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def serving_input_fn():
  feature_placeholders = {
    'crim' : tf.placeholder(tf.float64, [None]),
    'zn' : tf.placeholder(tf.float64, [None]),
    'indus' : tf.placeholder(tf.float64, [None]),
    'chas' : tf.placeholder(tf.int64, [None]),
    'nox' : tf.placeholder(tf.float64, [None]),
    'rm' : tf.placeholder(tf.float64, [None]),
    'age' : tf.placeholder(tf.float64, [None]),
    'dis' : tf.placeholder(tf.float64, [None]),

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def train_and_evaluate(output_dir, num_train_steps):
  estimator = tf.estimator.LinearRegressor(
                       model_dir = output_dir,
                       feature_columns = feature_cols)
  train_spec=tf.estimator.TrainSpec(
                       input_fn = read_dataset('./train_headless.csv', mode = tf.estimator.ModeKeys.TRAIN),
                       max_steps = num_train_steps)
  exporter = tf.estimator.LatestExporter('exporter', serving_input_fn)
  eval_spec=tf.estimator.EvalSpec(
                       input_fn = read_dataset('./valid_headless.csv', mode = tf.estimator.ModeKeys.EVAL),

securetorobert

from sklearn.neighbors import NearestNeighbors

nn = NearestNeighbors(n_neighbors=5, radius=2.0)
nn.fit(users)

user = np.array([1, 4, 4, 5])
d, neighbors = nn.kneighbors(user.reshape(1, -1))

print(neighbors)

securetorobert

import pandas as pd
import numpy as np
from tensorflow import keras

ratings = pd.read_csv('./train.csv')
test_df = pd.read_csv('./test.csv')
test_df['Rating'] = 0

merged_temp = pd.concat([ratings, test_df], axis=0)
merged_temp.reindex()

securetorobert

import tensorflow as tf
from tensorflow import keras

model = keras.models.Sequential([
  keras.layers.Dense(4, activation=tf.nn.relu, input_shape=(4,)),
  keras.layers.Dense(4, activation=tf.nn.relu),
  keras.layers.Dense(1, activation=tf.nn.sigmoid)
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

securetorobert

import tensorflow as tf
from tensorflow import keras

model = keras.models.Sequential([
  keras.layers.Dense(4, activation=tf.nn.relu, input_shape=(4,), kernel_regularizer=keras.regularizers.l2(l=0.1)),
  keras.layers.Dense(4, activation=tf.nn.relu, kernel_regularizer=keras.regularizers.l2(l=0.1)),
  keras.layers.Dense(1, activation=tf.nn.sigmoid)
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

securetorobert

import tensorflow as tf
from tensorflow import keras

model = keras.models.Sequential([
  keras.layers.Dense(4, activation=tf.nn.relu, input_shape=(4,), kernel_regularizer=keras.regularizers.l1_l2(l1=0.1, l2=0.01)),
  keras.layers.Dense(4, activation=tf.nn.relu, kernel_regularizer=keras.regularizers.l1_l2(l1=0.1, l2=0.01)),
  keras.layers.Dense(1, activation=tf.nn.sigmoid)
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

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AUTOTUNE = tf.data.experimental.AUTOTUNE

path_ds = tf.data.Dataset.from_tensor_slices(train_file_list)
image_ds = path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)
label_ds = tf.data.Dataset.from_tensor_slices(tf.cast(train_label_list, tf.int64))
image_label_ds = tf.data.Dataset.zip((image_ds, label_ds))

ds = image_label_ds.shuffle(buffer_size=1000 * BATCH_SIZE)
ds = ds.repeat()
ds = ds.batch(BATCH_SIZE)

securetorobert

#standardSQL
CREATE MODEL `bqml_tutorial.sample_model`
OPTIONS(model_type='logistic_reg') AS
SELECT
  IF(totals.transactions IS NULL, 0, 1) AS label,
  IFNULL(device.operatingSystem, "") AS os,
  device.isMobile AS is_mobile,
  IFNULL(geoNetwork.country, "") AS country,
  IFNULL(totals.pageviews, 0) AS pageviews
FROM

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SELECT
  *
FROM
  ML.EVALUATE(MODEL `bqml_tutorial.sample_model`,
    (
    SELECT
      IF(totals.transactions IS NULL, 0, 1) AS label,
      IFNULL(device.operatingSystem, "") AS os,
      device.isMobile AS is_mobile,
      IFNULL(geoNetwork.country, "") AS country,