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import numpy as np | |
from random import sample | |
import tensorflow as tf | |
import tf.keras.layers as lyr | |
from tf.keras.models import Model | |
import os | |
from deep_racer_env import DeepRacerEnv | |
from gym_wrappers import ContinuesToDiscreteActionWrapper |
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def get_headers(self): | |
headers = { | |
'authority': self.host, | |
'x-requested-with': 'XMLHttpRequest', | |
'origin': 'https://{}'.format(self.host), | |
'x-csrf-token': self.x_csrf_token, | |
'user-agent': '...', | |
'content-type': 'application/json;charset=UTF-8', | |
'accept': '*/*', | |
'sec-fetch-site': 'same-origin', |
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def validate_model(): | |
""" | |
Function to validate the model results | |
""" | |
df_val = pd.read_csv('{}/processed_data/test.csv'.format(job_dir)) | |
# Submit only 10 samples to the server, ignore the first column (=target column) | |
instances = [", ".join(x) for x in df_val.iloc[:10, 1:].astype(str).values.tolist()] | |
service = discovery.build('ml', 'v1') | |
version_name = 'projects/{}/models/{}'.format(project_id, model_name) |
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def create_model(cloudml_client): | |
""" | |
Creates a Model entity in AI Platform | |
:param cloudml_client: discovery client | |
""" | |
models = cloudml_client.projects().models() | |
create_spec = {'name': model_name} | |
models.create(body=create_spec, | |
parent=project_name).execute() |
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def train_hyper_params(cloudml_client, training_inputs): | |
job_name = 'chicago_travel_time_training_{}'.format(datetime.utcnow().strftime('%Y%m%d%H%M%S')) | |
project_name = 'projects/{}'.format(project_id) | |
job_spec = {'jobId': job_name, 'trainingInput': training_inputs} | |
response = cloudml_client.projects().jobs().create(body=job_spec, | |
parent=project_name).execute() | |
print(response) |
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{ | |
"scaleTier": "CUSTOM", | |
"masterType": "standard_gpu", | |
"args": [ | |
"--preprocess", | |
"--validation_split=0.2", | |
"--model_type=regression", | |
"--hidden_units=120,60,60", | |
"--batch_size=128", | |
"--eval_frequency_secs=128", |
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WITH dataset AS( SELECT | |
EXTRACT(HOUR FROM trip_start_timestamp) trip_start_hour | |
, EXTRACT(DAYOFWEEK FROM trip_start_timestamp) trip_start_weekday | |
, EXTRACT(WEEK FROM trip_start_timestamp) trip_start_week | |
, EXTRACT(DAYOFYEAR FROM trip_start_timestamp) trip_start_yearday | |
, EXTRACT(MONTH FROM trip_start_timestamp) trip_start_month | |
, (trip_miles * 1.60934 ) / ((trip_seconds + .01) / (60 * 60)) trip_speed_kmph | |
, trip_miles | |
, pickup_latitude |
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SELECT | |
year, | |
month, | |
day, | |
cumsum, | |
ROUND(predicted_label) predicted, | |
monthly_cost actual_month_cost, | |
ROUND(100 * (ABS(predicted_label - monthly_cost) / monthly_cost),1) abs_err | |
FROM | |
ML.PREDICT(MODEL `billing_dataset_example.model_linear_regression`, |
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CREATE OR REPLACE MODEL | |
billing_dataset_example.model_linear_regression --model save path | |
OPTIONS | |
( model_type='linear_reg', -- As of Aug 2018 you can choose between linear regression and logistic regression | |
ls_init_learn_rate=.015, | |
l1_reg=0.1, | |
l2_reg=0.1, | |
data_split_method='seq', | |
data_split_col='split_col', | |
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WITH | |
cumsum_table AS( | |
SELECT | |
account_name, year, month, day, | |
ROUND(SUM(daily_cost) OVER (PARTITION BY account_name, year, month ORDER BY day )) AS cumsum, | |
ROUND(AVG(daily_cost) OVER (PARTITION BY account_name, year, month ORDER BY day )) AS mean_daily_cost | |
FROM | |
`billing_dataset_example.billing_daily_monthly` | |
ORDER BY account_name, year, month, day), | |
monthly_cost_table AS ( |
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