker-python-sdk) handles transferring your script to a SageMaker tr... | !pip install sagemaker --upgrade --ignore-installed --no-cache --user
!pip install torch==1.3.1 torchvision==0.4.2 --upgrade --ignore-installed --no-cache --user | _____no_output_____ | Apache-2.0 | 07_train/archive/extras/pytorch/pytorch_mnist.ipynb | MarcusFra/workshop |
Forcing `pillow==6.2.1` due to https://discuss.pytorch.org/t/cannot-import-name-pillow-version-from-pil/66096 | !pip uninstall -y pillow
!pip install pillow==6.2.1 --upgrade --ignore-installed --no-cache --user | _____no_output_____ | Apache-2.0 | 07_train/archive/extras/pytorch/pytorch_mnist.ipynb | MarcusFra/workshop |
Restart the Kernel to Recognize New Dependencies Above | from IPython.display import display_html
display_html("<script>Jupyter.notebook.kernel.restart()</script>", raw=True)
!pip3 list | _____no_output_____ | Apache-2.0 | 07_train/archive/extras/pytorch/pytorch_mnist.ipynb | MarcusFra/workshop |
Create the SageMaker Session | import os
import sagemaker
from sagemaker import get_execution_role
sagemaker_session = sagemaker.Session() | _____no_output_____ | Apache-2.0 | 07_train/archive/extras/pytorch/pytorch_mnist.ipynb | MarcusFra/workshop |
Setup the Service Execution Role and RegionGet IAM role arn used to give training and hosting access to your data. See the documentation for how to create these. Note, if more than one role is required for notebook instances, training, and/or hosting, please replace the `sagemaker.get_execution_role()` with a the ap... | role = get_execution_role()
print('RoleARN: {}\n'.format(role))
region = sagemaker_session.boto_session.region_name
print('Region: {}'.format(region)) | _____no_output_____ | Apache-2.0 | 07_train/archive/extras/pytorch/pytorch_mnist.ipynb | MarcusFra/workshop |
Training Data Copy the Training Data to Your Notebook Disk | local_data_path = './data'
from torchvision import datasets, transforms
normalization_mean = 0.1307
normalization_std = 0.3081
# download the dataset
# this will not only download data to ./mnist folder, but also load and transform (normalize) them
datasets.MNIST(local_data_path, download=True, transform=transforms.C... | _____no_output_____ | Apache-2.0 | 07_train/archive/extras/pytorch/pytorch_mnist.ipynb | MarcusFra/workshop |
Upload the Data to S3 for Distributed Training Across Many WorkersWe are going to use the `sagemaker.Session.upload_data` function to upload our datasets to an S3 location. The return value inputs identifies the location -- we will use later when we start the training job.This is S3 bucket and prefix that you want to ... | bucket = sagemaker_session.default_bucket()
data_prefix = 'sagemaker/pytorch-mnist/data'
training_data_uri = sagemaker_session.upload_data(path=local_data_path, bucket=bucket, key_prefix=data_prefix)
print('Input spec (S3 path): {}'.format(training_data_uri))
!aws s3 ls --recursive {training_data_uri} | _____no_output_____ | Apache-2.0 | 07_train/archive/extras/pytorch/pytorch_mnist.ipynb | MarcusFra/workshop |
Train Training ScriptThe `mnist_pytorch.py` script provides all the code we need for training and hosting a SageMaker model (`model_fn` function to load a model).The training script is very similar to a training script you might run outside of SageMaker, but you can access useful properties about the training environm... | !ls ./src/mnist_pytorch.py | _____no_output_____ | Apache-2.0 | 07_train/archive/extras/pytorch/pytorch_mnist.ipynb | MarcusFra/workshop |
You can add custom Python modules to the `src/requirements.txt` file. They will automatically be installed - and made available to your training script. | !cat ./src/requirements.txt | _____no_output_____ | Apache-2.0 | 07_train/archive/extras/pytorch/pytorch_mnist.ipynb | MarcusFra/workshop |
Train with SageMaker `PyTorch` EstimatorThe `PyTorch` class allows us to run our training function as a training job on SageMaker infrastructure. We need to configure it with our training script, an IAM role, the number of training instances, the training instance type, and hyperparameters. In this case we are going... | from sagemaker.pytorch import PyTorch
import time
model_output_path = 's3://{}/sagemaker/pytorch-mnist/training-runs'.format(bucket)
mnist_estimator = PyTorch(
entry_point='mnist_pytorch.py',
source_dir='./src',
output_path=model_output_path,
rol... | _____no_output_____ | Apache-2.0 | 07_train/archive/extras/pytorch/pytorch_mnist.ipynb | MarcusFra/workshop |
Attach to a training job to monitor the logs._Note: Each instance in the training job (2 in this example) will appear as a different color in the logs. 1 color per instance._ | mnist_estimator = PyTorch.attach(training_job_name=training_job_name) | _____no_output_____ | Apache-2.0 | 07_train/archive/extras/pytorch/pytorch_mnist.ipynb | MarcusFra/workshop |
Option 1: Perform Batch Predictions Directly in the Notebook Use PyTorch Core to load the model from `model_output_path` | !aws --region {region} s3 ls --recursive {model_output_path}/{training_job_name}/output/
!aws --region {region} s3 cp {model_output_path}/{training_job_name}/output/model.tar.gz ./model/model.tar.gz
!ls ./model
!tar -xzvf ./model/model.tar.gz -C ./model
# Based on https://github.com/pytorch/examples/blob/master/mnist/m... | _____no_output_____ | Apache-2.0 | 07_train/archive/extras/pytorch/pytorch_mnist.ipynb | MarcusFra/workshop |
Option 2: Create a SageMaker Endpoint and Perform REST-based Predictions Deploy the Trained Model to a SageMaker Endpoint (Approx. 10 mins)After training, we use the `PyTorch` estimator object to build and deploy a `PyTorchPredictor`. This creates a Sagemaker Endpoint -- a hosted prediction service that we can use t... | predictor = mnist_estimator.deploy(initial_instance_count=1, instance_type='ml.c5.2xlarge') | _____no_output_____ | Apache-2.0 | 07_train/archive/extras/pytorch/pytorch_mnist.ipynb | MarcusFra/workshop |
Invoke the EndpointWe can now use this predictor to classify hand-written digits. Drawing into the image box loads the pixel data into a `data` variable in this notebook, which we can then pass to the `predictor`. | from IPython.display import HTML
HTML(open("input.html").read()) | _____no_output_____ | Apache-2.0 | 07_train/archive/extras/pytorch/pytorch_mnist.ipynb | MarcusFra/workshop |
The value of `data` is retrieved from the HTML above. | print(data)
import numpy as np
image = np.array([data], dtype=np.float32)
response = predictor.predict(image)
prediction = response.argmax(axis=1)[0]
print(prediction) | _____no_output_____ | Apache-2.0 | 07_train/archive/extras/pytorch/pytorch_mnist.ipynb | MarcusFra/workshop |
(Optional) Cleanup EndpointAfter you have finished with this example, remember to delete the prediction endpoint to release the instance(s) associated with it | sagemaker.Session().delete_endpoint(predictor.endpoint) | _____no_output_____ | Apache-2.0 | 07_train/archive/extras/pytorch/pytorch_mnist.ipynb | MarcusFra/workshop |
Train a Plane Detection Model from Voxel51 DatasetThis notebook trains a plane detection model using transfer learning. Depending on the label used, it can just detect a plane or it can try to detect the model of the plane.A pre-trained model is used as a starting point. This means that fewer example images are needed... | training_name="881images-efficientdet-d0-model" # The name for the model. All of the different directories will be based on this
label_field = "detections" # The field from the V51 Samples around which will be used for the Labels for training.
dataset_name = "jsm-test-dataset" # The name of the V51 dataset that will b... | Reading package lists... Done
Building dependency tree
Reading state information... Done
protobuf-compiler is already the newest version (3.0.0-9.1ubuntu1).
libgl1-mesa-glx is already the newest version (20.0.8-0ubuntu1~18.04.1).
wget is already the newest version (1.19.4-1ubuntu2.2).
0 upgraded, 0 newly install... | Apache-2.0 | ml-model/notebooks/Train Plane Detection Model.ipynb | wiseman/SkyScan |
Download and Install TF ModelsThe TF Object Detection API is available here: https://github.com/tensorflow/models | import os
import pathlib
# Clone the tensorflow models repository if it doesn't already exist
if "models" in pathlib.Path.cwd().parts:
while "models" in pathlib.Path.cwd().parts:
os.chdir('..')
elif not pathlib.Path('models').exists():
# pull v2.5.0 of tensorflow models to make deterministic
!git clone --... | _____no_output_____ | Apache-2.0 | ml-model/notebooks/Train Plane Detection Model.ipynb | wiseman/SkyScan |
Export the Training and Val Dataset from Voxel 51 | import fiftyone as fo
import math
dataset = fo.load_dataset(dataset_name)
| _____no_output_____ | Apache-2.0 | ml-model/notebooks/Train Plane Detection Model.ipynb | wiseman/SkyScan |
Explore the dataset contentHere are some basic stats on the Voxel51 dataset you are going to build training the model on. An example of the samples is also printed out. In the Sample, make sure the *label_field* you selected has some detections in it. | print("\t\tDataset\n-----------------------------------")
view = dataset.match_tags("training").shuffle(seed=51) # You can add additional things to the query to further refine it. eg .match_tags("good_box")
print(view)
print("\n\n\tExample Sample\n-----------------------------------")
print(view.first())
| Dataset
-----------------------------------
Dataset: jsm-test-dataset
Media type: image
Num samples: 881
Tags: ['capture-3-29', 'capture-3-30', 'capture-5-13', 'training']
Sample fields:
id: fiftyone.core.fields.ObjectIdField
filepath: fiftyone.core.fields.StringField... | Apache-2.0 | ml-model/notebooks/Train Plane Detection Model.ipynb | wiseman/SkyScan |
Export the dataset into TFRecordsThe selected dataset samples will be exported to TensorFlow Records (TFRecords). They will be split between Training and Validation. The ratio can be adjusted below. You only need to do this once to build the dataset. If you run this a second time with the same **model_name** additiona... | # The Dataset or DatasetView to export
sample_len = len(view)
val_len = math.floor(sample_len * 0.2)
train_len = math.floor(sample_len * 0.8)
print("Total: {} Val: {} Train: {}".format(sample_len,val_len,train_len))
val_view = view.take(val_len)
train_view = view.skip(val_len).take(train_len)
# Export the dataset
val_v... | Total: 881 Val: 176 Train: 704
100% |█████████████████| 176/176 [4.1s elapsed, 0s remaining, 52.9 samples/s]
100% |█████████████████| 704/704 [13.2s elapsed, 0s remaining, 54.4 samples/s]
| Apache-2.0 | ml-model/notebooks/Train Plane Detection Model.ipynb | wiseman/SkyScan |
Create a file with the Labels for the objectsThe TF2 Object Detection API looks for a map of the labels used and a corresponding Id. You can build a list of the unique classnames by itterating the dataset. You can also just hardcode it if there only a few. | def convert_classes(classes, start=1):
msg = StringIntLabelMap()
for id, name in enumerate(classes, start=start):
msg.item.append(StringIntLabelMapItem(id=id, name=name))
text = str(text_format.MessageToBytes(msg, as_utf8=True), 'utf-8')
return text
# If labelfield is a classification
class_nam... | item {
name: "plane"
id: 1
}
| Apache-2.0 | ml-model/notebooks/Train Plane Detection Model.ipynb | wiseman/SkyScan |
Download a pretrained Model & default ConfigA list of the models can be found here: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.mdThe configs are here: https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/configs/tf2/ | #download pretrained weights
%mkdir /tf/models/research/deploy/
%cd /tf/models/research/deploy/
import tarfile
download_tar = 'http://download.tensorflow.org/models/object_detection/tf2/20200711/' + pretrained_checkpoint
!wget {download_tar}
tar = tarfile.open(pretrained_checkpoint)
tar.extractall()
tar.close()
#downl... | /tf/models/research/deploy
--2021-07-08 20:52:50-- https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/configs/tf2/ssd_efficientdet_d0_512x512_coco17_tpu-8.config
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...
C... | Apache-2.0 | ml-model/notebooks/Train Plane Detection Model.ipynb | wiseman/SkyScan |
Build the Config for trainingThe default config for the model being trained needs to be updated with the correct parameters and paths to the data. This just adds some standard settings, you may need to do some additional tuning if the training is not working well. | # Gets the total number of classes from the Label Map
def get_num_classes(pbtxt_fname):
from object_detection.utils import label_map_util
label_map = label_map_util.load_labelmap(pbtxt_fname)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=90, use_display_nam... | working with 1 classes
| Apache-2.0 | ml-model/notebooks/Train Plane Detection Model.ipynb | wiseman/SkyScan |
You may need to adjust the learning rate section below. The number used here are from the EfficentDet config. I noticed that this learning rate worked well for the small bounding boxes I was using when planes were at a high altitude. You can try increasing it if the planes take up more of the image. If the initial loss... | # write custom configuration file by slotting our dataset, model checkpoint, and training parameters into the base pipeline file
import re
%cd /tf/models/research/deploy
print('writing custom configuration file')
with open(pipeline_fname) as f:
s = f.read()
with open('pipeline_file.config', 'w') as f:
#... | # SSD with EfficientNet-b0 + BiFPN feature extractor,
# shared box predictor and focal loss (a.k.a EfficientDet-d0).
# See EfficientDet, Tan et al, https://arxiv.org/abs/1911.09070
# See Lin et al, https://arxiv.org/abs/1708.02002
# Trained on COCO, initialized from an EfficientNet-b0 checkpoint.
#
# Train on TP... | Apache-2.0 | ml-model/notebooks/Train Plane Detection Model.ipynb | wiseman/SkyScan |
Train Custom TF2 Object DetectorThis step will launch the TF2 Object Detection training. It can take a while to start-up. If you get an error about not finding the GPU, try shutting down the Jupyter kernel and restarting it.While it is running, it should print out the Current Loss and which Step it is on.* pipeline_fi... | # 2:48 PM ET Tuesday, May 25, 2021
!python /tf/models/research/object_detection/model_main_tf2.py \
--pipeline_config_path={pipeline_file} \
--model_dir={model_dir} \
--alsologtostderr \
--num_train_steps={num_steps} \
--sample_1_of_n_eval_examples=1 \
--num_eval_steps={num_eval_steps} | 2021-07-08 20:53:56.154660: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
2021-07-08 20:53:59.234024: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1
2021-07-08 20:53:59.259096: I ten... | Apache-2.0 | ml-model/notebooks/Train Plane Detection Model.ipynb | wiseman/SkyScan |
Evaluate trained modelAfter the model has finished training, try running it against some data to see if it atleast works. |
import matplotlib
import matplotlib.pyplot as plt
import io, os, glob
import scipy.misc
import numpy as np
from six import BytesIO
from PIL import Image, ImageDraw, ImageFont
import tensorflow as tf
from object_detection.utils import label_map_util
from object_detection.utils import config_util
from object_detectio... | _____no_output_____ | Apache-2.0 | ml-model/notebooks/Train Plane Detection Model.ipynb | wiseman/SkyScan |
Load model from a training checkpointSelect a checkpoint index from above | # generally you want to put the last ckpt index from training in here
checkpoint_index=41
# recover our saved model
pipeline_config = pipeline_file
checkpoint = model_dir + "ckpt-" + str(checkpoint_index)
configs = config_util.get_configs_from_pipeline_file(pipeline_config)
model_config = configs['model']
detection_m... | _____no_output_____ | Apache-2.0 | ml-model/notebooks/Train Plane Detection Model.ipynb | wiseman/SkyScan |
Export the modelWhen you have a working model, use the TF2 Object Detection API to export it to a saved model. Export a Saved Model that uses Image Tensors | image_tensor_model_export_dir = model_export_dir + "image_tensor_saved_model"
print(image_tensor_model_export_dir)
!python /tf/models/research/object_detection/exporter_main_v2.py \
--input_type image_tensor \
--trained_checkpoint_dir={model_dir} \
--pipeline_config_path={pipeline_file} \
--output_direc... | 2021-06-28 23:00:37.233618: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
2021-06-28 23:00:39.839076: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1
2021-06-28 23:00:39.864436: I ten... | Apache-2.0 | ml-model/notebooks/Train Plane Detection Model.ipynb | wiseman/SkyScan |
Export a Saved Model that uses TF Examples | # Ignore for now - we do not need to use the TF Example approach.
#tf_example_model_export_dir = model_export_dir + "tf_example_saved_model"
#!python /tf/models/research/object_detection/exporter_main_v2.py \
# --input_type=tf_example \
# --trained_checkpoint_dir={model_dir} \
# --pipeline_config_path={pipeli... | _____no_output_____ | Apache-2.0 | ml-model/notebooks/Train Plane Detection Model.ipynb | wiseman/SkyScan |
Export a TFLite compatible modelRemeber that only Detection models that use SSDs are supported | !python /tf/models/research/object_detection/export_tflite_graph_tf2.py \
--pipeline_config_path={pipeline_file} \
--trained_checkpoint_dir={model_dir} \
--output_directory={model_export_dir}tflite-compatible
# I think we skip this step...
#! tflite_convert \
# --saved_model_dir="{model_export_dir}tflite-com... | _____no_output_____ | Apache-2.0 | ml-model/notebooks/Train Plane Detection Model.ipynb | wiseman/SkyScan |
Export a TensorJS compatible modelFrom: https://www.tensorflow.org/js/tutorials/conversion/import_saved_model | !pip install tensorflowjs
! tensorflowjs_converter \
--input_format=tf_saved_model \
{model_export_dir}image_tensor_saved_model/saved_model \
{model_export_dir}web_model
!saved_model_cli show --dir /tf/models/research/deploy/ssd_mobilenet_v2_320x320_coco17_tpu-8/saved_model --all | _____no_output_____ | Apache-2.0 | ml-model/notebooks/Train Plane Detection Model.ipynb | wiseman/SkyScan |
Python Homework 1 - The challengeTake the python challenge found on www.pythonchallenge.com/.You will copy this notebook. Rename it as:YOURLASTNAME-FIRSTINITIAL-python-challenge-xx-Sept-2017with your name replacing your last name and first initial and the xx replaced by the date you started or submitted.Do the first ... | #http://www.pythonchallenge.com/pc/def/0.html
print(2**38)
print(pow(2,38))
#http://www.pythonchallenge.com/pc/def/map.html | 274877906944
274877906944
| MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Python challenge question 2 I changed the URL to "http://www.pythonchallenge.com/pc/def/274877906944.html" which redirected to "http://www.pythonchallenge.com/pc/def/map.html". This challenge has a picture with letters on i and a text below it. It can be seen that the letters on the right are two characters after the ... | #http://www.pythonchallenge.com/pc/def/274877906944.html
#http://www.pythonchallenge.com/pc/def/map.html
import string
inp="abcdefghijklmnopqrstuvwxyz"
outp="cdefghijklmnopqrstuvwxyzab"
trans=str.maketrans(inp, outp)
strg = "g fmnc wms bgblr rpylqjyrc gr zw fylb. rfyrq ufyr amknsrcpq ypc dmp. bmgle gr gl zw fylb gq ... | i hope you didnt translate it by hand. thats what computers are for. doing it in by hand is inefficient and that's why this text is so long. using string.maketrans() is recommended. now apply on the url.
ocr
| MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Python challenge question 3 I then changed the URL to "http://www.pythonchallenge.com/pc/def/ocr.html". This challenge shows a picture of a book and it says to recognize the characters, giving a clue to check the page source. When I checked it, I found big block of characters in the page source which I thought should... | #http://www.pythonchallenge.com/pc/def/ocr.html
import urllib.request
url_ocr = urllib.request.urlopen("http://www.pythonchallenge.com/pc/def/ocr.html").read().decode()
#print(url_ocr)
import re
content = re.findall("<!--(.*?)-->", url_ocr, re.S)[-1] #findall() matches all occurrences of a pattern
... | ['e', 'q', 'u', 'a', 'l', 'i', 't', 'y']
| MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Python challenge question 4 Then, I changed the URL to "http://www.pythonchallenge.com/pc/def/equality.html" and there is text which says "One small letter, surrounded by EXACTLY three big bodyguards on each of its sides". I checked the page source to find any other clues and there is a big block of text, just as prev... | #http://www.pythonchallenge.com/pc/def/equality.html
import urllib.request
url_eq = urllib.request.urlopen("http://www.pythonchallenge.com/pc/def/equality.html").read().decode()
import re
data = re.findall("<!--(.*?)-->", url_eq, re.S)[-1] #findall() matches all occurrences of a pattern
... | ['l', 'i', 'n', 'k', 'e', 'd', 'l', 'i', 's', 't']
linkedlist
| MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Python challenge question 5 For the next challenge, I changed the URL to "http://www.pythonchallenge.com/pc/def/linkedlist.html" but it showed text "linkedlist.php". So, I changed the URL to "http://www.pythonchallenge.com/pc/def/linkedlist.php". When I checked the page source, it has "urllib may help. DON'T TRY ALL N... | #http://www.pythonchallenge.com/pc/def/linkedlist.php
import urllib
import re
url_ll = ("http://www.pythonchallenge.com/pc/def/linkedlist.php?nothing=%s")
num="12345"
#num=16044/2
while num!="":
data = urllib.request.urlopen(url_ll % num).read().decode()
#print(data)
num = "".join(re.findall("and the nex... | Came to an End
and the next nothing is 25357
and the next nothing is 89879
and the next nothing is 80119
and the next nothing is 50290
and the next nothing is 9297
and the next nothing is 30571
and the next nothing is 7414
and the next nothing is 30978
and the next nothing is 16408
and the next nothing is 80109
and the... | MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Python challenge question 6 For the next challenge, I changed the URL to "http://www.pythonchallenge.com/pc/def/peak.html" which showed a picture of a hill with the text “pronounce it”. When I checked the page source, it showed some text "peak hell sounds familiar ?" and a file named "banner.p" which again took me to ... | #http://www.pythonchallenge.com/pc/def/peak.html
import urllib.request
url_ban = urllib.request.urlopen("http://www.pythonchallenge.com/pc/def/banner.p")
import pickle
data = pickle.load(url_ban) #Reads a pickled object representation from the open file object given in the constructor, and return the reconstituted o... |
##### #####
#### ####
#### ... | MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Python challenge question 7 For the next challenge, I changed the URL to "http://www.pythonchallenge.com/pc/def/channel.html" and it showed a picture of a zipper and I felt it is something related to zip files. When I checked page source, it showed some text. I changed the URl to ".zip" and got a file with a lot of te... | #http://www.pythonchallenge.com/pc/def/channel.html
import urllib
import zipfile
import re
url_ll = "http://www.pythonchallenge.com/pc/def/channel.html"
zf = zipfile.ZipFile("channel.zip", 'r')
print(zf.read("readme.txt").decode())
num = "90052"
comments = ""
while num != "" :
data = zf.read(num + ".txt").decode... | welcome to my zipped list.
hint1: start from 90052
hint2: answer is inside the zip
Collect the comments.
****************************************************************
****************************************************************
** **
** OO OO X... | MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Python challenge question 8 For the next challenge, I tried URL "http://www.pythonchallenge.com/pc/def/hockey.html" but it gave a text saying "it's in the air. look at the letters". Then, I tried "http://www.pythonchallenge.com/pc/def/oxygen.html". It gave a picture in which the center of the picture was grey scaled f... | #http://www.pythonchallenge.com/pc/def/oxygen.html
import urllib.request
from PIL import Image
import requests
from io import BytesIO
url = "http://www.pythonchallenge.com/pc/def/oxygen.png"
img_oxy = requests.get(url)
img = Image.open(BytesIO(img_oxy.content))
width,height = img.size
print(width)
print(height)
#fo... | 629
95
smart guy, you made it. the next level is [105, 110, 116, 101, 103, 114, 105, 116, 121]pe_integrity
| MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Python challenge question 9 For the next challenge, I changed the URL to "http://www.pythonchallenge.com/pc/def/integrity.html" which showed a picture of a bee with text "Where is the missing link?". It seemed the bee is clickable and when clicked, it asked for a a userame and password.Also, when I checked page source... | #http://www.pythonchallenge.com/pc/def/integrity.html
import bz2
usr = b"BZh91AY&SYA\xaf\x82\r\x00\x00\x01\x01\x80\x02\xc0\x02\x00 \x00!\x9ah3M\x07<]\xc9\x14\xe1BA\x06\xbe\x084"
pwd = b"BZh91AY&SY\x94$|\x0e\x00\x00\x00\x81\x00\x03$ \x00!\x9ah3M\x13<]\xc9\x14\xe1BBP\x91\xf08"
print(bz2.BZ2Decompressor().decompress(us... | b'huge'
b'file'
| MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Python challenge question 10 For this challenge, I gave username and password previously obtained which took me to URL "http://www.pythonchallenge.com/pc/return/good.html". It has a picture of a stem with black dots. It seemed like we need to connect the dots to get the answer. Looking at page source, my intuition is ... | #http://www.pythonchallenge.com/pc/return/good.html
from PIL import Image, ImageDraw
first=[
146,399,163,403,170,393,169,391,166,386,170,381,170,371,170,355,169,346,167,335,170,329,170,320,170,
310,171,301,173,290,178,289,182,287,188,286,190,286,192,291,194,296,195,305,194,307,191,312,190,316,
190,321,192,331,193,338... | _____no_output_____ | MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Python challenge question 11 For the next challenge, I tried URL "http://www.pythonchallenge.com/pc/return/bull.html" and it showed a picture of a bull. In text below it says ‘len(a[30]) = ?’. When I clicked the bull, which is clickable, a new page shoed a sequence ‘a = [1, 11, 21, 1211, 111221,..]'. When I googled th... | #http://www.pythonchallenge.com/pc/return/bull.html
from itertools import groupby
def lookandsay(n):
return (''.join(str(len(list(g))) + k
for k,g in groupby(n)))
n='1'
for i in range(30):
print("Term", i,"--", n)
n = lookandsay(n)
type(n)
len(n)
#http://www.pythonchallenge.com/pc/retur... | Term 0 -- 1
Term 1 -- 11
Term 2 -- 21
Term 3 -- 1211
Term 4 -- 111221
Term 5 -- 312211
Term 6 -- 13112221
Term 7 -- 1113213211
Term 8 -- 31131211131221
Term 9 -- 13211311123113112211
Term 10 -- 11131221133112132113212221
Term 11 -- 3113112221232112111312211312113211
Term 12 -- 132113213211121312211231131122211311122113... | MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Python challenge question 12 For the next challenge, I tried URL "http://www.pythonchallenge.com/pc/return/5808.html" and it showed a blurry picture with page title 'odd even'. When I checked page source, there is nothing much except cave.jpg, which when clicked got to the same image. I tried searching '"cave in pytho... | #http://www.pythonchallenge.com/pc/return/5808.html
import urllib.request
from PIL import Image
from io import StringIO
#url = 'http://www.pythonchallenge.com/pc/return/cave.jpg'
#img_cav = urllib.request.urlopen(url).read()
#img = Image.open(StringIO.StringIO(img_cav))
im = Image.open('cave.jpg')
im.size
w, h = im.... | _____no_output_____ | MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Python challenge question 13 For the next challenge, I tried URL "http://www.pythonchallenge.com/pc/return/evil.html" and it showed a picture of a man dealing with cards. When I checked page source, there is a link which redirected me to the URL "http://www.pythonchallenge.com/pc/return/evil1.jpg". When I changed the ... | #http://www.pythonchallenge.com/pc/return/evil.html
import requests
from PIL import Image
#url_evl = "http://www.pythonchallenge.com/pc/return/evil2.gfx"
#un, pw = 'huge', 'file'
#d = requests.get(url_evl, auth=(un, pw)).content
#print(d)
data = open("evil2.gfx", "rb").read()
#print(data)
for i in range(0,5):
o... | _____no_output_____ | MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Python challenge question 14 For the next challenge, I tried URL "http://www.pythonchallenge.com/pc/return/disproportional.html" and it gave an image with numbers on phone and text "phone that evil". The number "5" is clickable and it took me to URL "http://www.pythonchallenge.com/pc/phonebook.php" which is XML file.O... | #http://www.pythonchallenge.com/pc/return/disproportional.html
url_evl = "http://www.pythonchallenge.com/pc/return/evil4.jpg"
un, pw = 'huge', 'file'
d = requests.get(url_evl, auth=(un, pw)).content
print(d)
import xmlrpc.client
url_pb = 'http://www.pythonchallenge.com/pc/phonebook.php'
with xmlrpc.client.ServerProx... | b'Bert is evil! go back!\n'
['phone', 'system.listMethods', 'system.methodHelp', 'system.methodSignature', 'system.multicall', 'system.getCapabilities']
Returns the phone of a person
[['string', 'string']]
555-ITALY
| MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Python challenge question 15 For the next challenge, I tried URL "http://www.pythonchallenge.com/pc/return/italy.html" and it gave an image of a roll in spiral form and other square image with vertical lines. The pagetitle is "walk around". When I checked the page source, it has a link to "http://www.pythonchallenge.c... | #http://www.pythonchallenge.com/pc/return/italy.html
#http://www.pythonchallenge.com/pc/return/uzi.html | _____no_output_____ | MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Python challenge question 16 For this challenge, I had to see the URL of the previous challenge. For the next challenge, I tried URL "http://www.pythonchallenge.com/pc/return/uzi.html" and it gave an image with calendar with year 1_6 and January 26th rounded, which is a Monday. Also, when I checked the page source, th... | #http://www.pythonchallenge.com/pc/return/uzi.html
import datetime
import calendar
for year in range(1006, 2000, 10):
if calendar.isleap(year) and datetime.date(year, 1, 26).weekday() == 0:
print(year)
#http://www.pythonchallenge.com/pc/return/mozart.html | 1176
1356
1576
1756
1976
| MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Python challenge question 17 | #http://www.pythonchallenge.com/pc/return/mozart.html
| _____no_output_____ | MIT | Homeworks/Homework1/VANGUMALLI-D-python-challenge-04-sept-2017.ipynb | DineshVangumalli/big-data-python-class |
Testing a 1D case | from scipy.interpolate import interp1d
from scipy.optimize import bisect
# 4th-order Runge-Kutta
def rk4(x, t, h, f):
# x is coordinates (as a vector)
# h is timestep
# f(x) is a function that returns the derivative
# "Slopes"
k1 = f(x, t)
k2 = f(x + k1*h/2, t + h/2)
k3 = f(x + k... | _____no_output_____ | MIT | notebooks/1D_test.ipynb | nordam/Discontinuities |
Run a quick test to verify that results don't look crazy | # Problem properties
X0 = 50
Tmax = 10
dt = 0.01
# Interpolation points
xc = np.linspace(0, 100, 1001)
# kind of interpolation
#kind = 'linear'
kind = 'quadratic'
#kind = 'cubic'
fig = plt.figure(figsize = (9, 5))
# Positive derivative
interpolator = interp1d(xc, 1.2 + np.sin(2*np.pi*xc), kind = kind)
f = lambda x... | _____no_output_____ | MIT | notebooks/1D_test.ipynb | nordam/Discontinuities |
Run convergence test | X0 = 0
Tmax = 10
# Interopolation points
xc = np.linspace(0, 100, 1001)
# kind of interpolation
kind = 'linear'
#kind = 'quadratic'
#kind = 'cubic'
# create interpolator, and wrap with lambda to get f(x, t)
interpolator = interp1d(xc, 2 + np.sin(2*np.pi*xc), kind = kind)
f = lambda x, t: interpolator(x)
# Referenc... | _____no_output_____ | MIT | notebooks/1D_test.ipynb | nordam/Discontinuities |
SSD512 Training正しく学習できたらこんな感じ[SSD300 "07+12" training summary](https://github.com/pierluigiferrari/ssd_keras/blob/master/training_summaries/ssd300_pascal_07%2B12_training_summary.md) | from tensorflow.python.keras.optimizers import Adam, SGD
from tensorflow.python.keras.callbacks import ModelCheckpoint, LearningRateScheduler, TerminateOnNaN, CSVLogger
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.models import load_model
from math import ceil
import numpy as np
from ma... | _____no_output_____ | Apache-2.0 | ssd512_training _2.ipynb | hidekazu300/ssd_512_2 |
0. Make annotation data | datasize = 10
Make_PicXML(sample_filename = 'sample/home' ,
save_pic_filename = 'DATASET/JPEGImages',
save_xml_filename = 'DATASET/Annotations',
robust = 0 ,
datasize = datasize )
Make_txt( save_file = 'DATASET', datasize = datasize , percent = 0.2 ) | _____no_output_____ | Apache-2.0 | ssd512_training _2.ipynb | hidekazu300/ssd_512_2 |
1. Set the model configuration parametersパラメーターを設定する。 2. Build or load the model初めてであれば2.1を、2回目の学習以降は2.2を実行。両方はだめ 2.1 Create a new model and load trained VGG-16 weights into it (or trained SSD weights)If you want to create a new SSD300 model, this is the relevant section for you. If you want to load a previously sav... | """
# TODO: Set the path to the `.h5` file of the model to be loaded.
model_path = 'path/to/trained/model.h5'
# We need to create an SSDLoss object in order to pass that to the model loader.
ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)
K.clear_session() # Clear previous models from memory.
model = load_model(model... | _____no_output_____ | Apache-2.0 | ssd512_training _2.ipynb | hidekazu300/ssd_512_2 |
3. Set up the data generators for the trainingThe code cells below set up the data generators for the training and validation datasets to train the model. The settings below reproduce the original SSD training on Pascal VOC 2007 `trainval` plus 2012 `trainval` and validation on Pascal VOC 2007 `test`.The only thing yo... | # 1: Instantiate two `DataGenerator` objects: One for training, one for validation.
# Optional: If you have enough memory, consider loading the images into memory for the reasons explained above.
train_dataset = DataGenerator(load_images_into_memory=False, hdf5_dataset_path=None)
val_dataset = DataGenerator(load_imag... | _____no_output_____ | Apache-2.0 | ssd512_training _2.ipynb | hidekazu300/ssd_512_2 |
4. Set the remaining training parametersWe've already chosen an optimizer and set the batch size above, now let's set the remaining training parameters. I'll set one epoch to consist of 1,000 training steps. The next code cell defines a learning rate schedule that replicates the learning rate schedule of the original ... | # Define a learning rate schedule.
def lr_schedule(epoch):
if epoch < 80:
return 0.001
elif epoch < 100:
return 0.0001
else:
return 0.00001
# Define model callbacks.
# TODO: Set the filepath under which you want to save the model.
model_checkpoint = ModelCheckpoint(filepath='traine... | _____no_output_____ | Apache-2.0 | ssd512_training _2.ipynb | hidekazu300/ssd_512_2 |
5. Train In order to reproduce the training of the "07+12" model mentioned above, at 1,000 training steps per epoch you'd have to train for 120 epochs. That is going to take really long though, so you might not want to do all 120 epochs in one go and instead train only for a few epochs at a time. You can find a summar... | # If you're resuming a previous training, set `initial_epoch` and `final_epoch` accordingly.
initial_epoch = 0
final_epoch = 120
steps_per_epoch = 1000
history = model.fit_generator(generator=train_generator,
steps_per_epoch=steps_per_epoch,
epochs=fina... | _____no_output_____ | Apache-2.0 | ssd512_training _2.ipynb | hidekazu300/ssd_512_2 |
6. Make predictionsNow let's make some predictions on the validation dataset with the trained model. For convenience we'll use the validation generator that we've already set up above. Feel free to change the batch size.You can set the `shuffle` option to `False` if you would like to check the model's progress on the ... | # 1: Set the generator for the predictions.
predict_generator = val_dataset.generate(batch_size=1,
shuffle=True,
transformations=[convert_to_3_channels,
resize],
... | _____no_output_____ | Apache-2.0 | ssd512_training _2.ipynb | hidekazu300/ssd_512_2 |
Now let's decode the raw predictions in `y_pred`.Had we created the model in 'inference' or 'inference_fast' mode, then the model's final layer would be a `DecodeDetections` layer and `y_pred` would already contain the decoded predictions, but since we created the model in 'training' mode, the model outputs raw predict... | # 4: Decode the raw predictions in `y_pred`.
y_pred_decoded = decode_detections(y_pred,
confidence_thresh=0.5,
iou_threshold=0.4,
top_k=200,
normalize_coords=normalize_coords,
... | _____no_output_____ | Apache-2.0 | ssd512_training _2.ipynb | hidekazu300/ssd_512_2 |
We made the predictions on the resized images, but we'd like to visualize the outcome on the original input images, so we'll convert the coordinates accordingly. Don't worry about that opaque `apply_inverse_transforms()` function below, in this simple case it just aplies `(* original_image_size / resized_image_size)` t... | # 5: Convert the predictions for the original image.
y_pred_decoded_inv = apply_inverse_transforms(y_pred_decoded, batch_inverse_transforms)
np.set_printoptions(precision=2, suppress=True, linewidth=90)
print("Predicted boxes:\n")
print(' class conf xmin ymin xmax ymax')
print(y_pred_decoded_inv[i]) | _____no_output_____ | Apache-2.0 | ssd512_training _2.ipynb | hidekazu300/ssd_512_2 |
Finally, let's draw the predicted boxes onto the image. Each predicted box says its confidence next to the category name. The ground truth boxes are also drawn onto the image in green for comparison. | # 5: Draw the predicted boxes onto the image
# Set the colors for the bounding boxes
colors = plt.cm.hsv(np.linspace(0, 1, n_classes+1)).tolist()
classes = ['1m','2m','3m','4m','5m','6m','7m','8m','9m','1p','2p','3p','4p','5p','6p',
'7p','8p','9p','1s','2s','3s','4s','5s','6s','7s','8s','9s',
'ea... | _____no_output_____ | Apache-2.0 | ssd512_training _2.ipynb | hidekazu300/ssd_512_2 |
Build a machine learning workflow using Step Functions and SageMaker1. [Introduction](Introduction)1. [Setup](Setup)1. [Build a machine learning workflow](Build-a-machine-learning-workflow) IntroductionThis notebook describes using the AWS Step Functions Data Science SDK to create and manage workflows. The Step Funct... | %%sh
pip -q install --upgrade stepfunctions | _____no_output_____ | Apache-2.0 | step-functions-data-science-sdk/machine_learning_workflow_abalone/machine_learning_workflow_abalone.ipynb | juliensimon/amazon-sagemaker-examples |
Setup Add a policy to your SageMaker role in IAM**If you are running this notebook on an Amazon SageMaker notebook instance**, the IAM role assumed by your notebook instance needs permission to create and run workflows in AWS Step Functions. To provide this permission to the role, do the following.1. Open the Amazon [... | import sagemaker
# SageMaker Execution Role
# You can use sagemaker.get_execution_role() if running inside sagemaker's notebook instance
sagemaker_execution_role = sagemaker.get_execution_role() #Replace with ARN if not in an AWS SageMaker notebook
# paste the StepFunctionsWorkflowExecutionRole ARN from above
workflo... | _____no_output_____ | Apache-2.0 | step-functions-data-science-sdk/machine_learning_workflow_abalone/machine_learning_workflow_abalone.ipynb | juliensimon/amazon-sagemaker-examples |
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